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Predictive Modular Neural Networks: Applications to Time Series / Edition 1

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This book presents a unified methodology for designing modular neural networks. A family of online algorithms for time series classification, prediction and identification are developed; and a rigorous mathematical analysis of their properties is provided. Case studies involving a number of real world problems are also presented. Finally, an overview of the modular neural networks literature, including coverage of theoretical and experimental analysis, is provided. Predictive Modular Neural Networks: Applications to Time Series is an important reference work for engineers, computer scientists, and other researchers working in time series analysis, neural networks, control engineering, data mining and other intelligent and decision support areas. The book will also be of interest to researchers in biological and medical informatics.
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Product Details

Table of Contents

1 Introduction 1
Pt. I Known Sources 9
2 Premonn Classification and Prediction 11
3 Generalizations of the Basic Premonn 39
4 Mathematical Analysis 59
5 System Identification by the Predictive Modular Approach 81
Pt. II Applications 99
6 Implementation Issues 101
7 Classification of Visually Evoked Responses 109
8 Prediction of Short Term Electric Loads 123
9 Parameter Estimation for and Activated Sludge Process 135
Pt. III Unknown Sources 147
10 Source Identification Algorithms 149
11 Convergence of Parallel Data Allocation 173
12 Convergence of Serial Data Allocation 209
Pt. IV Connections 247
13 Bibliographic Remarks 249
14 Epilogue 267
App. A Mathematical Concepts 271
References 283
Index 313
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