Advisor

George G. Lendaris

Date of Award

1996

Document Type

Dissertation

Degree Name

Doctor of Philosophy (Ph.D.) in Electrical and Computer Engineering

Department

Electrical and Computer Engineering

Physical Description

ix, 142 leaves: ill. 28 cm.

Subjects

Neural networks (Computer science), Algorithms, Neural circuitry

DOI

10.15760/etd.1354

Abstract

Artificial neural networks are computational paradigms which are inspired by biological neural networks (the human brain). Recurrent neural networks (RNNs) are characterized by neuron connections which include feedback paths. This dissertation uses the dynamics of RNN architectures for solving linear and certain nonlinear equations. Neural network with linear dynamics (variants of the well-known Hopfield network) are used to solve systems of linear equations, where the network structure is adapted to match properties of the linear system in question. Nonlinear equations inturn are solved using the dynamics of nonlinear RNNs, which are based on feedforward multilayer perceptrons. Neural networks are well-suited for implementation on special parallel hardware, due to their intrinsic parallelism. The RNNs developed here are implemented on a neural network processor (NNP) designed specifically for fast neural type processing, and are applied to the inverse kinematics problem in robotics, demonstrating their superior performance over alternative approaches.

Description

If you are the rightful copyright holder of this dissertation or thesis and wish to have it removed from the Open Access Collection, please submit a request to pdxscholar@pdx.edu and include clear identification of the work, preferably with URL

Persistent Identifier

http://archives.pdx.edu/ds/psu/4462

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