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Deep neuro-fuzzy systems with Python : with case studies and applications from the industry /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York :
Apress,
[2020]
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Singh, Himanshu, |e author. | |
| 245 | 1 | 0 | |a Deep neuro-fuzzy systems with Python : |b with case studies and applications from the industry / |c Himanshu Singh, Yunis Ahmad Lone. |
| 264 | 1 | |a New York : |b Apress, |c [2020] | |
| 264 | 4 | |c ©2020 | |
| 300 | |a 1 online resource (xv, 260 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 588 | 0 | |a Online resource; title from PDF title page (SpringerLink, viewed December 16, 2019). | |
| 500 | |a Includes index. | ||
| 505 | 0 | |a 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. | |
| 520 | |a 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. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Neural networks (Computer science) | |
| 650 | 0 | |a Fuzzy systems. | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 2 | |a Neural Networks, Computer | |
| 650 | 6 | |a Réseaux neuronaux (Informatique) | |
| 650 | 6 | |a Systèmes flous. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 7 | |a Fuzzy systems |2 fast | |
| 650 | 7 | |a Neural networks (Computer science) |2 fast | |
| 650 | 7 | |a Python (Computer program language) |2 fast | |
| 700 | 1 | |a Lone, Yunis Ahmad, |e author. | |
| 776 | 0 | 8 | |i Print version: |z 9781484253601 |
| 776 | 0 | 8 | |i Print version: |z 9781484253625 |
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