Hybrid Intelligent Systems: Analysis and Design

ISBN-13: 9783540374190 / Angielski / Twarda / 2006 / 433 str.

Oscar Castillo; Patricia Melin; Janusz Kacprzyk

Opis

We describe in this book, new methods for analysis and design of hybrid intelligent systems using soft computing techniques. Soft Computing (SC) consists of several computing paradigms, including fuzzy logic, neural networks, and genetic algorithms, which can be used to produce powerful hybrid intelligent systems for solving problems in pattern recognition, time series prediction, intelligent control, robotics and automation. Hybrid int- ligent systems that combine several SC techniques are needed due to the complexity and high dimensionality of real-world problems. Hybrid int- ligent systems can have different architectures, which have an impact on the efficiency and accuracy of these systems, for this reason it is very - portant to optimize architecture design. The architectures can combine, in different ways, neural networks, fuzzy logic and genetic algorithms, to achieve the ultimate goal of pattern recognition, time series prediction, - telligent control, or other application areas. This book is intended to be a major reference for scientists and en- neers interested in applying new computational and mathematical tools to design hybrid intelligent systems. This book can also be used as a textbook or major reference for graduate courses like the following: soft computing, intelligent pattern recognition, computer vision, applied artificial intel- gence, and similar ones. We consider that this book can also be used to get novel ideas for new lines of research, or to continue the lines of research proposed by the authors of the book.

Spis treści

Theory.- Hybridization Schemes in Architectures of Computational Intelligence.- ChapBoltzmann Machines Learning Using High Order Decimation.- Evolutionary Optimization of a Wiener Model.- Synchronization of Chaotic Neural Networks: A Generalized Hamiltonian Systems Approach.- Mediative Fuzzy Logic: A Novel Approach for Handling Contradictory Knowledge.- Intelligent Control Applications.- Direct and Indirect Adaptive Neural Control of Nonlinear Systems.- Simple Tuning of Fuzzy Controllers.- From Type-1 to Type-2 Fuzzy Logic Control: A Stability and Robustness Study.- A Comparative Study of Controllers Using Type-2 and Type-1 Fuzzy Logic.- Evolutionary Computing for Topology Optimization of Type-2 Fuzzy Controllers.- Robotic Applications.- Decision Trees and CBR for the Navigation System of a CNN-based Autonomous Robot.- Intelligent Agents in Distributed Fault Tolerant Systems.- Genetic Path Planning with Fuzzy Logic Adaptation for Rovers Traversing Rough Terrain.- Chattering Attenuation Using Linear-in-the-Parameter Neural Nets in Variable Structure Control of Robot Manipulators with Friction.- Tracking Control for a Unicycle Mobile Robot Using a Fuzzy Logic Controller.- Intelligent Control and Planning of Autonomous Algorithms Mobile Robots Using Fuzzy Logic and Genetic.- Pattern Recognition Applications.- The Role of Neural Networks in the Interpretation of Antique Handwritten Documents.- Reasoning Object Recognition Using Fuzzy Inferential.- The Fuzzy Sugeno Integral as a Decision Operator in the Recognition of Images with Modular Neural Networks.- Modular Neural Networks and Fuzzy Sugeno Integral for Pattern Recognition: The Case of Human Face and Fingerprint.- Time Series and Diagnosis.- Optimal Training for Associative Memories: Application to Fault Diagnosis in Fossil Electric Power Plants.- Acceleration Output Prediction of Buildings Using a Polynomial Artificial Neural Network.- Time Series Forecasting of Tomato Prices and Processing in Parallel in Mexico Using Modular Neural Networks.- Modular Neural Networks with Fuzzy Sugeno Integration Applied to Time Series Prediction.- On Linguistic Summaries of Time Series Using a Fuzzy Quantifier Based Aggregation via the Sugeno Integral.

Blurb

The objective of this edited volume is to offer a general view at the recent conceptual developments of Soft Computing (SC) regarded as a general methodology supporting the design of hybrid systems along with their diversified applications to modeling, simulation and control of non-linear dynamical systems. As of now, SC methodologies embrace neural networks, fuzzy logic, genetic algorithms and chaos theory. Each of these methodologies exhibits well delineated advantages and disadvantages. Interestingly, they have been found useful in solving a broad range of problems. However, many real-world complex problems require a prudent, carefully orchestrated integration of several of these methodologies to fully achieve the required efficiency, accuracy, and interpretability of the solutions. In this edited volume, an overview of SC methodologies, and their applications to modeling, simulation and control, will be given in an introductory paper by the Editors. Then, detailed methods for integrating the different SC methodologies in solving real-world problems will be given in the papers by the other authors in the book. The edited volume will cover a wide spectrum of applications including areas such as: robotic dynamic systems, non-linear plants, manufacturing systems, and time series prediction.