Melin P., Castillo O. (eds.) Soft Computing Applications in Optimization, Control, and Recognition

Springer, 2013. — 341 p.We describe in this book the application of soft computing techniques for intelligent control, pattern recognition, and optimization of complex problems. Soft Computing (SC) consists of several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful hybrid intelligent systems. The book is organized in four main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of nature-inspired optimization methods and their applications, which are basically papers that propose new models and concepts, which can be the basis for achieving intelligent optimization in diverse areas of application. The second part contains papers with the main theme of hybrid intelligent systems for achieving intelligent control, which are basically papers using nature-inspired techniques, like evolutionary algorithms, fuzzy logic and neural networks, for the optimal design of intelligent controllers in diverse areas of application. The third part contains papers with the theme of pattern recognition based on SC techniques, which basically consider the proposal of new methods and their applications to solve complex pattern recognition problems. The fourth part contains papers that deal with the application of intelligent optimization techniques in real world problems. The fifth part contains papers with the theme of new theoretical concepts and methods in SC, which are papers considering soft computing methods for applications related to diverse areas, such as natural language processing, clustering and optimization.Part I: Optimization Methods and Applications
Optimization of Type-2 and Type-1 Fuzzy Tracking Controllers for an Autonomous Mobile Robot under Perturbed Torques by Means of a Chemical Optimization Paradigm
A Genetic Algorithm for the Problem of Minimal Brauer Chains for Large Exponents
Cellular Processing Algorithms
Part II: Soft Computing in Intelligent Control Applications
Hierarchical Genetic Optimization of the Fuzzy Integrator for Navigation of a Mobile Robot
Particle Swarm Optimization for Multi-objective Control Design Using AT2-FLC in FPGA Device
Genetic Optimization of Modular Type-1 Fuzzy Controllers for Complex Control Problems
Part III: Soft Computing in Pattern Recognition Applications
Multi-Objective Hierarchical Genetic Algorithm for Modular Granular Neural Network Optimization
Type-2 Fuzzy Weight Adjustment for Backpropagation in Prediction Time Series and Pattern Recognition
Brain Computer Interface Development Based on Recurrent Neural Networks and ANFIS Systems
Part IV: Soft Computing: Theory and New Models
An Analysis of the Relationship between the Size of the Clusters and the Principle of Justifiable Granularity in Clustering Algorithms
Type-2 Fuzzy Logic Grammars in Language Evolution
Methodology of Design: A Novel Generic Approach Applied to the Course Timetabling Problem
High-Performance Architecture for the Modified NSGA-II