Analysis and Design of Intelligent Systems Using Soft Computing Techniques [electronic resource] /
Melin, Patricia.editor.
Castillo, Oscar.editor.
Ramírez, Eduardo G.editor.
Pedrycz, Witold.editor.
This book comprises a selection of papers from IFSA 2007 on 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 intelligent systems that combine several SC techniques are needed due to the complexity and high dimensionality of real-world problems. Hybrid intelligent systems can have different architectures, which have an impact on the efficiency and accuracy of these systems, for this reason it is very important 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, intelligent control, or other application areas.
Description based upon print version of record.
Includes bibliographical references and index.
Fuzzy Logic as the Logic of Natural Languages -- I: Type-2 Fuzzy Logic: Theory and Applications -- II: Fuzzy Clustering: Theory and Applications -- III: Intelligent Identification and Control -- IV: Time Series Prediction -- V: Pattern Recognition -- VI: Evolutionary Computation -- VII: Fuzzy Modeling -- VIII: Intelligent Manufacturing and Scheduling -- IX: Intelligent Agents -- X: Neural Networks Theory -- XI: Robotics -- XII: Fuzzy Logic Applications.
This book comprises a selection of papers from IFSA 2007 on 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 intelligent systems that combine several SC techniques are needed due to the complexity and high dimensionality of real-world problems. Hybrid intelligent systems can have different architectures, which have an impact on the efficiency and accuracy of these systems, for this reason it is very important 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, intelligent control, or other application areas.
Applied mathematics.
Engineering mathematics.
Artificial intelligence.
Mathematical and Computational Engineering.
Artificial Intelligence.
Applications of Mathematics.

Advances in Intelligent and Soft Computing