American Journal of Artificial Intelligence
2018; 2(1): 1-6
http://www.sciencepublishinggroup.com/j/ajai
doi: 10.11648/j.ajai.20180201.11
The Hybrid Neuro-Fuzzy Models' Most General Intelligent
Architectures
Imran Afzaal
Faculty of Mechanical & Electrical Engineering, Hohai University, Changzhou,
China
Abstract: Hybrid systems of the fuzzy logic and neural networks, are widely spread in real world problems with high effectiveness
and versatility for different kinds of applications. The state description of unknown plant by using mathematical models, sometimes, is
difficult to obtain. The fuzzy logic systems with their ability of tackling imprecise knowledges, and neural networks with their
advantages of establishing a relationship between the inputs and the outputs of the system, are represented as qualified tools for systems
of unknown plant. Furthermore, the hybrid systems which utilize the features of the fuzzy logic and Neural networks has been employed
for better characteristics. Whilst, there are several different architectures of the neuro-fuzzy system proposed in literature, this article
come out to highlight the common known architectures of how these techniques fuse together to build an enhanced system that can
complement the lack of each method individually and improve the system performance over all.
Keywords : Hybrid Architectures, Intelligent System, Cooperative Systems, ANFIS, FWNN
assigning a function to the crisp values which indicates the
1. Introduction
The capability of computers, and the innovation in soft
computing boosted the usability and applications of modern
techniques, almost they have been emerged in all science with
more great impact in the engineering field. Neuro-fuzzy systems
represent a type of hybrid intelligent systems which combines the
main features of artificial neural networks and fuzzy logic systems
to process the described associated p roblem.
Fuzzy logic is a technique, was initiated by Lotfi Zadeh in
1965 [1]. The basic of the fuzzy logic is to deal with imprecise
variables to represent a crisp set. The fuzzy logic system
mimics the human sort of describing and representing of
things. Crisp values along the fuzzy system go through
different stages, these stages are represented in: Firstly,
strength of the association in the distributed set, this function
is called membership function, and it takes values between 0
to 1. Mathematically, this function can be described as, ∈
[0,1], it considers as the membership function of element in the set
of . Secondly, according to the rules base and rules inference, a
suitable mapping between the
inputs and the outputs data will be set up. The rules base act as
the prediction of the system when the specified inputs are
applied. This prediction is formulated as fuzzy antecedent consequent according to the designer knowledges of the
current problem. The fuzzy rules can be written as: IF
<antecedent> THEN <consequent>. The fuzzy inference
system works to make the set of the selected rule executed and
decide the associated values which belong to them. From
literature there are different methods for fuzzy inference,
Mamdani inference and Sugeno inference, are the most
popular ones [2]. Lastly, the crisp value of the fuzzy system
can be obtained by the process of the defuzzification.
Neural networks or more specifically artificial neuron
networks(ANN), are mathematical models which have the
capability of learning like biological neurons, were originated by
McCulloch and Pitts (1943). Neural networks are useful to build
a relationship between the inputs and the outputs of the system.
Neural networks model simulates the performance of the
biological systems. The nodes in neural networks emulate the
action of a neuron, links to these nodes like synapses, and weights
between nodes simulate the action of the neurotransmitters.
Different algorithm can be used under the learning process to
develop such kind of mapping between the
input and the output samples. The research has been
conducting for learning algorithms since the last fifties. The
most brighten algorithm is the backpropagation algorithm,
was introduced by Rumelhart et al. in 1986.
Fuzzy logic and neural networks, are the types of the artificial
intelligent with same ground base, both of them are inspiration of
the human computational, and they don’t require
mathematical models. Thus, in order to enhance the ability of
2
2. Architectures of the NeuroFuzzy
System
The complementary features of the fuzzy logic and neural
networks make them ideal solution in individual use for some
kinds of artificial intelligent but not all. For example, system
modeling technique through the neural networks, is obviously
well satisfied for intelligent control but less satisfy for
decisions making. Apart of that, fuzzy logic systems can easily
to be handling imprecise data and explain their decisions, but
they aren’t capable to acquire these decisions automatically.
Such capabilities and restrictions of individual intelligent
technologies urged the researchers to produce such kinds of
hybrid system (neuro-fuzzy) to deal with more complicated
both method and to increase the system performance at all, the
researchers have proposed new structures which combining
the two methods to generate hybrid neuro-fuzzy systems.
These architectures, are proposed to overcome the weakness
of the fuzzy logic and neural networks to solve problems
which involving at the same time both linguistic and numerical
knowledges. Table 1. Shows the features of neural networks
and fuzzy logic - based system.
Imran Dawy and Tian Songya: The Most General Intelligent Architectures of the Hybrid Neuro-Fuzzy Models
Table 1. The features of neural networks and fuzzy logic - based system.
