Deep Neuro-Fuzzy Systems with Python: With Case Studies and Applications from the Industry - Softcover

Singh, Himanshu; Lone, Yunis Ahmad

Softcover

ISBN 10: 1484253604 ISBN 13: 9781484253601

Publisher: Apress, 2019

Synopsis

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.

About this title

About the Author

Himanshu Singh is currently a Consultant to Artificial Intelligence for ADP Inc. with over 5 years of experience in the AI industry, primarily in Computer Vision and Natural Language Processing. Himanshu has authored three books on Machine Learning. He has an MBA from Narsee Monjee Institute of Management Studies, and a postgraduate diploma in Applied Statistics. 

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.

From the Back Cover

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. 

Edition details

• Publisher: Apress
• Publication date: 2019
• ISBN 10: 1484253604
• ISBN 13: 9781484253601
• Binding: Paperback
• Language: English
• Edition number: 1
• Number of pages: 275