ARTIFICIAL NEURAL NETWORK TRAINING AND SOFTWARE IMPLEMENTATION TECHNIQUES -- ARTIFICIAL NEURAL NETWORK TRAINING AND SOFTWARE IMPLEMENTATION TECHNIQUES -- LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA -- CONTENTS -- PREFACE -- Chapter 1: INTRODUCTION -- Chapter 2: FEED-FORWARD NEURAL NETWORKS -- 2.1. TOPOLOGY -- 2.2. FUNCTIONALITY, PATTERN-RECOGNITION & FORECASTING -- 2.3. REPRESENTATION -- Chapter 3: FFANN SOFTWARE SIMULATION -- 3.1. SEQUENTIAL SOFTWARE SIMULATION -- 3.2. PARALLEL SOFTWARE SIMULATION -- Chapter 4: FFANN TRAINING CONCEPT -- 4.1. TRAINING DATA PREPARATIONS -- 4.2. ERROR CALCULATIONS -- 4.3. TERMINATION CONDITIONS -- Chapter 5: TRAJECTORY-DRIVEN TRAINING PARADIGM -- 5.1. BACKPROPAGATION TRAINING -- 5.2. OTHER COMMON TRAJECTORY-DRIVEN TRAINING METHODS -- Chapter 6: EVOLUTIONARY-BASED TRAINING PARADIGM -- 6.1. GENETIC-ALGORITHM BASEDTRAINING METHODS -- 6.2. OTHER COMMON EVOLUTIONARY-BASED TRAINING METHODS -- Chapter 7: FFANN SIMULATION UTILIZING GRAPHIC-PROCESSING UNITS -- 7.1. GPU PROGRAMMING PARADIGMS -- 7.2. ANN GPU-BASED SIMULATION TECHNIQUES -- Chapter 8: CONCLUSION -- REFERENCES -- INDEX -- Blank Page.