Fault Diagnosis and Fault-Tolerant Control Strategies for Non-Linear Systems

Fault Diagnosis and Fault-Tolerant Control Strategies for Non-Linear Systems

This book presents selected fault diagnosis and fault-tolerant control strategies for non-linear systems in a unified framework. In particular, starting from advanced state estimation strategies up to modern soft computing, the discrete-time description of the system is employed Part I of the book presents original research results regarding state estimation and neural networks for robust fault diagnosis. Part II is devoted to the presentation of integrated fault diagnosis and fault-tolerant systems. It starts with a general fault-tolerant control framework, which is then extended by introducing robustness with respect to various uncertainties. Finally, it is shown how to implement the proposed framework for fuzzy systems described by the well-known TakagiSugeno models.

Fault Diagnosis and Fault-Tolerant Control Strategies for Non-Linear Systems

Fuzzy Probabilities New Approach and Applications

Fuzzy Probabilities New Approach and Applications

In probability and statistics we often have to estimate probabilities and parameters in probability distributions using a random sample. Instead of using a point estimate calculated from the data we propose using fuzzy numbers which are constructed from a set of confidence intervals. In probability calculations we apply constrained fuzzy arithmetic because probabilities must add to one. Fuzzy random variables have fuzzy distributions. A fuzzy normal random variable has the normal distribution with fuzzy number mean and variance. Applications are to queuing theory, Markov chains, inventory control, decision theory and reliability theory.

Fuzzy Probabilities New Approach and Applications

Neural and Fuzzy Logic Control of Drives and Power Systems

Neural and Fuzzy Logic Control of Drives and Power Systems

The authors guide readers quickly and concisely through the complex topics of neural networks, fuzzy logic, mathematical modelling of electrical machines, power systems control and VHDL design. Unlike the academic monographs that have previously been published on each of these subjects, this book combines them and is based round case studies of systems analysis, control strategies, design, simulation and implementation. The result is a guide to applied control systems design that will appeal equally to students and professional design engineers. The book can also be used as a unique VHDL design aid, based on real-world power engineering applications.

Neural and Fuzzy Logic Control of Drives and Power Systems

The Fuzzy Systems Handbook

The Fuzzy Systems Handbook

‘The Fuzzy Systems Handbook’ provides an introduction to fuzzy logic, the fast-growing alternative to binary logic that has wide applications from computer science to process control. This handbook leads the reader through the complete process of designing, constructing, implementing, verifying and maintaining a platform-independent fuzzy-system model. It is written in a tutorial style that assumes no background in fuzzy logic on the reader’s part. This pack: includes an IBM DOS diskette, with all the book’s examples implemented in C++ code; provides mathematically straightforward exposition, with emphasis on practical applications; presents case studies on fraud detection, entropy, managed health care and metallurgical analysis. It features a foreword by Lotfi Zadeh, who developed fuzzy logic in the 1960s.

The Fuzzy Systems Handbook

Fuzzy Logic

Fuzzy Logic

‘Fuzzy Logic: A Practical Approach’ provides a complete overview of fuzzy logic and outlines how it can be applied to real-world problems in industry and business. The book includes a disk with interactive tutorial programs to illustrate the concepts and applications of fuzzy logic.

Fuzzy Logic

Applications of Fuzzy Logic in Bioinformatics

Applications of Fuzzy Logic in Bioinformatics

Many biological systems and objects are intrinsically fuzzy as their properties and behaviors contain randomness or uncertainty. In addition, it has been shown that exact or optimal methods have significant limitation in many bioinformatics problems. Fuzzy set theory and fuzzy logic are ideal to describe some biological systems/objects and provide good tools for some bioinformatics problems. This book comprehensively addresses several important bioinformatics topics using fuzzy concepts and approaches, including measurement of ontological similarity, protein structure prediction/analysis, and microarray data analysis. It also reviews other bioinformatics applications using fuzzy techniques.

Applications of Fuzzy Logic in Bioinformatics

Agent-Based Hybrid Intelligent Systems

Agent-Based Hybrid Intelligent Systems

Solving complex problems in real-world contexts, such as financial investment planning or mining large data collections, involves many different sub-tasks, each of which requires different techniques. To deal with such problems, a great diversity of intelligent techniques are available, including traditional techniques like expert systems approaches and soft computing techniques like fuzzy logic, neural networks, or genetic algorithms. These techniques are complementary approaches to intelligent information processing rather than competing ones, and thus better results in problem solving are achieved when these techniques are combined in hybrid intelligent systems. Multi-Agent Systems are ideally suited to model the manifold interactions among the many different components of hybrid intelligent systems.

Agent-Based Hybrid Intelligent Systems

Mastering Algorithms with Perl

Mastering Algorithms with Perl

Many programmers would love to use , for projects that involve heavy lifting, but miss the many traditional algorithms that textbooks teach for other languages. Computer scientists have identified many techniques that a wide range of programs need, such as: Fuzzy pattern matching for text (identify misspellings!), Finding correlations in data, Game-playing algorithms, Predicting phenomena such as Web traffic, Polynomial and spline fitting.,

Using algorithms explained in this book, you too can carry out traditional , tasks in a high-powered, efficient, easy-to-maintain manner with Perl.,

This book assumes a , understanding of Perl syntax and functions, but not necessarily any background in computer science. The authors explain in a readable fashion the reasons for using various classic programming techniques, the kind of applications that use them, and – most important – how to code these algorithms in Perl.

Mastering Algorithms with Perl

Perceptual Computing

Perceptual Computing

This book focuses on the three components of a Perceptual Computer – encoder, CWW engines, and decoder – and then provides detailed applications for each. It uses interval type-2 fuzzy sets (IT2 FSs) and fuzzy logic as the mathematical vehicle for perceptual computing, because such fuzzy sets can model first-order linguistic uncertainties whereas the usual kind of fuzzy sets cannot. Drawing upon the work on subjective judgments that Jerry Mendel and his students completed over the past decade.

Perceptual Computing