This book introduces the development process, structural theories and research areas of intelligent control; explains the knowledge representations, searching and reasoning mechanisms as the fundamental techniques of intelligent control; studies the theoretical principles and architectures of various intelligent control systems; analyzes the paradigms of representative applications of intelligent control; and discusses the research and development trends of the intelligent control.From the general point of view, this book possesses the following features: updated research results both in theory and application that reflect the latest advances in intelligent control; closed connection between theory and practice that enables readers to use the principles to their case studies and practical projects; and comprehensive materials that helps readers in understanding and learning.

What is intelligence? Are truly intelligent machines a practical reality? If so, can they work in harmony with human beings and improve the quality of our lives? How are they designed, built, and controlled? The fact is that machines with brains are no longer the stuff of science fiction. Research focused on developing smarter, more flexible machines and new applications continues at a remarkable pace, yet for many people-even engineers-these and other questions linger. Intelligent Machines: Myths and Realities explores the technological, industrial, economic, social, and research issues related to intelligent machines. Nine chapters-authored by highly distinguished international authorities-take you from the fundamentals and general aspects of intelligent machines through current techniques and research, and finally to their practical aspects and applications. Written for both technical and nontechnical readers, Intelligent Machines presents complex issues in simple, qualitative terms, yet discusses important theoretical aspects, industrial applications, and design issues where they are appropriate. The result is an intriguing exploration of this revolutionary technology, its design, uses, limitations, and future prospects. Features

Computational Intelligence (CI) is a recently emerging area in fundamental and applied research, exploiting a number of advanced information processing technologies that mainly embody neural networks, fuzzy logic and evolutionary computation. With a major concern to exploiting the tolerance for imperfection, uncertainty, and partial truth to achieve tractability, robustness and low solution cost, it becomes evident that composing methods of CI should be working concurrently rather than separately. It is this conviction that research on the synergism of CI paradigms has experienced significant growth in the last decade with some areas nearing maturity while many others remaining unresolved. This book systematically summarizes the latest findings and sheds light on the respective fields that might lead to future breakthroughs.

This English edition monograph is developed and updated from China's best-selling, and award-winning, book on Artificial Intelligence (AI). It covers the foundations as well as the latest developments of AI in a comprehensive and systematic manner. It is a valuable guide for students and researchers on artificial intelligence.A wide range of topics in AI are covered in this book with four distinct features. First of all, the book comprises a comprehensive system, covering the core technology of AI, including the basic theories and techniques of 'traditional' artificial intelligence, and the basic principles and methods of computational intelligence. Secondly, the book focuses on innovation, covering advanced learning methods for machine learning and deep learning techniques and other artificial intelligence that have been widely used in recent years. Thirdly, the theory and practice of the book are highly integrated. There are theories, techniques and methods, as well as many application examples, which will help readers to understand the artificial intelligence theory and its application development. Fourthly, the content structure of the book is quite characteristic, consisting of three parts: (i) knowledge-based artificial intelligence, (ii) data-based artificial intelligence, and (iii) artificial intelligence applications.It is closely related to the core elements of artificial intelligence, namely knowledge, data, algorithms, and computing powers. This reflects the authors' deep understanding of the artificial intelligence discipline.

Computational Intelligence (CI) is a recently emerging area in fundamental and applied research, exploiting a number of advanced information processing technologies that mainly embody neural networks, fuzzy logic and evolutionary computation. With a major concern to exploiting the tolerance for imperfection, uncertainty, and partial truth to achieve tractability, robustness and low solution cost, it becomes evident that composing methods of CI should be working concurrently rather than separately. It is this conviction that research on the synergism of CI paradigms has experienced significant growth in the last decade with some areas nearing maturity while many others remaining unresolved. This book systematically summarizes the latest findings and sheds light on the respective fields that might lead to future breakthroughs. Contents: A Quest for Granular Computing and Logic Processing (W Pedrycz); Abstraction and Linguistic Analysis of Conventional Numerical Dynamic Systems (F-Y Wang); Slicing: A Distributed Learning Approach (S A Eschrich & L O Hall); Marginal Learning Algorithms in Statistical Machine Learning (Q Tao & J Wang); Constraint Handling in Genetic Algorithm for Optimization (G G Yen); Hybrid PSO-EA Algorithm for Training Feedforward and Recurrent Neural Networks for Challenging Problems (X Cai et al.); Modular Wavelet-Fuzzy Networks (Y Lin & F-Y Wang); Ant Colony Algorithms: The State-of-the-Art (J Zhang et al.); Motif Discoveries in DNA and Protein Sequences Using Self-Organizing Neural Networks (D Liu & X Xiong); Computational Complexities of Combinatorial Problems with Applications to Reverse Engineering of Biological Networks (P Berman et al.); Advances in Fingerprint Recognition Algorithms with Application (J Tian et al.); Adaptation and Predictive Control Observed in Neuromuscular Control Systems (J He); Robust Adaptive Approximation Based Backstepping via Localized Adaptive Bounding (Y Zhao & J A Farrell); Dynamically Connected Fuzzy Single Input Rule Modules and Application to Underactuated Systems (J Yi et al.). Readership: Researchers, graduate and senior level undergraduate students in electrical & electronic engineering, computer engineering, neural networks, fuzzy logic and artificial intelligence.

