Design of an Optimization Model
using improved Group Method of Data Handling (GMDH)
PhD proposal defense
by
Maryam Pournasir 1081600004
Supervisor : Dr. Md. Jahangir Alam
Co-supervisor: Dr. G. Marthandan
The main components of soft computing such as artificial neural networks have been recognized
to have a strong capability in solving complex non-linear problems. Research shows that most of
artificial neural network methods have some specific limitations and requirements like, the
operational problem encountered when attempting to simulate the parallelism of neural networks.
Group Method of Data Handling (GMDH) has been used in solving artificial neural network
problems by many researchers with partial success. GMDH is a family of inductive algorithms for
computer-based mathematical modeling of multi-parametric datasets that are generated by using
the heuristic self-organization method. Although GMDH provides for a systematic procedure of
system modeling and prediction, it has also a number of shortcomings.
In order to alleviate the problems, a number of researchers have attempted to hybridize GMDH
with some evolutionary optimization methods. A common type of problem encountered is
optimizing nonlinear functions. There are some popular algorithms to solve nonlinear
optimization problems, but they involve numerous function evaluations, which prove expensive.
Many popular algorithms that are currently available for finding nonlinear least squares estimators
are sometimes inadequate. There may be instances when singularity problem occurs and thereby
its inverse cannot be obtained easily. So there is a need to have an algorithm to overcome the
shortcomings in the existing procedure. A hybrid method which combines the LevenbergMarquardt method with genetic algorithm will give better result and therefore would be effective
for nonlinear least squares estimation. So far, none of the past researchers have attempted this
novel method.
In this research, an attempt will be made to use Genetic Algorithm (GA), which is an optimization
technique for optimal design of connectivity configurations, in Levenberg-Marquardt method to
eliminate the dependence on a user-specified initial guess for calculating the coefficients of the
neurons and for increasing the accuracy.
Using this novel combination, it is intended to improve the GMDH with Levenberg-Marquardt
method using genetic algorithm to overcome the inverse problems. Hence the objective of the
research is to design an optimization model using improved GMDH to solve inverse problems and
to validate the model in a real world application.
To train and test the model, the data will be simulated for a real-world application. Data
normalization will also be carried as a part of preparation of data for GMDH. A part of data will
be used to train the improved GMDH-based neural network and the remaining data will be used
for validating the performance of the model.
Past research shows that there are several conflicting performance criterion in determining the
optimal production control policy, especially with respect to inventory control. So it is proposed
to validate the improved GMDH model for its performance and applicability in just-in-time base
kanban systems.