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

ISBN-13: 9781484253601 / Angielski / Miękka / 2019 / 260 str.

Himanshu Singh; Yunis Ahmad Lone

Spis treści

Deep Neuro-Fuzzy Systems with Python

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

O autorze

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.

Blurb

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.