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Deep neuro-fuzzy systems with python : with case studies and applications from the industry / By Himanshu Singh and Yunis Ahmad Lone.

By: Contributor(s): Material type: TextTextPublication details: New York : Apress , 2020.Edition: 1st edDescription: xv, 260p.; 23 cmISBN:
  • 9781484253618
Subject(s): Genre/Form: Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 006.32 1 SINH
LOC classification:
  • Q334-342
Online resources:
Contents:
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.
In: Springer eBooksSummary: 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. .
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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.

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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. .

Mode of access: World Wide Web.

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