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Genetic Algorithms, Fuzzy Systems & Website Classification

This book presents and discusses current research in the study of genetic algorithms, fuzzy systems and website classification. Topics;

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Computational Intelligence Paradigms

neural networks, fuzzy systems, evolutionary computation, genetic algorithms and programming, and swarm intelligence. It covers numerous;

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Computational Intelligence Paradigms

neural networks, fuzzy systems, evolutionary computation, genetic algorithms and programming, and swarm intelligence. It covers numerous;

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Fuzzy Control Systems

controllers. The various paradigms include fuzzy reasoning models, fuzzy neural networks, fuzzy expert systems, and genetic algorithms. The final part;

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Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms

in solving problems, but a definitive theoretical basis does not yet exist for their design. Fusion of Neural Networks, Fuzzy Systems and Genetic;

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Advances in Swarm and Computational Intelligence

separation; swarm interaction behavior; parameters and system optimization; neural networks; evolutionary and genetic algorithms; fuzzy systems;

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Advances in Swarm and Computational Intelligence

separation; swarm interaction behavior; parameters and system optimization; neural networks; evolutionary and genetic algorithms; fuzzy systems;

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Advances in Swarm and Computational Intelligence

separation; swarm interaction behavior; parameters and system optimization; neural networks; evolutionary and genetic algorithms; fuzzy systems;

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New Hybrid Intelligent Systems for Diagnosis and Risk Evaluation of Arterial Hypertension

combined using fuzzy logic. Finally, two genetic algo-rithms are used to perform the optimization of the modular neural networks parameters and;

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Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration

Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration is a handbook for analysts, engineers, and managers involved;

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Genetic Algorithms for Pattern Recognition

of genetic algorithms; and they illustrate applications in artificial neural networks and fuzzy logic. The cross-sectional view of current research;

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Genetic Algorithms for Pattern Recognition

of genetic algorithms; and they illustrate applications in artificial neural networks and fuzzy logic. The cross-sectional view of current research;

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Advances in Swarm Intelligence

from 197 submissions. The papers were organized in topical sections namely: multi-agent systems; swarm robotics; fuzzy logic approaches;

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Classification Analysis of DNA Microarrays

approaches such as fuzzy methods, artificial neural networks, evolutionary-based genetic algorithms, support vector machines, and swarm intelligence;

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Fuzzy and Neural Approaches in Engineering

concepts. It also explores the value of relating genetic algorithms and expert systems to fuzzy and neural technologies.;

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Systems and Control

of dynamics and control. In particular, it presents neural networks, fuzzy systems, and genetic algorithms, and provides a concise introducton to;

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Robust Control Systems with Genetic Algorithms

control. It also explores the application of hybrid approaches: how to enhance genetic algorithms and programming with fuzzy logic to design;

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Robust Control Systems with Genetic Algorithms

control. It also explores the application of hybrid approaches: how to enhance genetic algorithms and programming with fuzzy logic to design;

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Chemometrics

formats, tree based classification and regression, independent component analysis, ensemble methods and neuro-fuzzy systems. It still retains;

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Interval-Valued Methods in Classifications and Decisions

This book describes novel algorithms based on interval-valued fuzzy methods that are expected to improve classification and decision-making;

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Advances in Swarm Intelligence

robotics; hybrid optimization algorithms and applications; fuzzy and swarm approach; clustering and forecast; classification and detection;

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Advances in Swarm Intelligence

robotics; hybrid optimization algorithms and applications; fuzzy and swarm approach; clustering and forecast; classification and detection;

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Algorithms for Regression and Classification

The focus of this dissertation is on robust regression and classification in genetic association studies. In the context of robust;

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Industrial Intelligent Control

as neural networks, fuzzy logic, ruled based, and genetic algorithms. Demonstrates how to develop, design and use intelligent systems to;

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Applied Soft Computing

It explores a variety of modern applications in soft computing, including bioinspired computing, reconfigurable computing, fuzzy logic;

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Soft Computing In Systems And Control Technology

constituents of soft computing are fuzzy-logic-based computing, neurocomputing, and genetic algorithms. Fuzzy logic contributes the capability;

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Multiple Fuzzy Classification Systems

and business applications. Fuzzy classifiers use fuzzy rules and do not require assumptions common to statistical classification. Rough set;

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Einde inhoud

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