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 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.

深入了解模糊邏輯和神經網絡，以及兩種模型之間的整合如何在當今世界中創造智能系統。本書使用Python簡化了模糊邏輯和神經網絡概念的實現。

您將首先深入了解模糊集合和關係的基礎知識，以及集合中的每個成員都有自己的隸屬函數值。您還將研究已開發的不同架構和模型，以及如何定義規則和推理來實現這些架構。本書還詳細介紹了神經網絡及其相關架構，重點關注神經網絡在訓練過程中可能遇到的各種問題，以及不同的優化方法如何幫助您解決這些問題。

在本書的最後一部分，您將研究模糊邏輯和神經網絡的整合，自適應神經模糊推理系統以及與之相關的各種近似方法。您將回顧不同類型的深度神經模糊分類器、模糊神經元以及神經網絡的自適應學習能力。本書最後對先進的神經模糊模型和應用進行了回顧。

您將學到以下內容：
- 理解模糊邏輯、隸屬函數、模糊關係和模糊推理
- 回顧神經網絡、反向傳播和優化
- 使用不同的架構，如高田-菅野模型、混合模型、遺傳算法和近似方法
- 應用Python實現深度神經模糊系統

本書適合具有基本機器學習理解的數據科學家和軟件工程師，他們希望擴展到深度學習和模糊邏輯的混合應用領域。

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.

Himanshu Singh目前是ADP Inc.的人工智慧顧問，擁有超過5年的人工智慧行業經驗，主要專注於計算機視覺和自然語言處理。Himanshu撰寫了三本關於機器學習的書籍。他擁有Narsee Monjee Institute of Management Studies的MBA學位，以及應用統計學的研究生文憑。

Yunis Ahmad Lone在IT行業擁有超過22年的經驗，從事機器學習已有10年。目前，Yunis是都柏林三一學院的博士研究生。Yunis在BITS Pilani獲得學士和碩士學位，並在塔塔諮詢服務公司、德勤和富達投資等跨國公司擔任過多個領導職位。