34 Best Books on Neural Network and Fuzzy Logic

Book Review: This book covers various topics, including feedforward networks, backpropagation, self-organizing maps, PCA, and hierarchical machines. It draws inspiration from biological models of brain function and structure, and includes network models such as adaptive resonance theory, BCS/FCS, and integrate-and-fire models, among others. With applications ranging from pattern recognition to image processing and clustering, this book is a valuable resource for both advanced undergraduates and graduate students.

Book Review: This informative book is geared towards practicing engineers and research scientists, as it covers the fundamentals of computing with artificial neural networks. It presents several emerging and exciting areas of neural network models, with a particular focus on their pattern processing features. The book also showcases real-world applications of these networks in speech and image processing. Topics covered in the book include activation and synaptic dynamics, learning laws, analysis of feedforward and feedback neural networks, as well as complex pattern recognition architectures.

Book Review: This book provides a comprehensive introduction to the theory, design, and applications of artificial neural networks. The book covers various types of neural networks, such as feedforward networks, Hopfield networks, and Kohonen networks, and their learning algorithms. It also includes discussions on neural network architectures, pattern recognition, supervised and unsupervised learning, and associative memory.

Book Review: This book is a valuable resource for both undergraduate and graduate students in the field of neural networks and neurocomputing. It provides a comprehensive overview of neural network theory, covering diverse applications in a unique manner. The structures of artificial neural networks are distinguished by various classes of graphs, adding to the book’s originality. Additionally, the book delves into concepts of artificial intelligence and intelligent systems, using examples, exercises, and graphics to aid in comprehension. The book includes precise analyses of perceptron and LMS-theory-based neural networks, as well as multilayer feedforward networks and self-organizing competitive learning neural networks.

“Fundamentals of Neural Networks: Architectures, Algorithms and Applications” Book Review: This book provides an introduction to neural networks in language that is easy to understand. It covers systematic discussions of major neural networks and presents all the related fundamentals. The use of examples and illustrations throughout the book helps readers to better understand the concepts being presented.

“Fuzzy Logic with Engineering Applications” Book Review: This student-friendly book focuses on major advances in the field of fuzzy logic. It includes illustrations and examples that use MATLAB code to help readers learn the basic operations, including the generation of membership functions and specialized applications. The book covers the latest advancements in fuzzy logic, such as MLFE methods using genetic algorithms, cognitive mapping, fuzzy agent-based models, and total uncertainty. It avoids redundant topics and presents a variety of methods and techniques. The book has been updated, with every chapter revised and new illustrations and examples included throughout. Topics covered in the book include basic operations, membership function generation, specialized applications, genetic algorithms, cognitive mapping, fuzzy agent-based models, overall uncertainty, and a final question segment. This book will serve as an excellent guide for undergraduate electrical engineering students.

“Fuzzy Set Theory Fuzzy Logic and their Applications” Book Review: This textbook is aimed at postgraduate students in various engineering colleges who wish to learn the basics of fuzzy set theory and fuzzy logic and develop new applications. It covers the mathematical aspects of fuzzy set theory and fuzzy logic, making it easier for readers to solve problems and understand the theory. The book includes examples and new techniques to help readers gain a thorough understanding of the subject. It will serve as an excellent guide for undergraduate electrical engineering students as well.

“Neural Networks, Fuzzy Logic and Genetic Algorithms: Synthesis and Applications (Computer)” Book Review: This book is intended for undergraduate and first-year postgraduate students in engineering, as well as researchers and academicians. It covers the technologies behind soft computing that are linked with computational intelligence. The book covers a range of topics, including Networks Associative Memory Adaptive Resonance Theory and Neural Networks, Fundamentals of Neural Networks Backpropagation, Fuzzy Systems and Fuzzy Set Theory, and more. The book also provides a detailed description of FL-GA hybridization, with vivid examples from various architectures. The book includes detailed information about various technology architectures, and a CD-ROM is provided for practical representation of concepts.

“Type-2 Fuzzy Logic: Theory and Applications (Studies in Fuzziness and Soft Computing)” Book Review: This book serves as a textbook in many universities and is also a valuable reference tool. It presents new methods for constructing intelligent systems using type-2 fuzzy logic and soft computing techniques. The book combines type-2 fuzzy logic with traditional soft computing techniques to create more powerful hybrid intelligent systems, leveraging the benefits of each approach.

