Title page for ETD etd-06092000-12150028

Type of Document Dissertation
Author Azam, Farooq
Author's Email Address fazam@vt.edu
URN etd-06092000-12150028
Title Biologically Inspired Modular Neural Networks
Degree PhD
Department Electrical and Computer Engineering
Advisory Committee
Advisor Name Title
VanLandingham, Hugh F. Committee Chair
Athanas, Peter M. Committee Member
Baumann, William T. Committee Member
Bay, John S. Committee Member
Saunders, William R. Committee Member
  • a priori expert knowledge
  • generalization
  • artificial neural networks
  • accuracy
  • robustness
  • Biologically inspired neural networks
  • modular neural networks
  • principle of divide and conquer
Date of Defense 2000-05-19
Availability unrestricted
This dissertation explores the modular learning in artificial neural

networks that mainly driven by the inspiration from the

neurobiological basis of the human learning. The presented

modularization approaches to the neural network design and learning

are inspired by the engineering, complexity, psychological and

neurobiological aspects. The main theme of this dissertation is to

explore the organization and functioning of the brain to discover

new structural and learning inspirations that can be subsequently

utilized to design artificial neural network.

The artificial neural networks are touted to be a neurobiologicaly

inspired paradigm that emulate the functioning of the vertebrate

brain. The brain is a highly structured entity with localized

regions of neurons specialized in performing specific tasks. On the

other hand, the mainstream monolithic feed-forward neural networks are

generally unstructured black boxes which is their major performance

limiting characteristic. The non explicit structure and monolithic

nature of the current mainstream artificial neural networks results

in lack of the capability of systematic incorporation of functional

or task-specific a priori knowledge in the artificial neural network

design process. The problem caused by these limitations are

discussed in detail in this dissertation and remedial solutions are

presented that are driven by the functioning of the brain and its

structural organization.

Also, this dissertation presents an in depth study of the currently

available modular neural network architectures along with

highlighting their shortcomings and investigates new modular

artificial neural network models in order to overcome pointed out

shortcomings. The resulting proposed modular neural network models

have greater accuracy, generalization, comprehensible simplified

neural structure, ease of training and more user confidence. These

benefits are readily obvious for certain problems, depending upon

availability and usage of available a priori knowledge about the


The modular neural network models presented in this dissertation

exploit the capabilities of the principle of divide and conquer in the

design and learning of the modular artificial neural networks. The

strategy of divide and conquer solves a complex computational

problem by dividing it into simpler sub-problems and then combining

the individual solutions to the sub-problems into a solution to the

original problem. The divisions of a task considered in this

dissertation are the automatic decomposition of the mappings to be

learned, decompositions of the artificial neural networks to minimize

harmful interaction during the learning process, and explicit

decomposition of the application task into sub-tasks that are

learned separately.

The versatility and capabilities of the new proposed modular neural

networks are demonstrated by the experimental results. A comparison

of the current modular neural network design techniques with the ones

introduced in this dissertation, is also presented for reference. The

results presented in this dissertation lay a solid foundation for

design and learning of the artificial neural networks that have sound

neurobiological basis that leads to superior design techniques. Areas

of the future research are also presented.

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