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Deep Neuro-Fuzzy Systems with Python - With Case Studies and Applications from the Industry (Paperback, 1st ed.)
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Deep Neuro-Fuzzy Systems with Python - With Case Studies and Applications from the Industry (Paperback, 1st ed.)
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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.
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