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Springer Fuzzy Logic Hybrid Extensions Neural Optimization - AI Book

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Description

Fuzzy Logic Hybrid Extensions of Neural and Optimization Algorithms: Theory and Applications explores the intersection of fuzzy logic systems with advanced neural network and optimization methodologies. This volume, part of the Studies in Computational Intelligence series (volume 940), offers comprehensive coverage of hybrid approaches that combine fuzzy logic reasoning with machine learning techniques. The book provides both theoretical foundations and practical applications, making it suitable for researchers, academics, and professionals in artificial intelligence, machine learning, and computational intelligence. It addresses how fuzzy systems enhance neural networks and optimization algorithms through uncertainty handling and approximate reasoning capabilities. Published by Springer, a leading academic publisher, this work is essential for anyone seeking deep understanding of hybrid intelligent systems that leverage multiple computational paradigms to solve complex real-world problems.

Key Features

Part of the Studies in Computational Intelligence series (volume 940) published by Springer, ensuring academic credibility and peer-reviewed content.

Covers hybrid integration of fuzzy logic with neural networks and optimization algorithms for enhanced intelligent system design.

Provides both theoretical foundations and practical applications relevant to AI and machine learning professionals.

Explores advanced techniques for handling uncertainty and approximate reasoning in computational intelligence systems.

Suitable for researchers and academics working in artificial intelligence, fuzzy systems, and hybrid intelligent computing methods.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
29 March 2022
Listed Since
27 February 2022

Barcode

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