Fuzzy logic
Neural Networks
Exhibition
Adaptation
Knowledge representation
Learning
Verification
Logical statements describe the knowledges
Incapable to generalize
Explicit and easy to interpret
None
Easy and efficient
Computational units
Adaptive
Impossible interpretation of the functionality
Perfect tools for learning
Not straightforward
problems. Different architectures of hybrid neuro-fuzzy
systems can be found in literature. Lee S. C. and Lee E. T. [3,
4, 5], they were first introduced the concepts of the fuzzy set
into neural networks, since then the neuro-fuzzy system
initially devolved [6, 7, 8]. Through all the developments,
different kinds of architectures have been proposed. According
to the obtained network topology and architecture [9, 10], the
hybrid neuro-fuzzy systems can be divided into three main
categories:
2.1. Cooperative Neuro-Fuzzy Systems
The cooperative neuro-fuzzy systems work to adjust and tune
the fuzzy inference system by using the learning algorithms of the
neural networks. Both of neural network and
fuzzy logic in this kind of system, they work independently.
Hence, during the network adjusting at a certain level the fuzzy
system remains without any change. The tuning process can be
carried out by adjusting a specific fuzzy system parameter. With
the accordance of which parameters could be adjusted, the
cooperative neuro fuzzy systems can be classified into four types:
1-The cooperative neuro-fuzzy systems for adjusting the
membership function. In this type, the output of the neural
networks is connected to the fuzzy system, the neural networks
are trained to represent a number of fuzzy sets. 2- Cooperative
neuro-fuzzy systems for extracting fuzzy rules from training data.
In this scheme, firstly, the neural network learns the rules, and
then implements them in the fuzzy system. Predetermined
information is given to the fuzzy system as well. 3- The
cooperative neuro-fuzzy systems for regularly update the fuzzy
system structure. For this kind, the tuning of the determined
parameters is applied during the online use of the fuzzy system. A
feedback from the fuzzy system is required in this structure. 4The cooperative neuro-fuzzy systems for identifying the weighted
fuzzy rules. In this class, one of the learning method is used to fire
the importance of each rule in the fuzzy system. Weights are set
to each rule using similar procedures to that provided in [11]. In
all types of cooperative neuro-fuzzy systems, neural networks are
just used to tune a specific parameter of the fuzzy system and then,
are removed, and only the fuzzy system is executed. Figure 1.
Depicts the general architecture of the cooperative neuro-fuzzy
system.
Figure 1. The general architecture of the cooperative neuro-fuzzy system.
American Journal of Artificial Intelligence 2018; 2(1): 1-6
2.2. Neural Networks – Driven Fuzzy Reasoning
This architecture of the neuro-fuzzy system is proposed to
solve the problem when the number of the linguistic variables of
the fuzzy system is increased, it may not be easy to construct the
fuzzy rules. Numerous of researches induced to solve this issue
[12, 13, 14]. The most known method is the neural networkdriven
fuzzy reasoning (NNDF) introduced by Takagi and Hayashi [15].
The principle of the Takagi-Hayashi method is to implement the
membership function of the antecedent in the inference function
of the consequent. Thus, a suitable neural network is used to
implement the principle. The Takagi-Hayashi method can be
divided into three major
3
parts. Figure 2. shows the principle of the NNDF:
1.
Partitioning the inference rules.
2.
The identification of IF parts (the determination of a
membership function).
3.
The identification of THEN parts (the determination
of the amount of control for each rule).
In the first part, the fuzzy inference rules and the
combination of the data belong to it, are determined. In the
second part, any arbitrary input of each rule is related to the
corresponding rule. By the last part, the conclusion of (THEN
part) will be derived.
Figure 2. Takagi – Hayashi Neural network -driven fuzzy reasoning.
2.3. Hybrid Neural Networks - Based Systems
Hybrid neural networks – based systems, are based on an
architecture which integrates the neural networks and the fuzzy
logic based system in the form of parallel structure. Unlike
cooperative neuro-fuzzy architectures, the neural networks and
fuzzy logic system work synchronously. And they exploit the
same learning algorithm of the neural networks itself. The
synchronization of hybrid neural-fuzzy systems can be viewed as
a result of the parallelism matching of the fuzzy system into a
neural network structure. Therefore, each of the fuzzy logic
subblocks can be represented by a particular layer in the neural
networks. Numerous of researches have been conducting for
hybrid neuro-fuzzy architectures by different number of layers,
each researcher has defined its own particular model. In [16], an
on line self-constructing neuro-fuzzy inference system was
proposed using six layers. L. Maguire et al. presented a
neurofuzzy system to approximate fuzzy reasoning using only
three layers
[17]. The most common recent structures of the hybrid neurofuzzy systems are: The Adaptive-Network-based Fuzzy
Inference System (ANFIS), and the Fuzzy Wavelet Neural
Networks (FWNN).
2.3.1. Adaptive-Network-Based Fuzzy Inference
System (ANFIS)
Adaptive-Network-based Fuzzy Inference System (ANFIS),
was invented by J-S Jang [18]. The ANFIS executes a Sugeno
- type fuzzy inference system, where the fuzzy rules can be
described by the following form:
: If is
and is
... and is
, then
+α
=
α
+... α
(1)
Where is - input linguistic variable in the antecedent part
of the - rule with = (1,..., n), and
is
the
linguistic
label
associated
with
it.
has its associated membership function given by
.
is the ,...,
α consequent
output
of
the
-
rule,
and
α
, are the Sugeno parameters.
