Overview
- Explains deep neuro-fuzzy systems with applications and mathematical details
- Implementations of all the applications using Python
- Covers the recent applications of neuro fuzzy inference systems in industry
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Table of contents (7 chapters)
Keywords
About this book
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.
What You’ll Learn
- Understand fuzzy logic, membership functions, 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 Machine Learning who want to expand into the hybrid applications of deep learning and fuzzy logic.
Authors and Affiliations
About the authors
Yunis Ahmad Lone has over 22 years of experience in the IT industry, has been involved with Machine Learning for 10 years. Currently, Yunis is a PhD researcher at Trinity College, Dublin, Ireland. Yunis completed his Bachelors and Masters both from BITS Pilani, and worked on various leadership positions in MNCs like Tata Consultancy Services, Deloitte, and Fidelity Investments.
Bibliographic Information
Book Title: Deep Neuro-Fuzzy Systems with Python
Book Subtitle: With Case Studies and Applications from the Industry
Authors: Himanshu Singh, Yunis Ahmad Lone
DOI: https://doi.org/10.1007/978-1-4842-5361-8
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Himanshu Singh, Yunis Ahmad Lone 2020
Softcover ISBN: 978-1-4842-5360-1Published: 01 December 2019
eBook ISBN: 978-1-4842-5361-8Published: 30 November 2019
Edition Number: 1
Number of Pages: XV, 260
Number of Illustrations: 143 b/w illustrations
Topics: Artificial Intelligence, Python, Open Source