Synopses & Reviews
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.
Synopsis
The subject of this book is predictive modular neural networks and their ap- plication to time series problems: classification, prediction and identification. The intended audience is researchers and graduate students in the fields of neural networks, computer science, statistical pattern recognition, statistics, control theory and econometrics. Biologists, neurophysiologists and medical engineers may also find this book interesting. In the last decade the neural networks community has shown intense interest in both modular methods and time series problems. Similar interest has been expressed for many years in other fields as well, most notably in statistics, control theory, econometrics etc. There is a considerable overlap (not always recognized) of ideas and methods between these fields. Modular neural networks come by many other names, for instance multiple models, local models and mixtures of experts. The basic idea is to independently develop several "subnetworks" (modules), which may perform the same or re- lated tasks, and then use an "appropriate" method for combining the outputs of the subnetworks. Some of the expected advantages of this approach (when compared with the use of "lumped" or "monolithic" networks) are: superior performance, reduced development time and greater flexibility. For instance, if a module is removed from the network and replaced by a new module (which may perform the same task more efficiently), it should not be necessary to retrain the aggregate network.
Description
Includes bibliographical references (p. [283]-312) and index.
Table of Contents
Preface. 1. Introduction. Part I: Known Sources. 2. PREMONN Classification and Prediction. 3. Generalizations of the Basic Premonn. 4. Mathematical Analysis. 5. System Identification by the Predictive Modular Approach. Part II: Applications. 6. Implementation Issues. 7. Classification of Visually Evoked Responses. 8. Prediction of Short Term Electric Loads. 9. Parameter Estimation for and Activated Sludge Process. Part III: Unknown Sources. 10. Source Identification Algorithms. 11. Convergence of Parallel Data Allocation. 12. Convergence of Serial Data Allocation. Part IV: Connections. 13. Bibliographic Remarks. 14. Epilogue. Appendices. References. Index.