Structure Level Adaptation For Artificial Neural Networks
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Structure Level Adaptation For Artificial Neural Networks
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Author : Tsu-Chang Lee
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Structure Level Adaptation For Artificial Neural Networks written by Tsu-Chang Lee and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
63 3. 2 Function Level Adaptation 64 3. 3 Parameter Level Adaptation. 67 3. 4 Structure Level Adaptation 70 3. 4. 1 Neuron Generation . 70 3. 4. 2 Neuron Annihilation 72 3. 5 Implementation . . . . . 74 3. 6 An Illustrative Example 77 3. 7 Summary . . . . . . . . 79 4 Competitive Signal Clustering Networks 93 4. 1 Introduction. . 93 4. 2 Basic Structure 94 4. 3 Function Level Adaptation 96 4. 4 Parameter Level Adaptation . 101 4. 5 Structure Level Adaptation 104 4. 5. 1 Neuron Generation Process 107 4. 5. 2 Neuron Annihilation and Coalition Process 114 4. 5. 3 Structural Relation Adjustment. 116 4. 6 Implementation . . 119 4. 7 Simulation Results 122 4. 8 Summary . . . . . 134 5 Application Example: An Adaptive Neural Network Source Coder 135 5. 1 Introduction. . . . . . . . . . 135 5. 2 Vector Quantization Problem 136 5. 3 VQ Using Neural Network Paradigms 139 Vlll 5. 3. 1 Basic Properties . 140 5. 3. 2 Fast Codebook Search Procedure 141 5. 3. 3 Path Coding Method. . . . . . . 143 5. 3. 4 Performance Comparison . . . . 144 5. 3. 5 Adaptive SPAN Coder/Decoder 147 5. 4 Summary . . . . . . . . . . . . . . . . . 152 6 Conclusions 155 6. 1 Contributions 155 6. 2 Recommendations 157 A Mathematical Background 159 A. 1 Kolmogorov's Theorem . 160 A. 2 Networks with One Hidden Layer are Sufficient 161 B Fluctuated Distortion Measure 163 B. 1 Measure Construction . 163 B. 2 The Relation Between Fluctuation and Error 166 C SPAN Convergence Theory 171 C. 1 Asymptotic Value of Wi 172 C. 2 Energy Function . .
Structure Level Adaptation For Artificial Neural Networks
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Author : Tsu-Chang Lee
language : en
Publisher:
Release Date : 1991
Structure Level Adaptation For Artificial Neural Networks written by Tsu-Chang Lee and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with Neural networks (Computer science) categories.
Neural Network Systems Techniques And Applications
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Author :
language : en
Publisher: Academic Press
Release Date : 1998-02-09
Neural Network Systems Techniques And Applications written by and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-02-09 with Computers categories.
The book emphasizes neural network structures for achieving practical and effective systems, and provides many examples. Practitioners, researchers, and students in industrial, manufacturing, electrical, mechanical,and production engineering will find this volume a unique and comprehensive reference source for diverse application methodologies. Control and Dynamic Systems covers the important topics of highly effective Orthogonal Activation Function Based Neural Network System Architecture, multi-layer recurrent neural networks for synthesizing and implementing real-time linear control,adaptive control of unknown nonlinear dynamical systems, Optimal Tracking Neural Controller techniques, a consideration of unified approximation theory and applications, techniques for the determination of multi-variable nonlinear model structures for dynamic systems with a detailed treatment of relevant system model input determination, High Order Neural Networks and Recurrent High Order Neural Networks, High Order Moment Neural Array Systems, Online Learning Neural Network controllers, and Radial Bias Function techniques. Coverage includes: - Orthogonal Activation Function Based Neural Network System Architecture (OAFNN) - Multilayer recurrent neural networks for synthesizing and implementing real-time linear control - Adaptive control of unknown nonlinear dynamical systems - Optimal Tracking Neural Controller techniques - Consideration of unified approximation theory and applications - Techniques for determining multivariable nonlinear model structures for dynamic systems, with a detailed treatment of relevant system model input determination
Structure Level Adaptation For Artificial Neural Networks
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Author : Tsu-Chang Lee
language : en
Publisher:
Release Date : 1990
Structure Level Adaptation For Artificial Neural Networks written by Tsu-Chang Lee and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with categories.
Methodologies For The Conception Design And Application Of Intelligent Systems Proceedings Of The 4th International Conference On Soft Computing In 2 Volumes
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Author : Gen Matsumoto
language : en
Publisher: World Scientific
Release Date : 1996-08-31
Methodologies For The Conception Design And Application Of Intelligent Systems Proceedings Of The 4th International Conference On Soft Computing In 2 Volumes written by Gen Matsumoto and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-08-31 with categories.
