Fuzzy Systems and Knowledge Discovery

Fuzzy Systems and Knowledge Discovery

Author: Lipo Wang

Publisher: Springer Science & Business Media

ISBN: 9783540459163

Category: Computers

Page: 1340

View: 309

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This book constitutes the refereed proceedings of the Third International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2006, held in federation with the Second International Conference on Natural Computation ICNC 2006. The book presents 115 revised full papers and 50 revised short papers. Coverage includes neural computation, quantum computation, evolutionary computation, DNA computation, fuzzy computation, granular computation, artificial life, innovative applications to knowledge discovery, finance, operations research, and more.

Introduction to Neuro-Fuzzy Systems

Introduction to Neuro-Fuzzy Systems

Author: Robert Fuller

Publisher: Springer Science & Business Media

ISBN: 3790812560

Category: Business & Economics

Page: 289

View: 352

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This book contains introductory material to neuro-fuzzy systems. Its main purpose is to explain the information processing in mostly-used fuzzy inference systems, neural networks and neuro-fuzzy systems. More than 180 figures and a large number of (numerical) exercises (with solutions) have been inserted to explain the principles of fuzzy, neural and neuro-fuzzy systems. Also the mathematics applied in the models is carefully explained, and in many cases exact computational formulas have been derived for the rules in error correction learning procedures. Numerous models treated in the book will help the reader to design his own neuro-fuzzy system for his specific (managerial, industrial, financial) problem. The book can serve as a textbook for students in computer and management sciences who are interested in adaptive technologies.

Fuzzy Systems

Fuzzy Systems

Author: Ahmad Taher Azar

Publisher: BoD – Books on Demand

ISBN: 9789537619923

Category: Computers

Page: 228

View: 343

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While several books are available today that address the mathematical and philosophical foundations of fuzzy logic, none, unfortunately, provides the practicing knowledge engineer, system analyst, and project manager with specific, practical information about fuzzy system modeling. Those few books that include applications and case studies concentrate almost exclusively on engineering problems: pendulum balancing, truck backeruppers, cement kilns, antilock braking systems, image pattern recognition, and digital signal processing. Yet the application of fuzzy logic to engineering problems represents only a fraction of its real potential. As a method of encoding and using human knowledge in a form that is very close to the way experts think about difficult, complex problems, fuzzy systems provide the facilities necessary to break through the computational bottlenecks associated with traditional decision support and expert systems. Additionally, fuzzy systems provide a rich and robust method of building systems that include multiple conflicting, cooperating, and collaborating experts (a capability that generally eludes not only symbolic expert system users but analysts who have turned to such related technologies as neural networks and genetic algorithms). Yet the application of fuzzy logic in the areas of decision support, medical systems, database analysis and mining has been largely ignored by both the commercial vendors of decision support products and the knowledge engineers who use them.

Advanced Fuzzy Systems Design and Applications

Advanced Fuzzy Systems Design and Applications

Author: Yaochu Jin

Publisher: Physica

ISBN: 9783790817713

Category: Computers

Page: 272

View: 905

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Fuzzy rule systems have found a wide range of applications in many fields of science and technology. Traditionally, fuzzy rules are generated from human expert knowledge or human heuristics for relatively simple systems. In the last few years, data-driven fuzzy rule generation has been very active. Compared to heuristic fuzzy rules, fuzzy rules generated from data are able to extract more profound knowledge for more complex systems. This book presents a number of approaches to the generation of fuzzy rules from data, ranging from the direct fuzzy inference based to neural net works and evolutionary algorithms based fuzzy rule generation. Besides the approximation accuracy, special attention has been paid to the interpretabil ity of the extracted fuzzy rules. In other words, the fuzzy rules generated from data are supposed to be as comprehensible to human beings as those generated from human heuristics. To this end, many aspects of interpretabil ity of fuzzy systems have been discussed, which must be taken into account in the data-driven fuzzy rule generation. In this way, fuzzy rules generated from data are intelligible to human users and therefore, knowledge about unknown systems can be extracted.

Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms

Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms

Author: Lakhmi C. Jain

Publisher: CRC Press

ISBN: 0849398045

Category: Computers

Page: 368

View: 523

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Artificial neural networks can mimic the biological information-processing mechanism in - a very limited sense. Fuzzy logic provides a basis for representing uncertain and imprecise knowledge and forms a basis for human reasoning. Neural networks display genuine promise in solving problems, but a definitive theoretical basis does not yet exist for their design. Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms integrates neural net, fuzzy system, and evolutionary computing in system design that enables its readers to handle complexity - offsetting the demerits of one paradigm by the merits of another. This book presents specific projects where fusion techniques have been applied. The chapters start with the design of a new fuzzy-neural controller. Remaining chapters discuss the application of expert systems, neural networks, fuzzy control, and evolutionary computing techniques in modern engineering systems. These specific applications include: direct frequency converters electro-hydraulic systems motor control toaster control speech recognition vehicle routing fault diagnosis Asynchronous Transfer Mode (ATM) communications networks telephones for hard-of-hearing people control of gas turbine aero-engines telecommunications systems design Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms covers the spectrum of applications - comprehensively demonstrating the advantages of fusion techniques in industrial applications.

Deep Neuro-Fuzzy Systems with Python

Deep Neuro-Fuzzy Systems with Python

Author: Himanshu Singh

Publisher: Apress

ISBN: 9781484253618

Category: Computers

Page: 260

View: 408

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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 inferenceReview neural networks, back propagation, and optimizationWork 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.

New Trends in Fuzzy Systems

New Trends in Fuzzy Systems

Author: Dario Mancini

Publisher: World Scientific

ISBN: 9789814545662

Category:

Page: 288

View: 645

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The developments of fuzzy systems and fuzzy logic is permeating through the diverse branches of science where uncertainty has to be considered laying on the foundations and applicative developments. CIFT and MEPP conferences have been held in different venues in Scandinavia and Italy since 1990, and have stimulated the attention from academia and industry toward the novelties introduced by fuzzy logic and fuzzy systems theory. The papers presented in this volume are concerned with a wide vision of modern perspectives of science. These cover research areas such as management, financial and economic applications, urbanism and ecology, astronomical engineering, medical diagnosis and imaging, and human behavior. Contents:Retrieving Documents from Multiple Information Sources (R Yager & A Rybalov)Basic Principles of Rough Set Analysis (B Matarazzo)Conditional Measures: Old and New (G Coletti & R Scozzafava)Application of a New Fuzzy Identification Algorithm for the Control of a DC to DC Converter (A Luciano et al)Fuzzy Logic and the Engineering of Quality in Electronic Products (B Bosacchi)On Some Order Structures in Fuzzy Modelling (M Fedrizzi et al)Fuzzy Control for Medicine: State of the Art and New Perspectives (S Giove)The Generalised Perceptron is a Fuzzy Neuron and a Fuzzy Rule (L Kallin & P Eklund)Application of MEP-Based Fuzzy Clustering to the Segmentation of Multivariate Medical Images (F Masulli et al)and other papers Readership: Students, engineers, and researchers in fuzzy systems, artificial intelligence, systems/knowledge engineering, biomedical engineering, civil engineering, applied mathematics, materials science, economics/finance and management. keywords:Fuzzy Logic;Fuzzy Modeling;Fuzzy Rule;Fuzzy Clustering

Advanced Fuzzy Logic Technologies in Industrial Applications

Advanced Fuzzy Logic Technologies in Industrial Applications

Author: Ying Bai

Publisher: Springer Science & Business Media

ISBN: 9781846284694

Category: Technology & Engineering

Page: 334

View: 830

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This book introduces a dynamic, on-line fuzzy inference system. In this system membership functions and control rules are not determined until the system is applied and each output of its lookup table is calculated based on current inputs. The book describes the real-world uses of new fuzzy techniques to simplify readers’ tuning processes and enhance the performance of their control systems. It further contains application examples.

Design of Analog Fuzzy Logic Controllers in CMOS Technologies

Design of Analog Fuzzy Logic Controllers in CMOS Technologies

Author: Carlos Dualibe

Publisher: Springer Science & Business Media

ISBN: 9781402073595

Category: Computers

Page: 214

View: 969

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Nowadays, real-time applications of Fuzzy Logic in different domains are being increasingly reported. ASIC-based analog hardware becomes an interesting solution for these kinds of applications because it benefits from: savings on silicon surface and power consumption, readily accomplishment with strict timing constraints and cost-effective production. This book focuses in-depth on the VLSI CMOS implementation and application of programmable analog Fuzzy Logic Controllers following a mixed-signal philosophy. This is to say, signals are processed in the analog domain whereas programmability is achieved by means of standard digital memories. This approach highlights the following crucial aspects: *The comprehensive study and analysis of the main analog fuzzy operators: Fuzzy Membership Functions, T-Norm, T-CoNorm and Defuzzifier circuits. *The study and development of mixed-signal Fuzzy Controllers architectures targeting the requirements for different applications. *The fabrication and test of full-ended demonstrators. *The partial fabrication and test of a prototype corresponding to a real-time Fuzzy Logic application in the field of Signal Processing.

