Hybrid Soft Computing - Abraham, Ajith
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Soft computing is a well-established paradigm consisting of artificial neural networks, fuzzy inference systems, approximate reasoning and derivative free optimization techniques such as evolutionary computation etc. Several adaptive hybrid soft computing architectures have in recent years been developed for solving complicated real world problems. The hybridization aims at overcoming limitations of individual techniques through fusion of different techniques. Many of these approaches use the combination of different knowledge representation schemes, decision-making models, learning strategies…mehr

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Produktbeschreibung
Soft computing is a well-established paradigm consisting of artificial neural networks, fuzzy inference systems, approximate reasoning and derivative free optimization techniques such as evolutionary computation etc. Several adaptive hybrid soft computing architectures have in recent years been developed for solving complicated real world problems. The hybridization aims at overcoming limitations of individual techniques through fusion of different techniques. Many of these approaches use the combination of different knowledge representation schemes, decision-making models, learning strategies and optimization techniques to solve a computational task. This book investigates the optimization of artificial neural networks and fuzzy inference systems using a combination of evolutionary algorithms and local search techniques.
Autorenporträt
Dr. Abraham is the Director of Machine Intelligence Research Labs (MIR Labs), a Not-for-Profit Scientific Network for Innovation and Research Excellence connecting Industry and Academia. The Network with Head quarters in Seattle, USA has currently more than 1,000 scientific members from over 100 countries.