IIZUKA '96, the 4th International Conference on Soft Computing, emphasized the integration of the components of soft computing to promote the research work on post-digital computers and to realize the intelligent systems. At the conference, new developments and results in soft computing were introduced and discussed by researchers from academic, governmental, and industrial institutions.This volume presents the opening lectures by Prof. Lotfi A. Zadeh and Prof. Walter J. Freeman, the plenary lectures by seven eminent researchers, and about 200 carefully selected papers drawn from more than 20 countries. It documents current research and in-depth studies on the conception, design, and application of intelligent systems.
Advanced Techniques In Knowledge Discovery And Data Mining
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Author : Nikhil Pal
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-12-31
Advanced Techniques In Knowledge Discovery And Data Mining written by Nikhil Pal and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-12-31 with Computers categories.
Clear and concise explanations to understand the learning paradigms. Chapters written by leading world experts.
Explanation Based Neural Network Learning
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Author : Sebastian Thrun
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Explanation Based Neural Network Learning written by Sebastian Thrun and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
Lifelong learning addresses situations in which a learner faces a series of different learning tasks providing the opportunity for synergy among them. Explanation-based neural network learning (EBNN) is a machine learning algorithm that transfers knowledge across multiple learning tasks. When faced with a new learning task, EBNN exploits domain knowledge accumulated in previous learning tasks to guide generalization in the new one. As a result, EBNN generalizes more accurately from less data than comparable methods. Explanation-Based Neural Network Learning: A Lifelong Learning Approach describes the basic EBNN paradigm and investigates it in the context of supervised learning, reinforcement learning, robotics, and chess. `The paradigm of lifelong learning - using earlier learned knowledge to improve subsequent learning - is a promising direction for a new generation of machine learning algorithms. Given the need for more accurate learning methods, it is difficult to imagine a future for machine learning that does not include this paradigm.' From the Foreword by Tom M. Mitchell.
Shallow And Deep Learning Principles
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Author : Zekâi Şen
language : en
Publisher: Springer Nature
Release Date : 2023-06-01
Shallow And Deep Learning Principles written by Zekâi Şen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-01 with Technology & Engineering categories.
This book discusses Artificial Neural Networks (ANN) and their ability to predict outcomes using deep and shallow learning principles. The author first describes ANN implementation, consisting of at least three layers that must be established together with cells, one of which is input, the other is output, and the third is a hidden (intermediate) layer. For this, the author states, it is necessary to develop an architecture that will not model mathematical rules but only the action and response variables that control the event and the reactions that may occur within it. The book explains the reasons and necessity of each ANN model, considering the similarity to the previous methods and the philosophical - logical rules.
Recent Developments And New Direction In Soft Computing Foundations And Applications
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Author : Lotfi A. Zadeh
language : en
Publisher: Springer
Release Date : 2016-05-25
Recent Developments And New Direction In Soft Computing Foundations And Applications written by Lotfi A. Zadeh and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-05-25 with Technology & Engineering categories.
This book reports on advanced theories and cutting-edge applications in the field of soft computing. The individual chapters, written by leading researchers, are based on contributions presented during the 4th World Conference on Soft Computing, held May 25-27, 2014, in Berkeley. The book covers a wealth of key topics in soft computing, focusing on both fundamental aspects and applications. The former include fuzzy mathematics, type-2 fuzzy sets, evolutionary-based optimization, aggregation and neural networks, while the latter include soft computing in data analysis, image processing, decision-making, classification, series prediction, economics, control, and modeling. By providing readers with a timely, authoritative view on the field, and by discussing thought-provoking developments and challenges, the book will foster new research directions in the diverse areas of soft computing.
Multistrategy Learning
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Author : Ryszard S. Michalski
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Multistrategy Learning written by Ryszard S. Michalski and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
Most machine learning research has been concerned with the development of systems that implememnt one type of inference within a single representational paradigm. Such systems, which can be called monostrategy learning systems, include those for empirical induction of decision trees or rules, explanation-based generalization, neural net learning from examples, genetic algorithm-based learning, and others. Monostrategy learning systems can be very effective and useful if learning problems to which they are applied are sufficiently narrowly defined. Many real-world applications, however, pose learning problems that go beyond the capability of monostrategy learning methods. In view of this, recent years have witnessed a growing interest in developing multistrategy systems, which integrate two or more inference types and/or paradigms within one learning system. Such multistrategy systems take advantage of the complementarity of different inference types or representational mechanisms. Therefore, they have a potential to be more versatile and more powerful than monostrategy systems. On the other hand, due to their greater complexity, their development is significantly more difficult and represents a new great challenge to the machine learning community. Multistrategy Learning contains contributions characteristic of the current research in this area.