Understanding Neural Networks And Fuzzy Logic
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Understanding Neural Networks And Fuzzy Logic
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Author : Stamatios V. Kartalopoulos
language : en
Publisher: Wiley-IEEE Press
Release Date : 1996
Understanding Neural Networks And Fuzzy Logic written by Stamatios V. Kartalopoulos and has been published by Wiley-IEEE Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Computers categories.
Understand the fundamentals of the emerging field of fuzzy neural networks, their applications and the most used paradigms with this carefully organized state-of-the-art textbook. Previously tested at a number of noteworthy conference tutorials, the simple numerical examples presented in this book provide excellent tools for progressive learning. UNDERSTANDING NEURAL NETWORKS AND FUZZY LOGIC offers a simple presentation and bottom-up approach that is ideal for working professional engineers, undergraduates, medical/biology majors, and anyone with a nonspecialist background. Sponsored by: IEEE Neural Networks Council
Foundations Of Neural Networks Fuzzy Systems And Knowledge Engineering
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Author : Nikola K. Kasabov
language : en
Publisher: Marcel Alencar
Release Date : 1996
Foundations Of Neural Networks Fuzzy Systems And Knowledge Engineering written by Nikola K. Kasabov and has been published by Marcel Alencar this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with Computers categories.
Combines the study of neural networks and fuzzy systems with symbolic artificial intelligence (AI) methods to build comprehensive AI systems. Describes major AI problems (pattern recognition, speech recognition, prediction, decision-making, game-playing) and provides illustrative examples. Includes applications in engineering, business and finance.
Explainable Neural Networks Based On Fuzzy Logic And Multi Criteria Decision Tools
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Author : József Dombi
language : en
Publisher: Springer Nature
Release Date : 2021-04-28
Explainable Neural Networks Based On Fuzzy Logic And Multi Criteria Decision Tools written by József Dombi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-28 with Technology & Engineering categories.
The research presented in this book shows how combining deep neural networks with a special class of fuzzy logical rules and multi-criteria decision tools can make deep neural networks more interpretable – and even, in many cases, more efficient. Fuzzy logic together with multi-criteria decision-making tools provides very powerful tools for modeling human thinking. Based on their common theoretical basis, we propose a consistent framework for modeling human thinking by using the tools of all three fields: fuzzy logic, multi-criteria decision-making, and deep learning to help reduce the black-box nature of neural models; a challenge that is of vital importance to the whole research community.
C Neural Networks And Fuzzy Logic
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Author : Hayagriva V. Rao
language : en
Publisher:
Release Date : 1996
C Neural Networks And Fuzzy Logic written by Hayagriva V. Rao and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with C++ (Computer program language) categories.
Learning And Soft Computing
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Author : Vojislav Kecman
language : en
Publisher: MIT Press
Release Date : 2001
Learning And Soft Computing written by Vojislav Kecman and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Computers categories.
This textbook provides a thorough introduction to the field of learning from experimental data and soft computing. Support vector machines (SVM) and neural networks (NN) are the mathematical structures, or models, that underlie learning, while fuzzy logic systems (FLS) enable us to embed structured human knowledge into workable algorithms. The book assumes that it is not only useful, but necessary, to treat SVM, NN, and FLS as parts of a connected whole. Throughout, the theory and algorithms are illustrated by practical examples, as well as by problem sets and simulated experiments. This approach enables the reader to develop SVM, NN, and FLS in addition to understanding them. The book also presents three case studies: on NN-based control, financial time series analysis, and computer graphics. A solutions manual and all of the MATLAB programs needed for the simulated experiments are available.
Deep Neuro Fuzzy Systems With Python
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Author : Himanshu Singh
language : en
Publisher: Apress
Release Date : 2019-11-30
Deep Neuro Fuzzy Systems With Python written by Himanshu Singh and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-30 with Computers categories.