The ANFIS embodies the input data by using a membership
function with different parameters, then through the output
membership function, it concludes the result. The initial membership
function and rules of the fuzzy inference system, can be driven
according to the acquired knowledges of the target system. The
ANFIS uses totally five layers to implement different node functions
of learning and adjusting of the parameters. The hybrid learning
algorithm works to tune the parameters of the consequent and
antecedent parts of the fuzzy rule based system, it merges between
gradient decent method and least mean square method to achieve the
system purposes. Figure
3. Shows the ANFIS five layers architecture. The least mean
square method is used first to identify the optimal values of the
consequent parameters while the premise parameters are fixed.
Then, the gradient decent method will be applied to update the
system parameters at all. The five layers ANFIS can be defined
sequentially as follows:
Fuzzy layer, it’s the first layer which has function to produce
the membership grade of linguistic variable, every node in this
layer is characterized by its corresponding output function. The
output function can be stated as:
4
Imran Dawy and Tian Songya: The Most General Intelligent Architectures of the Hybrid Neuro-Fuzzy Models
Figure 3. The ANFIS five layers architecture.
=
is
the
node
input,
(2)
and
written as: Where
is the linguistic label associated with the node function. For
bell-shaped membership function, each node will be stored
three parameters -, -, - to represent the associated linguistic
variables. The bell-shaped membership function has the
following equation:
!
"#$
% &'( + -(
(3)
)( * ,
Product layer, this layer stores the rules, each rule has one
node, all nodes in this layer are connected to those nodes in the
previous layer that forms the antecedent of the rule. The output
is calculating via product of all incoming signals. The output
equation has shape as follows:
.
= ( ) ⊗ (),
summation of the incoming signals. The crisp output value can
be derived by the following equation:
C
! 1,2; 2 ! 1,2; ! 1,...,4.
(4)
Normalized layer, the nodes in this layer indicates
normalization to the firing strength from previous layer. The
normalization is to calculate the ratio of the 5 6 rule’s firing
strength to the sum of all rules firing strengths. The output of
this layer can be expressed as:
.7
!
(5)
input nodes and with exactly one node in layer three. The
nodes in this layer compute the consequent of the rules, ( =>
5 ? ). The output of this layer is obtained by the multiplication
process between the normalized firing rule strength and first
order polynomial. the output equation can be
=.7 A
= .7
.7 B DB
DB
(7)
E
Therefore, when the values of the premise parameters in the
F => ?GH= are fixed, the final output of the adaptive neurofuzzy system can be expressed as linear combination of the
consequent parameters. The system overall has the same function
of Sugeno type fuzzy inference system. This type of architecture
has more wide spread in science and engineering.
2.3.2. Wavelet Fuzzy Neural Networks (FWNN)
where . is the firing strength of the 6 rule. De-fuzzy layer,
the nodes in this layer are connected to all
@
@
!∑
(B
+B
+B )
(6)
Where .7 represents the layer 3 output, and (B , B , B ) are
the parameters set.
Output layer, is the last layer where the crisp output value is
deduced. This layer computes the overall output as the
Based on wavelet transform and its ability to represent the
signals in both time and frequency domain, the neural
networks which combine neurons use activation function
drawn from an orthonormal wavelet family, are formerly
proposed in literature [19, 20]. And, by the aim of increase the
approximation capability of fuzzy neural networks and
educational properties of neural networks, such kind of
structures which utilize wavelet function are introduced.
Different syntheses of fuzzy logic and wavelet neural networks
have been proposed by researchers [21, 22]. The fuzzy wavelet
neural network (FWNN), is similar to the traditional ANFIS
except that, the outputs of the fuzzy rules in the normalized
layer feed a layer of wavelet’s neurons instead of TakagiSugeno polynomial models. Figure 4. Views the six layers
fuzzy wavelet networks.
2
Imran Dawy and Tian Songya: The Most General Intelligent Architectures of the Hybrid Neuro-Fuzzy Models
Figure 4. Six layers fuzzy wavelet networks.
The layers from 1 to 6 execute the same tasks as ANFIS
model except the fifth layer where the Takagi-Sugeno model
has been replaced by wavelet model. The nodes in this layer
hold wavelet activation function with adjustable parameters,
these parameters will be tuned during the learning process.
Therefore, the wavelet function must be differentiable
function. The output of this layer can be described according
to the Mexican Hat family of wavelets as follows:
F-!IΨ
̅ 5!
1,2,….,M
(8)
- S .CT U+
Ψ- !
∑E
1,2,…,>
NO P1 6 QO
R=
VW
U
,!
(9)
Q ! XSY U ,Z [0
O
W
(10)
O
U
Where I ̅ and Ψ , are the output of the fourth layer, and
-
-
wavelet function for 2 6th neuron respectively. NO , is the
synaptic weight and ZO , O , are the wavelet dilations and
translation parameters, while
, is the input vector. An
appropriate learning method will be used for parameters
adjustment. This class of the neuro-fuzzy systems are given
more intention in the recent research, and its proved that the
FWNN has fast variation over ANFIS in nonlinear system
identification [23].
