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Yoüll start by walking through the basics of fuzzy sets and relations, and how each member of the set has its own membership function values. Yoüll 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 yoüll examine the integrations of fuzzy logics and neural networks, the adaptive neuro fuzzy Inference systems, and various approximations related to the same. Yoüll 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 Yoüll 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.
Yoüll start by walking through the basics of fuzzy sets and relations, and how each member of the set has its own membership function values. Yoüll 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 yoüll examine the integrations of fuzzy logics and neural networks, the adaptive neuro fuzzy Inference systems, and various approximations related to the same. Yoüll 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 Yoüll 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.
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
| Medium: | Taschenbuch |
|---|---|
| Inhalt: |
xv
260 S. 143 s/w Illustr. 260 p. 143 illus. |
| ISBN-13: | 9781484253601 |
| ISBN-10: | 1484253604 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: |
Lone, Yunis Ahmad
Singh, Himanshu |
| Auflage: | 1st ed. |
| Hersteller: |
Apress
Apress L.P. |
| Maße: | 235 x 155 x 16 mm |
| Von/Mit: | Yunis Ahmad Lone (u. a.) |
| Erscheinungsdatum: | 01.12.2019 |
| Gewicht: | 0,423 kg |
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
| Medium: | Taschenbuch |
|---|---|
| Inhalt: |
xv
260 S. 143 s/w Illustr. 260 p. 143 illus. |
| ISBN-13: | 9781484253601 |
| ISBN-10: | 1484253604 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: |
Lone, Yunis Ahmad
Singh, Himanshu |
| Auflage: | 1st ed. |
| Hersteller: |
Apress
Apress L.P. |
| Maße: | 235 x 155 x 16 mm |
| Von/Mit: | Yunis Ahmad Lone (u. a.) |
| Erscheinungsdatum: | 01.12.2019 |
| Gewicht: | 0,423 kg |
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