Author Type

Graduate Student

Date of Award

1990

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Shankar, Ravi

Abstract

Recent years have seen the renaissance of the neural network field. Significant advances in our understanding of neural networks and its possible applications necessitate investigations into possible implementation strategies. Among the presently available implementation medium, digital VLSI hardware is one of the more promising because of its maturity and availability. We discuss various issues connected with implementing neural networks in digital VLSI hardware. A new sigmoidal transfer function is proposed with that implementation in mind. Possible realizations of the function for stochastic and deterministic neural networks are discussed. Simulation studies of applying neural networks in constraint optimization and learning problems are carried out. These simulations were performed strictly in integer arithmetic. Simulation results provides an encouraging outlook for implementing these neural network applications in digital VLSI hardware. Important results concerning the sizes of various network values were found for learning algorithms.

Extent

226 p.

Comments

College of Engineering and Computer Science|FAU Electronic Theses and Dissertations Collection|Thesis (M.S.)--Florida Atlantic University, 1990.|College of Engineering and Computer Science

Identifier

FADT14646

Format

pdf

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