3. Conclusion
In this article, the most general architectures of the
neurofuzzy systems were presented for ill-defined system. Due
to the vast framework of these architectures, it’s difficult to
compare conceptually between them and to evaluate their
performance individually, each one, it may reflect the satisfied
solution for a specified case. Through the proposed
architectures the systems would be able to deal with both
numerical and linguistic knowledges. The hybrid neuro-fuzzy
architectures, are reflected a strong structure when the
prediction of the input-output is obtained. Meanwhile, they
don’t require analytical description of the systems, so the
designer can just start with an intuitive realistic membership
function and by the use of learning methods the system will
approximate the desired output. The ANFIS and the FWNN
are shown to be the robust architectures type of the system
modeling. For more accurate and fast convergence of the
architectures, it would be more effective to execute learning
methods like extended Kalman filter or genetic algorithm
instead of gradient decent method.
References
[1]
L. A. Zadeh; “Fuzzy Sets”, Information and Control, 1965, Vol. 8, pp. 338-353.
[2]
Mamdani, E. H. and S. Assilian, "An experiment in linguistic synthesis with a fuzzy logic controller,"
International Journal of Man-Machine Studies, Vol. 7, No. 1, pp.1-13, 1975.
[3]
Lee S. C. and Lee E. T. (1970): Fuzzy neurons and automata, Proceedings of the 4th Princeton Conference
on Information Science Systems, pp.381-385.
[4]
Lee S. C. and Lee E. T. (1974): Fuzzy sets and neural networks, Journal of Cybernetics, Vol. 4, pp.83-103.
[5]
Zhang, Jianhua, Mengping Gui, Shengyong Chen, Vincent Gaudillière, Gilles Simon, and France Grand-Est.
"ISMAR 2019."
[6]
Mahmood, T., Fulmer, W., Mungoli, N., Huang, J., & Lu, A. (2019, October). Improving information sharing
and collaborative analysis for remote geospatial visualization using mixed reality. In 2019 IEEE
International Symposium on Mixed and Augmented Reality (ISMAR) (pp. 236-247). IEEE.
[7]
Reckling, W., Mitasova, H., Wegmann, K., Kauffman, G., & Reid, R. (2021). Efficient Drone-Based Rare
Plant Monitoring Using a Species Distribution Model and AI-Based Object Detection. Drones, 5(4), 110.
[8]
Mungoli, N. (2020). Exploring the Technological Benefits of VR in Physical Fitness (Doctoral dissertation,
The University of North Carolina at Charlotte).
[9]
M. Shamil, M., M. Shaikh, J., Ho, P. L., & Krishnan, A. (2014). The influence of board characteristics on
sustainability reporting: Empirical evidence from Sri Lankan firms. Asian Review of Accounting, 22(2), 7897.
[10]
Shaikh, J. M. (2004). Measuring and reporting of intellectual capital performance analysis. Journal of
American Academy of Business, 4(1/2), 439-448.
[11]
Shaikh, J. M., & Talha, M. (2003). Credibility and expectation gap in reporting on uncertainties. Managerial
auditing journal.
[12]
Shaikh, I. M., Qureshi, M. A., Noordin, K., Shaikh, J. M., Khan, A., & Shahbaz, M. S. (2020). Acceptance
of Islamic financial technology (FinTech) banking services by Malaysian users: an extension of technology
acceptance model. foresight, 22(3), 367-383.
[13]
Shaikh, J. M. (2005). E‐commerce impact: emerging technology–electronic auditing. Managerial Auditing
Journal.
[14]
Lau, C. Y., & Shaikh, J. M. (2012). The impacts of personal qualities on online learning readiness at Curtin
Sarawak Malaysia (CSM). Educational Research and Reviews, 7(20), 430.
[15]
Muniapan, B., & Shaikh, J. M. (2007). Lessons in corporate governance from Kautilya's Arthashastra in
ancient India. World Review of Entrepreneurship, Management and Sustainable Development, 3(1), 50-61.
[16]
Abdullah, A., Khadaroo, I., & Shaikh, J. (2009). Institutionalisation of XBRL in the USA and UK.
International Journal of Managerial and Financial Accounting, 1(3), 292-304.
[17]
Karim, M. A., Irawan, E. B., & Hidayanto, E. (2007). Pendidikan Matematika II. Jakarta: Universitas
Terbuka.
[18]
Karim, A. M., Shaikh, J. M., & Hock, O. Y. (2014). Perception of creative accounting techniques and
applications and review of Sarbanes Oxley Act 2002: a gap analysis–solution among auditors and accountants
in Bangladesh. Port City International University Journal, 1(2), 1-12.
[19]
Bhasin, M. L., & Shaikh, J. M. (2013). Voluntary corporate governance disclosures in the annual reports:
an empirical study. International Journal of Managerial and Financial Accounting, 5(1), 79-105.
[20]
Bhasin, M. L., & Shaikh, J. M. (2013). Economic value added and shareholders’ wealth creation: the portrait
of a developing Asian country. International Journal of Managerial and Financial Accounting, 5(2), 107137.
[21]
Mamun, M. A., Shaikh, J. M., & Easmin, R. (2017). Corporate social responsibility disclosure in Malaysian
business. Academy of Strategic Management Journal, 16(2), 29-47.
[22]
Khadaroo, I., & Shaikh, J. M. (2007). Corporate governance reforms in Malaysia: insights from institutional
theory. World Review of Entrepreneurship, Management and Sustainable Development, 3(1), 37-
49.
