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Springer Soft Computing for Data Analytics and Control Book

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Description

Explore advanced computational methods with Soft Computing for Data Analytics, Classification Model, and Control from Springer. This volume, part of the Studies in Fuzziness and Soft Computing series (413), provides a comprehensive look at soft computing approaches and their practical applications. The text covers the fundamentals of fuzzy logic implementation for advanced hybrid fuzzy driven optimization methods. Readers will find detailed discussions on various techniques, including Fuzzy Logic, Rough Sets, Neutrosophic Sets, Type-2 Fuzzy logic, Neural Networks, Generative Adversarial Networks, and Evolutionary Computation. These methods are applied across diverse fields such as data analytics, classification models, and control systems. This book serves as a technical resource for understanding how these complex techniques function within modern computing environments.

Key Features

This Springer publication covers soft computing approaches applied to data analytics, classification models, and control systems.

The book explains the basics of fuzzy logic implementation for advanced hybrid fuzzy driven optimization methods.

Technical discussions include Fuzzy Logic, Rough Sets, Neutrosophic Sets, and Type-2 Fuzzy logic.

Content covers advanced topics like Neural Networks, Generative Adversarial Networks, and Evolutionary Computation.

Part of the Studies in Fuzziness and Soft Computing series, volume 413.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
31 January 2022
Listed Since
27 August 2020

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