“Introduction To Type–2 Fuzzy Logic Control: Theory and Applications (IEEE Press Series on Computational Intelligence)” Book Review: The primary audience for this book includes engineers, researchers, and graduate students who are interested in learning about type-2 fuzzy logic control. It provides experimental and simulation results, along with downloadable computer programs. The book also includes background chapters on type-2 fuzzy logic and a literature survey on fuzzy logic and related type-2 fuzzy control. Illustrative examples are included to aid readers in understanding the concepts presented.

“Fuzzy Logic For Business, Finance, And Management (2nd Edition) (Advances In Fuzzy Systems-applications And Theory)” Book Review: The book appeals to readers from the fields of business, finance, management, and socio-economic sciences who are interested in the application of fuzzy logic. It serves as a valuable reference for forecasting, decision making, and evaluation in various environments. The book explores the use of human experience and judgement to facilitate relatable reasoning in fuzzy logic and to arrive at sound conclusions.

“Fuzzy Logic and Its Application to Technology and Management” Book Review: This book contains mathematical information on fuzzy logic, as well as information on information technology and operations management. It explores the application of fuzzy logic in recent technologies and management strategies, making it useful for engineers and technologists looking to expand their knowledge of fuzzy logic and its applications in various fields.

“Fuzzy Logic: Applications, Systems and Technologies (Mathematics Research Developments)” Book Review: The book introduces fuzzy theory as a problem-solving tool for uncertain engineering scenarios. It presents an inference structure that allows the application of human reasoning capabilities to knowledge-based systems. The book includes numerous examples of fuzzy logic applications in electrical, chemical, computer, and textile engineering. The language used is clear and straightforward, making it easily understandable for readers.

“Fuzzy Logic Foundations and Industrial Applications (International Series in Intelligent Technologies)” Book Review: This book is an ideal resource for anyone interested in understanding fuzzy logic theory and its applications in industry. The book covers various topics such as fuzzy sets, fuzzy reasoning, fuzzy control, and fuzzy decision-making. The chapters include Introduction to Fuzzy Sets, Fuzzy Reasoning, Fuzzy Control, Fuzzy Decision Making, Fuzzy Optimization, Fuzzy Pattern Recognition, and Fuzzy Modeling. The book provides a comprehensive overview of fuzzy logic theory and its practical applications.

“Fuzzy Logic: Intelligence, Control, and Information” Book Review: This book introduces the principles and techniques of fuzzy logic in intelligent systems, information technology, and control. It explores the relationships between fuzzy logic, associated concepts, techniques, and theories. The content is suitable for undergraduate electrical engineering students.

“Fuzzy Sets and Fuzzy Logic: Theory and Applications” Book Review: This book discusses the fundamental principles of fuzzy set theory and fuzzy logic, including basic concepts such as neural networks, general algorithms, and rough collections. It explores diverse topics ranging from fuzzy sets and crisp sets to fuzzy databases, decision-making, and engineering applications. The book is intended to be a valuable resource for undergraduate electrical engineering students.

“Fuzzy Set theory and its Applications” Book Review: This book serves as a guide for undergraduate electrical engineering students by discussing the theoretical foundations of fuzzy set theory and fuzzy logic. It covers a variety of topics including fuzzy logic, rough reasoning, fuzzy control, fuzzy data analysis, decision-making, and fuzzy modeling.

“Fuzzy Sets and Fuzzy Logic: Theory and A: Theory and Applications” Book Review: This book presents the theoretical foundations of fuzzy set theory and fuzzy logic, covering topics such as fuzzy sets, crisp sets, fuzzy databases, fuzzy decision-making and their applications in engineering. Each chapter includes a collection of exercises to reinforce learning. This book will be a valuable resource for undergraduate electrical engineering students.

“Introduction to Fuzzy Logic using MATLAB” Book Review: This book is a useful guide for anyone interested in learning fuzzy logic theory and its implementation using MATLAB. The book covers various topics such as fuzzy sets, fuzzy relations, fuzzy inference, fuzzy clustering, and fuzzy neural networks. The chapters include Introduction to Fuzzy Sets, Fuzzy Relations, Fuzzy Inference Systems, Fuzzy Clustering, Fuzzy Neural Networks, and Fuzzy Control Systems. The book provides a practical approach to understanding and implementing fuzzy logic using MATLAB.

“Practical Applications of Fuzzy Technologies (The Handbooks of Fuzzy Sets)” Book Review: This book is a comprehensive guide to the practical applications of fuzzy logic in various fields. The book covers topics such as fuzzy clustering, fuzzy control, fuzzy decision-making, fuzzy image processing, and fuzzy optimization. The chapters include Fuzzy Control, Fuzzy Decision-Making, Fuzzy Image Processing, Fuzzy Clustering, Fuzzy Optimization, and Applications of Fuzzy Technologies. The book provides a practical approach to applying fuzzy logic in real-world scenarios and is a valuable resource for researchers, practitioners, and students alike.