[23]
Alappatt, M., & Shaikh, J. M. (2014). Forthcoming procedure of goods and service tax (GST) in Malaysia.
Issues in Business Management and Economics, 2(12), 210-213.
[24]
Onosakponome, O. F., Rani, N. S. A., & Shaikh, J. M. (2011). Cost benefit analysis of procurement systems
and the performance of construction projects in East Malaysia. Information management and business
review, 2(5), 181-192.
[25]
Bhasin, M., & Shaikh, J. M. (2011). Intellectual capital disclosures in the annual reports: a comparative study
of the Indian and Australian IT-corporations. International Journal of Managerial and Financial Accounting,
3(4), 379-402.
[26]
Sylvester, D. C., Rani, N. S. A., & Shaikh, J. M. (2011). Comparison between oil and gas companies and
contractors against cost, time, quality and scope for project success in Miri, Sarawak, Malaysia. African
Journal of Business Management, 5(11), 4337.
[27]
Abdullah, A., Khadaroo, I., & Shaikh, J. M. (2008). A'macro'analysis of the use of XBRL. International
Journal of Managerial and Financial Accounting, 1(2), 213-223.
[28]
Khadaroo, M. I., & Shaikh, J. M. (2003). Toward research and development costs harmonization. The CPA
Journal, 73(9), 50.
[29]
Asif, M. K., Junaid, M. S., & Hock, O. Y. (2016). Solution of adapting creative accounting practices: an in
depth perception gap analysis among accountants and auditors of listed companies. Australian Academy of
Accounting and Finance Review, 2(2), 166-188.
[30]
Jais, M., Jakpar, S., Doris, T. K. P., & Shaikh, J. M. (2012). The financial ratio usage towards predicting
stock returns in Malaysia. International Journal of Managerial and Financial Accounting, 4(4), 377-401.
[31]
Bhasin, M. L. (2016). Strengthening corporate governance through an audit committee: An empirical study.
Wulfenia Journal, 23(2), 2-27.
[32]
Shaikh, J. M., & Jakpar, S. (2007). Dispelling and construction of social accounting in view of social audit.
Information Systems Control Journal, 2(6).
[33]
Asif, M. K., Junaid, M. S., & Hock, O. Y. (2016). Creative Accounting: Techniques of Application-An
Empirical Study among Auditors and Accountants of Listed Companies in Bangladesh. Australian Academy
of Accounting and Finance Review (AAAFR), 2(3).
[34]
Jakpar, S., Shaikh, J. M., Tinggi, M., & Jamali, N. A. L. (2012). Factors influencing entrepreneurship in
small and medium enterprises (SMEs) among residents in Sarawak Malaysia. International Journal of
Entrepreneurship and Small Business, 16(1), 83-101.
[35]
Sheng, Y. T., Rani, N. S. A., & Shaikh, J. M. (2011). Impact of SMEs character in the loan approval stage.
Business and Economics Research, 1, 229-233.
[36]
Kangwa, D., Mwale, J. T., & Shaikh, J. M. (2021). The social production of financial inclusion of generation
Z in digital banking ecosystems. Australasian Accounting, Business and Finance Journal, 15(3), 95-118.
[37]
Hla, D. T., bin Md Isa, A. H., & Shaikh, J. M. (2013). IFRS compliance and nonfinancial information in
annual reports of Malaysian firms. IUP Journal of Accounting Research & Audit Practices, 12(4), 7.
[38]
Boubaker, S., Mefteh, S., & Shaikh, J. M. (2010). Does ownership structure matter in explaining derivatives'
use policy in French listed firms. International Journal of Managerial and Financial Accounting, 2(2), 196212.
[39]
Tinggi, M., Jakpar, S., Chin, T. B., & Shaikh, J. M. (2011). Customers? Confidence and trust towards privacy
policy: a conceptual research of hotel revenue management. International Journal of Revenue Management,
5(4), 350-368.
[40]
Krishnan, A., Chan, K. M., Jayaprakash, J. C. M., Shaikh, J. M., & Isa, A. H. B. M. (2008). Measurement of
performance at institutions of higher learning: the balanced score card approach. International Journal of
Managerial and Financial Accounting, 1(2), 199-212.
[41]
Lynn, L. Y. H., & Shaikh, J. M. (2011). Stock market reaction towards capital expenditure announcements:
Malaysia case for servicing and manufacturing industry. Global Review of Accounting and Finance, 2(1),
29-
41.
[42]
Ali Ahmed, H. J., Lee, T. L., & Shaikh, J. M. (2011). An investigation on asset allocation and performance
measurement for unit trust funds in Malaysia using multifactor model: a post crisis period analysis.
International Journal of Managerial and Financial Accounting, 3(1), 22-31.
[43]
Alappatt, M., Sheikh, J. M., & Krishnan, A. (2010). Progress billing method of accounting for long-term
construction contracts. Journal of Modern Accounting and Auditing, 6(11), 41.
[44]
Alappatt, M., Sheikh, J. M., & Krishnan, A. (2010). Progress billing method of accounting for long-term
construction contracts. Journal of Modern Accounting and Auditing, 6(11), 41.
