Title page for ETD etd-42598-01830

Type of Document Dissertation
Author Schinazi, Robert Glen
Author's Email Address reasor@VTVM1.cc.vt.edu
URN etd-42598-01830
Title Designing Massive 3-Dimensional Neural Networks with Chromosomal-Based Simulated Development
Degree PhD
Department Industrial and Systems Engineering
Advisory Committee
Advisor Name Title
Reasor, Roderick J. Committee Chair
Lu, Guo-Quan Committee Member
Rees, Loren Paul Committee Member
Sarin, Subhash C. Committee Member
Sullivan, William G. Committee Member
  • Neural Network
  • Cerebral cortex
  • Neocortex
  • automata simulation
  • cell growth simulation
  • cell growth modeling
Date of Defense 1995-12-04
Availability unrestricted
Designing Massive 3-Dimensional Neural Networks with

Chromosomal-Based Simulated Development

By Robert Glen Schinazi


A technique for designing and optimizing the next generation of smart process controllers

has been developed in this dissertation. The literature review indicated that neural networks held

the most promise for this application, yet fundamental limitations have prevented their

introduction to commercial settings thus far. This fundamental limitation has been overcome

through the enhancement of neural network theory.

The approach taken in this research was to produce highly intelligent process control

systems by accurately modeling the nervous structures of higher biological organisms. The

mammalian cerebral cortex was selected as the primary model since it is the only computational

element capable of interpreting and complex patterns that develop over time. However the choice

of the mammalian cerebral cortex as the model introduced two new levels of network complexity.

First, the cerebral cortex is a three dimensional structure with extremely complicated patterns of

interconnectivity. Second, the structure of the cerebral cortex can only be realized when

thousands or millions of neurons are integrated into a massive scale neural network. The neural

networks developed in this research were designed around the Hebbian adaptation, the only

training technique proven by the literature review to be applicable to massive scale networks.

These design difficulties were resolved by not only modeling the cerebral cortex, but the

process by which it develops and evolves in biological systems. To complete this model, an

advanced genetic algorithm was produced, and a technique was developed to encode all

functional and structural parameters that define the cerebral cortex into the artificial chromosome.

The neural networks were designed by a cell growth simulation program that decoded the

structural and functional information on the chromosome. The cell growth simulation program is

capable of producing patterns of differentiation unique for any slight variations in the genetic

parameters. These growth patterns are similar to patterns of cellular differentiation seen in

biological systems. While the computational resources needed to implement a massive scale

neural network are beyond that available in existing computer systems, the technique has

produced output lists which fully define the interconnections and functional characteristic of the

neurons, thereby laying the foundation for their future use in process control.

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