Book Review: This book discusses neural networks from an engineering perspective. It covers essential aspects such as the learning process, backpropagation, radial basis functions, recurrent and modular networks, neurodynamics, and VLSI implementation. The book also provides an in-depth analysis of backpropagation learning and multi-layer perceptrons, and includes chapters with objectives, problems, worked examples, illustrations, and a glossary. The book explores different facets of neural networks and their potential applications. It can be a valuable resource for engineers and students interested in neural networks.

“Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence” Book Review: This book is intended for students majoring in engineering, computer science, mathematics, psychology, and biology. It covers topics such as trainable dynamical systems, principles of adaptation and self-organization, convergence and global stability, a new geometric theory of fuzzy sets, systems, and associative memories. The book combines the fields of neural networks and fuzzy systems at a theoretical level, and also explores the applications of new adaptive fuzzy systems in control and signal processing.

“Digital Neural Networks” Book Review: This book is a valuable resource for electrical engineers, computer engineers, and computer scientists. It comprehensively describes various neural models, covering both their fundamental theories and practical implementations. The book provides clear explanations of the applications, algorithms, and architectures of neural networks, making it easily understandable for the readers.

Book Review: This book showcases the latest progress in fuzzy sets and fuzzy logic. It features various applications of fuzzy sets and fuzzy logic, covering basic types of fuzzy sets, relations between fuzzy and crisp sets, fuzzy numbers and their arithmetic operations, and fuzzy equations. The book also covers practical aspects such as fuzzy systems and controllers, fuzzy databases, fuzzy decision-making, and their engineering applications. It is an essential reference for those interested in fuzzy set theory and fuzzy logic.

Book Review: The main emphasis of this book is on the theoretical aspects of fuzzy and soft computing, as well as on practical applications and their empirical validation. With numerous exercises programmed using MATLAB and practical problem-solving, it is designed as a self-study resource for researchers interested in learning the basics and advanced concepts of neuro-fuzzy and soft computing within the framework of computational intelligence.

“Fuzzy Logic and Soft Computing” Book Review: This book is a comprehensive textbook that introduces the concepts of fuzzy logic and soft computing. The book covers topics such as fuzzy sets, fuzzy logic, neural networks, genetic algorithms, and hybrid systems. The chapters include Introduction to Fuzzy Sets, Fuzzy Logic, Fuzzy Control Systems, Neural Networks, Genetic Algorithms, and Hybrid Systems. The book provides a thorough introduction to the fundamental concepts of fuzzy logic and soft computing and their applications in various fields. It is a valuable resource for students and researchers in the field of computational intelligence.

“Timed Petri Nets: Theory and Application” Book Review: The book provides a systematic and comprehensive theoretical framework for timed Petri nets, along with numerous industrial applications to facilitate the application of theoretical concepts in practical settings. Additionally, it includes the latest research and developments in the field of timed Petri nets. As a result, the book is a valuable resource for researchers and practitioners in the field of Discrete Event Dynamic Systems, offering practical guidance and theoretical insights.

“Artificial Neural Nets and Genetic Algorithms” Book Review: The book revolves around the concepts of artificial neural networks and genetic algorithms, which originate from mathematical models that provide insights into natural processes. Rather than the process itself, this book emphasizes the process model. It highlights new computational techniques with broad applications in various fields. The topics in this book are closely related to the aforementioned techniques and applications. By reading this book, readers will be able to apply genetic algorithms and theorems to practical problems.

“Modeling Analysis and control Design for the Upfc with Fuzzy Theory and Genetic Algorithm Application” Book Review: This book is a comprehensive guide to the Unified Power Flow Controller (UPFC), utilizing fuzzy theory and genetic algorithms for control design. The book covers topics such as power system analysis, UPFC modeling, control design, and simulation. The chapters include Introduction to Power Systems, UPFC Modeling, Fuzzy Control Design, Genetic Algorithm Application, and Simulation Results. The book provides a valuable resource for researchers and practitioners in power system control, offering practical guidance and theoretical insights.

“Fundamentals of Artificial Neural Networks” Book Review: The book provides an in-depth introduction to the basics of artificial neural networks. It covers a range of topics, including threshold gates, computational power of neural networks, and learning rules. In addition, the book discusses the mathematical theory of neural learning, adaptive multilayer neural networks, and associative neural memories. To assist with comprehension, the book includes over 200 computer-based problems, 700 references, and numerous illustrative examples. This book is particularly helpful for students in computer science and information technology engineering.