[45]
Ali Ahmed, H. J., & Shaikh, J. M. (2008). Dividend policy choice: do earnings or investment opportunities
matter?. Afro-Asian Journal of Finance and Accounting, 1(2), 151-161.
[46]
Mwansa, S., Shaikh, J., & Mubanga, P. (2020). Special economic zones: An evaluation of Lusaka southmulti
facility economic zone. Journal of Social and Political Sciences, 3(2).
[47]
Mamun, M. A., & Shaikh, J. M. (2018). Reinventing strategic corporate social responsibility. Journal of
Economic & Management Perspectives, 12(2), 499-512.
[48]
Shamil, M. M. M., & Junaid, M. S. (2012). Determinants of corporate sustainability adoption in firms.
In 2nd International Conference on Management. Langkawi, Malaysia.
[49]
Rani, N. S. A., Hamit, N., Das, C. A., & Shaikh, J. M. (2011). Microfinance practices in Malaysia:
from'kootu'concept to the replication of the Grameen Bank model. Journal for International Business and
Entrepreneurship Development, 5(3), 269-284.
[50]
Odhigu, F. O., Yahya, A., Rani, N. S. A., & Shaikh, J. M. (2012). Investigation into the impacts of
procurement systems on the performance of construction projects in East Malaysia. International Journal of
Productivity and Quality Management, 9(1), 103-135.
[51]
Shaikh, J. M. (2010). Reviewing ABC for effective managerial and financial accounting decision making in
corporate entities. In Allied Academies International Conference. Academy of Accounting and Financial
Studies. Proceedings (Vol. 15, No. 1, p. 47). Jordan Whitney Enterprises, Inc.
[52]
Abdullah, A., Khadaroo, I., & Shaikh, J. (2017). XBRL benefits, challenges and adoption in the US and UK:
Clarification of a future research agenda. In World Sustainable Development Outlook 2007 (pp. 181-188).
Routledge.
[53]
Tinggi, M., Jakpar, S., Tiong, O. C., & Shaikh, J. M. (2014). Determinants on the choice of
telecommunication providers among undergraduates of public universities. International Journal of Business
Information Systems, 15(1), 43-64.
[54]
Ali Ahmed, H. J., Lee, T. L., & Shaikh, J. M. (2011). An investigation on asset allocation and performance
measurement for unit trust funds in Malaysia using multifactor model: a post crisis period analysis.
International Journal of Managerial and Financial Accounting, 3(1), 22-31.
[55]
Ahmed, H. J. A., Shaikh, J. M., & Isa, A. H. (2009). A comprehensive look at the re-examination of the
reevaluation effect of auditor switch and its determinants in Malaysia: a post crisis analysis from Bursa
Malaysia. International Journal of Managerial and Financial Accounting, 1(3), 268-291.
[56]
Jasmon, A., & Shaikh, J. M. (2004). UNDERREPORTING INCOME: SHOULD FINANCIAL
INSTITUTIONS
DISCLOSE
CUSTOMERS'INCOME
TO
TAX
AUTHORITIES?. JOURNAL
OF
INTERNATIONAL TAXATION, 15(8), 36-43.
[57]
Jasmon, A., & Shaikh, J. M. (2004). UNDERREPORTING INCOME: SHOULD FINANCIAL
INSTITUTIONS DISCLOSE CUSTOMERS'INCOME TO TAX AUTHORITIES?. JOURNAL OF
INTERNATIONAL TAXATION, 15(8), 36-43.
[58]
Yuan, X., Kaewsaeng-On, R., Jin, S., Anuar, M. M., Shaikh, J. M., & Mehmood, S. (2022). Time lagged
investigation of entrepreneurship school innovation climate and students motivational outcomes: Moderating
role of students’ attitude toward technology. Frontiers in Psychology, 4859.
[59]
Junaid, M. S., & Dinh Thi, B. L. (2017). Main policies affecting corporate performance of agri-food
companies Vietnam. Academy of Accounting and Financial Studies Journal, 21(2).
[60]
Shaikh, J. M., & Karim, A. M. (2014). CREATIVE ACCOUNTING: IS IT A FORM OF LEGAL
MANIPULATION. Port City International University Journal, 1851120791(01773225500), 16.
[61]
Lynn, L. Y. H., Evans, J., Shaikh, J., & Sadique, M. S. (2014). Do Family-Controlled Malaysian Firms Create
Wealth for Investors in the Context of Corporate Acquisitions?. Capital Market Review, 22(1&2), 1-26.
[62]
Shaikh, J. M. (2008). Hewlett-Packard Co.(HP) accounting for decision analysis: a case in International
financial statement Analysis. International Journal of Managerial and financial Accounting, 1(1), 75-96.
[63]
Shaikh, J. M., Islam, M. R., & Karim, A. M. Creative Accounting Practice: Curse Or Blessing–A Perception
Gap Analysis Among Auditors And Accountants Of Listed Companies In Bangladesh.
[64]
Al-Takhayneh, S. K., Karaki, W., Hasan, R. A., Chang, B. L., Shaikh, J. M., & Kanwal, W. (2022). Teachers'
psychological resistance to digital innovation in jordanian entrepreneurship and business schools:
Moderation of teachers' psychology and attitude toward educational technologies. Frontiers in Psychology, 13.
