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.
