Deep Neuro-Fuzzy Systems with Python

Deep Neuro-Fuzzy Systems with Python Front Cover
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260 pages

Book Description

Gain insight into fuzzy logic and neural , and how the integration between the two models makes intelligent in the current world. This book simplifies the implementation of fuzzy logic and neural 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 . 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.

What You’ll Learn

  • Understand fuzzy logic, membership , fuzzy relations, and fuzzy inference
  • Review neural networks, back propagation, and optimization
  • Work with different architectures such as Takagi-Sugeno model, Hybrid model, genetic algorithms, and approximations
  • Apply Python implementations of deep neuro fuzzy system

Who This book Is For 

Data scientists and software engineers with a basic understanding of who want to expand into the hybrid applications of deep learning and fuzzy logic.

Book Details

  • Title: Deep Neuro-Fuzzy Systems with Python
  • Author: ,
  • Length: 260 pages
  • Edition: 1st ed.
  • Language: English
  • Publisher:
  • Publication Date: 2019-12-24
  • ISBN-10: 1484253604
  • ISBN-13: 9781484253601
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