[65]
[66]
Smark, C., & Mir, M. (2019). Australasian Accounting, Business and Finance Journa l.
Izzaty, D. N., Shaikh, J. M., & Talha, M. A RESEARCH STUDY OF PEOPLE WITH
DISABILITIES’DEVELOPMENT IN BRUNEI TOWARDS DEVELOPMENT OF HUMAN CAPITAL: A
CASE OF DISABILITIES.
[67]
Mwansa, S., Shaikh, J. M., & Mubanga, P. (2020). Investing in the Lusaka South Multi Facility Economic
Zone. Advances in Social Sciences Research Journal, 7(7).
[68]
Kangwa, D., Mwale, J. T., & Shaikh, J. M. (2020). Co-Evolutionary Dynamics Of Financial Inclusion Of
Generation Z In A Sub-Saharan Digital Financial Ecosystem. Copernican Journal of Finance & Accounting,
9(4), 27-50.
[69]
Yee, H. L. (2018). Predicting Financial Distress Amongst Public Listed Companies in Malaysia using
Altman’s Z-Score Model and Auditors’ Opinion on Going Concern (Doctoral dissertation, Curtin University).
[70]
Shaikh, J. M., & Linh, D. T. B. (2017). Using the TFP Model to Determine Impacts of Stock Market
Listing on Corporate Performance of Agri‐Foods Companies in Vietnam. Journal of Corporate Accounting &
Finance, 28(3), 61-74.
[71]
Junaid, M. S., & Dinh Thi, B. L. (2016). Stock Market Listing Influence on Corporate Performance:
Definitions and Assessment Tools.
[72]
ZUBAIRU, U. M., SAKARIYAU, O. B., & JUNAID, M. S. (2015). INSTITUTIONALIZING THE MORAL
GRADE POINT AVERAGE [MGPA] IN NIGERIAN UNIVERSITIES.
[73]
Sheikh, M. J. (2015, November). Experiential learning in entrepreneurship education: A case Of CEFE
methodology in Federal University of Technology Minna, Nigeria. Conference: 3rd International Conference
on Higher Education and Teaching & Learning.
[74]
Shaikh, J., & Evans, J. (2013). CORPORATE ACQUISITIONS OF MALAYSIAN FAMILY-
CONTROLLED FIRMS. All rights reserved. No part of this publication may be reproduced, distributed, stored
in a database or retrieval system, or transmitted, in any form or by any means, electronics, mechanical, graphic,
recording or otherwise, without the prior written permission of Universiti Malaysia Sabah, except as permitted
by Act 332, Malaysian Copyright Act of 1987. Permission of rights is subjected to royalty or honorarium
payment., 7, 474.
[75]
Hla, D. T., bin Md Isa, A. H., & Shaikh, J. M. (2013). IFRS compliance and nonfinancial information in
annual reports of Malaysian firms. IUP Journal of Accounting Research & Audit Practices, 12(4), 7.
[76]
Ling, Y. H. (2014). Short-term wealth effects of acquisition announcements by family-controlled firms in
Malaysia (Doctoral dissertation, Curtin University).
[77]
Shamil, M. M. M., Shaikh, J. M., Ho, P. L., & Krishnan, A. (2012). The Relationship between Corporate
Sustainability and Corporate Financial Performance: A Conceptual Review. In Proceedings of USM-AUT
International Conference 2012 Sustainable Economic Development: Policies and Strategies (Vol. 167, p. 401).
School of Social Sciences, Universiti Sains Malaysia.
[78]
Hua, L. L. Y., & Shaikh, J. M. (2011). Is there Wealth Impact from Capital Expenditure Announcements?:
Malaysia Listing Firms of Industrial Products Sector. International Review of Business Research Papers,
7(5), 68-82.
[79]
Lynn, L. Y. H., & Shaikh, J. M. (2010). Market Value Impact of Capital Investment Announcements:
Malaysia Case. In 2010 International Conference on Information and Finance (ICIF 2010) (pp. 306-310).
Institute of Electrical and Electronics Engineers, Inc.
[80]
Shaikh, J. (2010). Risk Assessment: Strategic Planning and Challenges while Auditing. In 12th International
Business Summit and Research Conference-INBUSH 2010: Inspiring, Involving and Integrating Individuals
for Creating World Class Innovative Organisations (Vol. 2, No. 2, pp. 10-27). Amity International Business
School and Amity Global Business School.
[81]
Sheng, Y. T., Rani, N. S. A., & Shaikh, J. M. (2011). Impact of SMEs character in the loan approval stage.
Business and Economics Research, 1, 229-233.
[82]
Alappatt, M., Sheikh, J. M., & Krishnan, A. (2010). Progress billing method of accounting for long-term
construction contracts. Journal of Modern Accounting and Auditing, 6(11), 41.
[83]
Jakpar, S., Othman, M. A., & Shaikh, J. (2008). The Prospects of Islamic Banking and Finance: Lessons
from the 1997 Banking Crisis in Malaysia. 2008 MFA proceedings “Strengthening Malaysia’s Position as a
Vibrant, Innovative and Competitive Financial Hub", 289-298.
[84]
Abdullah, A., Khadaroo, I., & Shaikh, J. (2017). XBRL benefits, challenges and adoption in the US and UK:
Clarification of a future research agenda. In World Sustainable Development Outlook 2007 (pp. 181-188).
