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Books in Fuzzy control neural systems and genetic algorithms

  • Modeling and Control of Dynamic Spatially Distributed Systems

    Pharmaceutical Processes
    • 1st Edition
    • November 8, 2024
    • Yizhi Wang + 1 more
    • English
    Modeling and Control of Dynamic Spatially Distributed Systems: Pharmaceutical Processes provides a balanced approach to help readers to get started quickly in the field of biochemical pharmaceuticals. From a theoretical perspective, dynamic spatially distributed systems are introduced to address their industrial applications. After identifying problems, the book provides readers with modeling and control system design techniques via a novel fuzzy set (class of objects with a continuum of grades of membership, to describe the grade of the object belonging to this fuzzy set) and intelligent computation methods.From an application perspective, the book provides a thorough understanding of Good Manufacture Practices (GMP) and the importance of identification, modelling, and intelligent control of such systems, reducing the test-and-error cost, and the R&D design time cycle of original drug development.

  • Analysis and Synthesis of Singular Systems

    • 1st Edition
    • November 4, 2020
    • Zhiguang Feng + 4 more
    • English
    Analysis and Synthesis of Singular Systems provides a base for further theoretical research and a design guide for engineering applications of singular systems. The book presents recent advances in analysis and synthesis problems, including state-feedback control, static output feedback control, filtering, dissipative control, H∞ control, reliable control, sliding mode control and fuzzy control for linear singular systems and nonlinear singular systems. Less conservative and fresh novel techniques, combined with the linear matrix inequality (LMI) technique, the slack matrix method, and the reciprocally convex combination approach are applied to singular systems. This book will be of interest to academic researchers, postgraduate and undergraduate students working in control theory and singular systems.

  • Bio-inspired Algorithms for Engineering

    • 1st Edition
    • January 30, 2018
    • Nancy Arana-Daniel + 2 more
    • English
    Bio-inspired Algorithms for Engineering builds a bridge between the proposed bio-inspired algorithms developed in the past few decades and their applications in real-life problems, not only in an academic context, but also in the real world. The book proposes novel algorithms to solve real-life, complex problems, combining well-known bio-inspired algorithms with new concepts, including both rigorous analyses and unique applications. It covers both theoretical and practical methodologies, allowing readers to learn more about the implementation of bio-inspired algorithms. This book is a useful resource for both academic and industrial engineers working on artificial intelligence, robotics, machine learning, vision, classification, pattern recognition, identification and control.

  • Fuzzy Neural Networks for Real Time Control Applications

    Concepts, Modeling and Algorithms for Fast Learning
    • 1st Edition
    • September 17, 2015
    • Erdal Kayacan + 1 more
    • English
    AN INDISPENSABLE RESOURCE FOR ALL THOSE WHO DESIGN AND IMPLEMENT TYPE-1 AND TYPE-2 FUZZY NEURAL NETWORKS IN REAL TIME SYSTEMS Delve into the type-2 fuzzy logic systems and become engrossed in the parameter update algorithms for type-1 and type-2 fuzzy neural networks and their stability analysis with this book! Not only does this book stand apart from others in its focus but also in its application-based presentation style. Prepared in a way that can be easily understood by those who are experienced and inexperienced in this field. Readers can benefit from the computer source codes for both identification and control purposes which are given at the end of the book. A clear and an in-depth examination has been made of all the necessary mathematical foundations, type-1 and type-2 fuzzy neural network structures and their learning algorithms as well as their stability analysis. You will find that each chapter is devoted to a different learning algorithm for the tuning of type-1 and type-2 fuzzy neural networks; some of which are: • Gradient descent • Levenberg-Marquardt • Extended Kalman filter In addition to the aforementioned conventional learning methods above, number of novel sliding mode control theory-based learning algorithms, which are simpler and have closed forms, and their stability analysis have been proposed. Furthermore, hybrid methods consisting of particle swarm optimization and sliding mode control theory-based algorithms have also been introduced. The potential readers of this book are expected to be the undergraduate and graduate students, engineers, mathematicians and computer scientists. Not only can this book be used as a reference source for a scientist who is interested in fuzzy neural networks and their real-time implementations but also as a course book of fuzzy neural networks or artificial intelligence in master or doctorate university studies. We hope that this book will serve its main purpose successfully.

  • Genetic Programming

    An Introduction
    • 1st Edition
    • December 1, 1997
    • Wolfgang Banzhaf + 3 more
    • English
    Since the early 1990s, genetic programming (GP)—a discipline whose goal is to enable the automatic generation of computer programs—has emerged as one of the most promising paradigms for fast, productive software development. GP combines biological metaphors gleaned from Darwin's theory of evolution with computer-science approaches drawn from the field of machine learning to create programs that are capable of adapting or recreating themselves for open-ended tasks.This unique introduction to GP provides a detailed overview of the subject and its antecedents, with extensive references to the published and online literature. In addition to explaining the fundamental theory and important algorithms, the text includes practical discussions covering a wealth of potential applications and real-world implementation techniques. Software professionals needing to understand and apply GP concepts will find this book an invaluable practical and theoretical guide.

  • Foundations of Genetic Algorithms 1993 (FOGA 2)

    • 1st Edition
    • Volume 2
    • February 1, 1993
    • FOGA
    • English
    Foundations of Genetic Algorithms, Volume 2 provides insight of theoretical work in genetic algorithms. This book provides a general understanding of a canonical genetic algorithm. Organized into six parts encompassing 19 chapters, this volume begins with an overview of genetic algorithms in the broader adaptive systems context. This text then reviews some results in mathematical genetics that use probability distributions to characterize the effects of recombination on multiple loci in the absence of selection. Other chapters examine the static building block hypothesis (SBBH), which is the underlying assumption used to define deception. This book discusses as well the effect of noise on the quality of convergence of genetic algorithms. The final chapter deals with the primary goal in machine learning and artificial intelligence, which is to dynamically and automatically decompose problems into simpler problems to facilitate their solution. This book is a valuable resource for theorists and genetic algorithm researchers.

  • Foundations of Genetic Algorithms 1991 (FOGA 1)

    • 1st Edition
    • Volume 1
    • July 1, 1991
    • Gregory J.E. Rawlins
    • English
    Foundations of Genetic Algorithms 1991 (FOGA 1) discusses the theoretical foundations of genetic algorithms (GA) and classifier systems. This book compiles research papers on selection and convergence, coding and representation, problem hardness, deception, classifier system design, variation and recombination, parallelization, and population divergence. Other topics include the non-uniform Walsh-schema transform; spurious correlations and premature convergence in genetic algorithms; and variable default hierarchy separation in a classifier system. The grammar-based genetic algorithm; conditions for implicit parallelism; and analysis of multi-point crossover are also elaborated. This text likewise covers the genetic algorithms for real parameter optimization and isomorphisms of genetic algorithms. This publication is a good reference for students and researchers interested in genetic algorithms.