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Deep Neuro-Fuzzy Systems with Python = With Case Studies and Applications from the Industry /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Deep Neuro-Fuzzy Systems with Python/ by Himanshu Singh, Yunis Ahmad Lone.
Reminder of title:	With Case Studies and Applications from the Industry /
Author:	Singh, Himanshu.
other author:	Lone, Yunis Ahmad.
Description:	XV, 260 p. 143 illus.online resource. :
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence. -
Online resource:	https://doi.org/10.1007/978-1-4842-5361-8
ISBN:	9781484253618

Deep Neuro-Fuzzy Systems with Python = With Case Studies and Applications from the Industry /
Singh, Himanshu.

Deep Neuro-Fuzzy Systems with PythonWith Case Studies and Applications from the Industry /[electronic resource] :by Himanshu Singh, Yunis Ahmad Lone. - 1st ed. 2020. - XV, 260 p. 143 illus.online resource.

Chapter 1: Introduction to Fuzzy Set Theory -- Chapter 2: Fuzzy Rules and Reasoning -- Chapter 3: Fuzzy Inference Systems -- Chapter 4: Introduction to Machine Learning -- Chapter 5: Artificial Neural Networks -- Chapter 6: Fuzzy Neural Networks -- Chapter 7: Advanced Fuzzy Networks.

Gain insight into fuzzy logic and neural networks, and how the integration between the two models makes intelligent systems in the current world. This book simplifies the implementation of fuzzy logic and neural network concepts using Python. You’ll start by walking through the basics of fuzzy sets and relations, and how each member of the set has its own membership function values. You’ll also look at different architectures and models that have been developed, and how rules and reasoning have been defined to make the architectures possible. The book then provides a closer look at neural networks and related architectures, focusing on the various issues neural networks may encounter during training, and how different optimization methods can help you resolve them. In the last section of the book you’ll examine the integrations of fuzzy logics and neural networks, the adaptive neuro fuzzy Inference systems, and various approximations related to the same. You’ll review different types of deep neuro fuzzy classifiers, fuzzy neurons, and the adaptive learning capability of the neural networks. The book concludes by reviewing advanced neuro fuzzy models and applications. .

ISBN: 9781484253618

Standard No.: 10.1007/978-1-4842-5361-8doiSubjects--Topical Terms:

559380
Artificial intelligence.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

Deep Neuro-Fuzzy Systems with Python = With Case Studies and Applications from the Industry /
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