National Taiwan Ocean University
Department of Communications, Navigation and
Control Engineering
Speaker：游佳龍
ID：19967034
Date：11/24/2010
Outline
 Abstract
 Introduction
 The extended BAM Neural Network Model
 Proof of the New Model’s Stability
 Experiment Results
Abstract
 In this paper we propose an extended bidirectional associative
memory (BAM) neural network model which can do auto- and
hetero-associative memory. The theoretical proof for this neural
network model’s stability is given. Experiments show that this
neural network model is much more powerful than the M-P
Model, Discrete Hopfield Neural Network, Continuous
Hopfield Neural Network, Discrete Bidirectional Associative
Memory Neural Network, Continuous and Adaptive
Bidirectional Associative Memory Neural Network, BackPropagation Neural Network and Optimal Designed Nonlinear
Continuous Neural Network. Experimental results also show
that, when it does auto-associative memory, the power of this
model is the same as the Loop Neural Network Model which
can only do auto-associative memory.
Introduction
 Associative memory is an important part in neural
network theory and it is also an efficient function in
the applications of intelligent control, pattern
recognition and artificial intelligence.
 At present, many neural network models such as Loop
model, M-P model, Discrete and Continuous Hopfield
Model, Kosko’s Discrete BAM, Optimal Designed
Nonlinear Continuous Neural Network, etc., which
can do associative memory, have existed. Each model
has its own advantages and disadvantages.
Introduction
 In practical applications, the more powerful the
network is, the better the associative memory result
are. One important task is to find or construct a
powerful associative neural network. The so-called
neural network model has two meanings, that is its
structure and its training algorithm.
 In this paper we propose an extended bidirectional
associative memory(BAM) neural network model.
The reason why we call this new model an extended
BAM neural network model is that its structure is the
same as the BAM model. The different between the
BAM and the extended BAM is the training algorithm.
The extended BAM
Neural Network Model
 This part introduces the architecture and learning
algorithm for the Extended. This model can be used to
carry out both auto-associative memory and heteroassociative memory. The BAM model(Kosk0 Model)
is a memory consisting of two layers. It uses the
forward and backward information flow to produce an
associative search for stored stimulus-response
association.
The extended BAM
Neural Network Model
The extended BAM
Neural Network Model
 The firing function for both 1ayers:neuron is
 Consider the stored association pairs as
 The formula for the weight matrix is
 For our extended BAM model, the learning algorithm
is Delta Learning Rule.
Delta learning rule
r  [ d j f (W jT X )] f ' (W jT X )
1
T
2
E   ( d j  f (W j X ))
2 j
E
T
E 
 ( d j  y j ) f ' (W j X ) X
W j
w ji   E   ( d j  y j ) f ' ( net j ) xi
W j  r[W j (t ), X (t ), d j (t )]X (t )
The extended BAM
Neural Network Model
 During training we treat this two layer network as a
feedforward neural network, and the activation function for
output layer's neurons is sigmoid function.
 After the training is finished, we use the following activation
function in both layers to do associative memory.
 By this training method the forward connection weight matrix
M can be obtained. We use M as the backward connection
weight matrix.
Proof of the New Model’s
Stability
 We can define the energy function as
Since
we get the energy
function equivalent form as follows
 The energy change
due to the state change of a is
Proof of the New Model’s
Stability
 By the BAM theorem
has only three values, i.e., -2,0 and 2. If
, we have
So,
and
So,
we have
This is
the situation of zero change in and we don’t consider this
case. The energy change
due to the state change of
is the same as . Hence
along discrete trajectories
as claimed.
Proof of the New Model’s
Stability
 Since E is bounded below
the associative memory of the extended BAM converges
to some stable points, meaning that, the network is stable.
Experiment Result
 The experiment results show that the New Model is much
more powerful than the other models to carry out
associative memory.
 In our experiment the network consists of 8 processing
units(neurons) for each layer. The set of vector pairs to
be stored is
The experiments are carried out in the following four
cases.
Experiment Result
Experiment Result
Experiment Result
 Using the same method as above, we get the following
results.
References