Computational Intelligence Paradigms: Theory & Applications using MATLAB Review

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Computational Intelligence Paradigms: Theory & Applications using MATLAB ReviewWhile this book's technical content (e.g. neural net discussion) is quite good, I can't believe that the editor allowed such poor language to pass through the editing process. This book is probably the best example of the kind of linguistic degradation that George Orwell mentioned in "Politics and the English Language." Virtually every sentence contains pseudo-sophisticated words that appear to have been picked at random from a thesaurus. Synonyms usually convey a meaning similar, but not identical to, the original, but the authors never grasped this basic observation. Also, the authors seem not to understand the context in which a given word usually appears, thereby making pretty comical errors that distract the reader. Staying focused on the technical content while processing the linguistic gibberish is quite a challenge.Computational Intelligence Paradigms: Theory & Applications using MATLAB OverviewOffering a wide range of programming examples implemented in MATLAB, Computational Intelligence Paradigms: Theory and Applications Using MATLAB presents theoretical concepts and a general framework for computational intelligence (CI) approaches, including artificial neural networks, fuzzy systems, evolutionary computation, genetic algorithms and programming, and swarm intelligence. It covers numerous intelligent computing methodologies and algorithms used in CI research. The book first focuses on neural networks, including common artificial neural networks; neural networks based on data classification, data association, and data conceptualization; and real-world applications of neural networks. It then discusses fuzzy sets, fuzzy rules, applications of fuzzy systems, and different types of fused neuro-fuzzy systems, before providing MATLAB illustrations of ANFIS, classification and regression trees, fuzzy c-means clustering algorithms, fuzzy ART map, and Takagi-Sugeno inference systems. The authors also describe the history, advantages, and disadvantages of evolutionary computation and include solved MATLAB programs to illustrate the implementation of evolutionary computation in various problems. After exploring the operators and parameters of genetic algorithms, they cover the steps and MATLAB routines of genetic programming. The final chapter introduces swarm intelligence and its applications, particle swarm optimization, and ant colony optimization.Full of worked examples and end-of-chapter questions, this comprehensive book explains how to use MATLAB to implement CI techniques for the solution of biological problems. It will help readers with their work on evolution dynamics, self-organization, natural and artificial morphogenesis, emergent collective behaviors, swarm intelligence, evolutionary strategies, genetic programming, and the evolution of social behaviors.

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