The book is motivated by the pivotal issue: what is the performance limit of active control and energy harvesting? It aims to develop systematic design methodologies with a “visualization technique” where the performance limit can be readily determined solely based on visual inspections. Modern technological systems have evolved toward high speed, heavy load, lightweight, flexible operation and extreme conditions, as demonstrated in aerospace, marine, transportation and manufacturing industries. The associated vibration and noise issues have become such problematic that they may significantly confine the performance of the systems, to say the discomfort at least. Through the geometric representation of the performance specifications, fundamental issues such as (1) the existence of feasible controllers; (2) the optimality of controllers; (3) the performance limit of controllers; (4) compromisability among the performance specifications; (5) the synthesis of controllers; and (6) the influence of constraints on optimal solutions can all be resolved within the proposed framework. The state of the art is thus refined with a new approach complementary to those optimization-based routines, where extra effort would have to be exercised to disclose the compromisability of performance specifications. The proposed book will result in a new design methodology—performance limit-oriented active control. It was initiated by the author with the project “Active Control for Performance Limit” (ACPL). A series of fundamental results are obtained and will be disseminated in this book. The results are verified through extensive numerical demonstrations and are expected to provide useful guidance for practical engineering in the vibration and noise industry and research.

Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples. Key features: Covers all the aspects of fuzzy, neural and evolutionary approaches with worked out examples, MATLAB® exercises and applications in each chapter Presents the synergies of technologies of computational intelligence such as evolutionary fuzzy neural fuzzy and evolutionary neural systems Considers real world problems in the domain of systems modelling, control and optimization Contains a foreword written by Lotfi Zadeh Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing is an ideal text for final year undergraduate, postgraduate and research students in electrical, control, computer, industrial and manufacturing engineering.

This book attempts to couple control engineering with modern developments in science, through the concept of entropy. Such disciplines as intelligent machines, economics, manufacturing, environmental systems, waste etc. can be favorably affected and their performance can be improved or their catastrophic effects minimized. Entropy is used as the unifying measure of the various, seemingly disjoint, disciplines to represent the cost of producing work that improves the standard of living, both in engineering and in science. Modeling is done through probabilistic methods, thus establishing the irreversibility of the processes involved. This is in accordance with the modern view of science. In addition, the behavior of control for an arbitrary but fixed controller away from the optimal (equilibrium) has been obtained, the analytic expression of which should lead to chaotic solutions. The control activity is explained, based on the principle that control is making a system do what we want it to do. This helps to relate control theory with the sciences.

One critical barrier leading to successful implementation of flexible manufacturing and related automated systems is the ever-increasing complexity of their modeling, analysis, simulation, and control. Research and development over the last three decades has provided new theory and graphical tools based on Petri nets and related concepts for the design of such systems. The purpose of this book is to introduce a set of Petri-net-based tools and methods to address a variety of problems associated with the design and implementation of flexible manufacturing systems (FMSs), with several implementation examples.There are three ways this book will directly benefit readers. First, the book will allow engineers and managers who are responsible for the design and implementation of modern manufacturing systems to evaluate Petri nets for applications in their work. Second, it will provide sufficient breadth and depth to allow development of Petri-net-based industrial applications. Third, it will allow the basic Petri net material to be taught to industrial practitioners, students, and academic researchers much more efficiently. This will foster further research and applications of Petri nets in aiding the successful implementation of advanced manufacturing systems.

Introducton; Methology of knowledge representation; General inference principles; Hierarchical control systems; Expert control systems; Fuzzy control systems; Neurocontrol systems; Learning control systems; Intelligente control systems in application; Prospectives of intelligente control; References; Bibliography; Subject index.