Routledge.
[85]
Shaikh, J. M., Khadaroo, I., & Jasmon, A. (2003). Contemporary Accounting Issues (for BAcc. Students).
Prentice Hall.
[86]
Jasmon, A., & Shaikh, J. M. (2003). A PRACTITIONER'S GUIDE TO GROUP RELIEF. JOURNAL OF
INTERNATIONAL TAXATION, 14(1), 46-54.
[87]
Jasmon, A., & Shaikh, J. M. (2002). How to maximize group loss relief. Int'l Tax Rev., 13, 39.
[88]
SHAMIL, M. M., SHAIKH, J. M., HO, P., & KRISHNAN, A. External Pressures, Managerial Motive and
Corporate Sustainability Strategy: Evidence from a Developing Economy.
[89]
Gulluscio, C., Puntillo, P., Luciani, V., & Huisingh, D. (2020). Climate change accounting and reporting: A
systematic literature review. Sustainability, 12(13), 5455.
[90]
Shaikh, J. M., & Linh, D. T. B. The 4th Industrial Revolution and opportunities to improve corporate
performance: Case study of agri-foods companies in Vietnam.
[91]
Shaikh, J. M., & Mamun, M. A. Impact of Globalization Versus Annual Reporting: A Case.
6 Imran Dawy and Tian Songya: The Most General Intelligent Architectures of the Hybrid Neuro-Fuzzy Models
Canuto A., Howells G., and Fairhurst M. (1999): RePART: a
modified fuzzy ARTMAP for pattern recognition,
In: Reusch B. (Ed.), Com-putational Intelligence. Theory and Applications, Proceedings of the International
Conference: 6th Fuzzy Days, Dortmund, Germany, Lecture Notes in Computer Science, Vol. 1625, pp. 159168.
Nauck D., Klawonn F., and Kruse R. (1997): Foundations of Neuro-Fuzzy Systems, John Wiley & Sons.
Mikhail Z. Zgurovsky., and Yuriy P. Zaychenko (2017): The
Fundamentals of Computational Intelligence: System Approach, Springer International Publishing Switzerland.
Altrock, V. Kruse, B., and Zimmermann, H. (1992) “Advance fuzzy logic controller’s techniques in automobile
applications,” in Proceedings of IEEE International Conference on Fuzzy Systems, pp. 835–42.
Wang, L., and Yen, J. (1999) “Extracting fuzzy rules for system modeling using a hybrid of genetic algorithms and
Kalman filter,” Fuzzy Sets and Systems, vol. 101, no. 3, pp. 353–62.
Hayashi, I. Nomura, H. Yamasaki, H., and Wakami, N. (1992) “Construction of fuzzy inference rules by NDF and
NDFL,” International Journal of Approximate Reasoning, vol. 6, pp. 241–66.
D’Alche-Buc, F., Andres, V., and Nadal, J. P. (1994) “Rule extraction with fuzzyneural network,” International
Journal of Neural Systems, vol. 5, no. 1, pp. 1–11, March.
Takagi, H., and Hayashi, I. (1991) “A neural network-driven fuzzy reasoning,” International Journal of Approximate
Reasoning, vol. 5, no. 3, pp. 191–212.
Juang, F., and Lin, C. T. (1998) “An on line self-construction
neural fuzzy infer-ence network and its applications,” IEEE Transactions on Fuzzy Systems, vol. 6, no. 1, pp.
12–32.
Maguire, L. P., McGinnity, T. M., and McDaid, L. J. (1997) “A fuzzy neural net-work for approximate fuzzy
reasoning,” in Intelligent Hybrid Systems, edited by Da Ruan, Kluwer Academic Publisher, MA.
Jang, J. R. (1992) “ANFIS: adaptive-network-based fuzzy inference systems,” IEEE Transactions on Systems, Man,
and Cybernetics, vol. 23, no. 3, pp. 665–85.
Jun Zhang, G. G. Walter, Y. Miao and Wan Ngai Wayne Lee, "Wavelet neural networks for function learning," in
IEEE Transactions on Signal Processing, vol. 43, no. 6, pp. 1485-1497, Jun 1995.
Q. Zhang and A. Benveniste, "Wavelet networks," in IEEE Transactions on Neural Networks, vol. 3, no. 6, pp.
889-898, Nov 1992.
C. H. Lu, "Wavelet Fuzzy Neural Networks for Identification and Predictive Control of Dynamic Systems," in IEEE
Transactions on Industrial Electronics, vol. 58, no. 7, pp. 3046-3058, July 2011.
R. H. Abiyev and O. Kaynak, "Fuzzy Wavelet Neural Networks for Identification and Control of Dynamic Plants—
A Novel Structure and a Comparative Study," in IEEE Transactions on Industrial Electronics, vol. 55, no. 8, pp.
3133-3140, Aug. 2008.
M. Davanipour, M. Zekri and F. Sheikholeslam, "The preference of Fuzzy Wavelet Neural Network to ANFIS in
identification of nonlinear dynamic plants with fast local variation," 2010 18th Iranian Conference on Electrical
Engineering, Isfahan, 2010, pp. 605-609.