Design, Analysis and Realtime Realization of Artificial Neural Network for Control and Classification

Abstract

Artificial neural networks (ANNs) are parallel architectures for processing information even though they are usually realized on general-purpose digital computers. This research has been focused on the design, analysis and real-time realization of artificial neural networks using programmable analog hardware for control and classification. We have investigated field programmable analog arrays (FPAAs) for realizing artificial neural networks (ANN). Our research results and products include a general theoretical limit on the number of neurons required by an ANN to classify a given number of data points, a design methodology for the efficient use of specific FPAA resources in ANN applications, several multi-chip FPAA implementations of ANNs for classification experiments, several single-chip FPAA implementations of analog PID controllers for an unmanned ground vehicle (UGV), experimental evaluation of FPAA PID controllers with a conventional digital PID controller on a UGV, and finally a single-chip FPAA implementation of a (non-linear) ANN controller for comparison with the previous FPAA PID controller on a UGV.

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These results are collected as four papers formatted for publication and comprising chapters 3, 4, 5, and 6 of this thesis. The first paper develops our general bound for neural network complexity. The second presents a systematic approach based on the upper bound theory for implementing and simplifying neural network structures in FPAA technology. In the third paper, a FPAA based PID controller was designed and characterized in a path-tracking UGV; some of the results from this report are used as a baseline in the fourth paper. In the fourth paper, a FPAA based ANN controller is designed to control a path-tracking UGV and is investigated analytically and with simulation before its performance was experimentally compared to the previously designed FPAA PID controller regarding speed, stability and robustness. In conclusion, this dissertation focuses on the design, analysis and real-time realization of artificial neural networks. The proposed upper bound for neural network complexity provides guidelines for reducing hardware requirements and applies to any layered ANN approach to classification. It is complemented by the neural network structure simplification method which exploits specific features available in the FPAA technology which we used in our experiments and which we believe possess great potential for future real-time control and classification applications.