Gain insight into fuzzy logic and neural networks, and how the integration between the two models makes intelligent systems in the current world. This book simplifies the implementation of fuzzy logic and neural network concepts using Python. You’ll start by walking through the basics of fuzzy sets and relations, and how each member of the set has its own membership function values. You’ll also look at different architectures and models that have been developed, and how rules and reasoning have been defined to make the architectures possible. The book then provides a closer look at neural networks and related architectures, focusing on the various issues neural networks may encounter during training, and how different optimization methods can help you resolve them. In the last section of the book you’ll examine the integrations of fuzzy logics and neural networks, the adaptive neuro fuzzy Inference systems, and various approximations related to the same. You’ll review different types of deep neuro fuzzy classifiers, fuzzy neurons, and the adaptive learning capability of the neural networks. The book concludes by reviewing advanced neuro fuzzy models and applications. What You’ll Learn Understand fuzzy logic, membership functions, fuzzy relations, and fuzzy inference Review neural networks, back propagation, and optimization Work with different architectures such as Takagi-Sugeno model, Hybrid model, genetic algorithms, and approximations Apply Python implementations of deep neuro fuzzy system Who This book Is For Data scientists and software engineers with a basic understanding of Machine Learning who want to expand into the hybrid applications of deep learning and fuzzy logic.
Fuzzy Logic For Beginners
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Author : Masao Mukaidono
language : en
Publisher: World Scientific
Release Date : 2001
Fuzzy Logic For Beginners written by Masao Mukaidono and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Computers categories.
There are many uncertainties in the real world. Fuzzy theory treats a kind of uncertainty called fuzziness, where it shows that the boundary of yes or no is ambiguous and appears in the meaning of words or is included in the subjunctives or recognition of human beings. Fuzzy theory is essential and is applicable to many systems -- from consumer products like washing machines or refrigerators to big systems like trains or subways. Recently, fuzzy theory has been a strong tool for combining new theories (called soft computing) such as genetic algorithms or neural networks to get knowledge from real data. This introductory book enables the reader to understand easily what fuzziness is and how one can apply fuzzy theory to real problems -- which explains why it was a best-seller in Japan.
Neural Network And Fuzzy Logic Applications In C C
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Author : Stephen T. Welstead
language : en
Publisher: Wiley
Release Date : 1994-07-13
Neural Network And Fuzzy Logic Applications In C C written by Stephen T. Welstead and has been published by Wiley this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994-07-13 with Computers categories.
Using an engineering and science perspective, it explores diverse neural network, fuzzy logic and genetic algorithm techniques plus developing applications best suited for each of the methods discussed. Sample results are described and judgment made as to how well each application worked. The book/disk set includes an object-oriented user interface along with the code for numerous programs.
Fuzzy Logic And Neurofuzzy Applications Explained
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Author : Constantin Von Altrock
language : en
Publisher: Prentice Hall
Release Date : 1995
Fuzzy Logic And Neurofuzzy Applications Explained written by Constantin Von Altrock and has been published by Prentice Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Computers categories.
Disks contain simulation edition of fuzzyTECH development software from Inform Software corporation.
Neural Networks Fuzzy Logic And Genetic Algorithm
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Author : S. RAJASEKARAN
language : en
Publisher: PHI Learning Pvt. Ltd.
Release Date : 2003-01-01
Neural Networks Fuzzy Logic And Genetic Algorithm written by S. RAJASEKARAN and has been published by PHI Learning Pvt. Ltd. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-01-01 with Computers categories.
This book provides comprehensive introduction to a consortium of technologies underlying soft computing, an evolving branch of computational intelligence. The constituent technologies discussed comprise neural networks, fuzzy logic, genetic algorithms, and a number of hybrid systems which include classes such as neuro-fuzzy, fuzzy-genetic, and neuro-genetic systems. The hybridization of the technologies is demonstrated on architectures such as Fuzzy-Back-propagation Networks (NN-FL), Simplified Fuzzy ARTMAP (NN-FL), and Fuzzy Associative Memories. The book also gives an exhaustive discussion of FL-GA hybridization. Every architecture has been discussed in detail through illustrative examples and applications. The algorithms have been presented in pseudo-code with a step-by-step illustration of the same in problems. The applications, demonstrative of the potential of the architectures, have been chosen from diverse disciplines of science and engineering. This book with a wealth of information that is clearly presented and illustrated by many examples and applications is designed for use as a text for courses in soft computing at both the senior undergraduate and first-year post-graduate engineering levels. It should also be of interest to researchers and technologists desirous of applying soft computing technologies to their respective fields of work.