Fuzzy Logic with Engineering Applications

Fuzzy Logic with Engineering Applications

Author: Timothy J. Ross

Publisher: John Wiley & Sons

ISBN: 9781119235859

Category: Technology & Engineering

Page: 584

View: 723

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The latest update on this popular textbook The importance of concepts and methods based on fuzzy logic and fuzzy set theory has been rapidly growing since the early 1990s and all the indications are that this trend will continue in the foreseeable future. Fuzzy Logic with Engineering Applications, Fourth Edition is a new edition of the popular textbook with 15% of new and updated material. Updates have been made to most of the chapters and each chapter now includes new end-of-chapter problems. Key features: New edition of the popular textbook with 15% of new and updated material. Includes new examples and end-of-chapter problems. Has been made more concise with the removal of out of date material. Covers applications of fuzzy logic to engineering and science. Accompanied by a website hosting a solutions manual and software. The book is essential reading for graduates and senior undergraduate students in civil, chemical, mechanical and electrical engineering as wells as researchers and practitioners working with fuzzy logic in industry.

Analysis and Synthesis of Fuzzy Control Systems

Analysis and Synthesis of Fuzzy Control Systems

Author: Gang Feng

Publisher: CRC Press

ISBN: 9781420092653

Category: Technology & Engineering

Page: 299

View: 891

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Fuzzy logic control (FLC) has proven to be a popular control methodology for many complex systems in industry, and is often used with great success as an alternative to conventional control techniques. However, because it is fundamentally model free, conventional FLC suffers from a lack of tools for systematic stability analysis and controller design. To address this problem, many model-based fuzzy control approaches have been developed, with the fuzzy dynamic model or the Takagi and Sugeno (T–S) fuzzy model-based approaches receiving the greatest attention. Analysis and Synthesis of Fuzzy Control Systems: A Model-Based Approach offers a unique reference devoted to the systematic analysis and synthesis of model-based fuzzy control systems. After giving a brief review of the varieties of FLC, including the T–S fuzzy model-based control, it fully explains the fundamental concepts of fuzzy sets, fuzzy logic, and fuzzy systems. This enables the book to be self-contained and provides a basis for later chapters, which cover: T–S fuzzy modeling and identification via nonlinear models or data Stability analysis of T–S fuzzy systems Stabilization controller synthesis as well as robust H∞ and observer and output feedback controller synthesis Robust controller synthesis of uncertain T–S fuzzy systems Time-delay T–S fuzzy systems Fuzzy model predictive control Robust fuzzy filtering Adaptive control of T–S fuzzy systems A reference for scientists and engineers in systems and control, the book also serves the needs of graduate students exploring fuzzy logic control. It readily demonstrates that conventional control technology and fuzzy logic control can be elegantly combined and further developed so that disadvantages of conventional FLC can be avoided and the horizon of conventional control technology greatly extended. Many chapters feature application simulation examples and practical numerical examples based on MATLAB®.

Fuzzy Logic and Soft Computing

Fuzzy Logic and Soft Computing

Author: Bernadette Bouchon-Meunier

Publisher: World Scientific

ISBN: 9789814500081

Category: Computers

Page: 508

View: 418

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Soft computing is a new, emerging discipline rooted in a group of technologies that aim to exploit the tolerance for imprecision and uncertainty in achieving solutions to complex problems. The principal components of soft computing are fuzzy logic, neurocomputing, genetic algorithms and probabilistic reasoning. This volume is a collection of up-to-date articles giving a snapshot of the current state of the field. It covers the whole expanse, from theoretical foundations to applications. The contributors are among the world leaders in the field. Contents:Fuzzy Logic and Genetic AlgorithmsLearningFuzzy and Hybrid SystemsDecision and Aggregation TechniquesFuzzy Logic in DatabasesFoundations of Fuzzy LogicApplications of Fuzzy Sets Readership: Researchers and computer scientists. keywords: