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Titlebook: Deep Neuro-Fuzzy Systems with Python; With Case Studies an Himanshu Singh,Yunis Ahmad Lone Book 2020 Himanshu Singh, Yunis Ahmad Lone 2020

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發(fā)表于 2025-3-21 19:44:16 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Deep Neuro-Fuzzy Systems with Python
副標(biāo)題With Case Studies an
編輯Himanshu Singh,Yunis Ahmad Lone
視頻videohttp://file.papertrans.cn/265/264650/264650.mp4
概述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 ind
圖書封面Titlebook: Deep Neuro-Fuzzy Systems with Python; With Case Studies an Himanshu Singh,Yunis Ahmad Lone Book 2020 Himanshu Singh, Yunis Ahmad Lone 2020
描述.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’
出版日期Book 2020
關(guān)鍵詞Fuzzy Logic; Python; Neural Networks; Neuro Fuzzy Inference System; Hybrid Modelling; Fuzzy Sets
版次1
doihttps://doi.org/10.1007/978-1-4842-5361-8
isbn_softcover978-1-4842-5360-1
isbn_ebook978-1-4842-5361-8
copyrightHimanshu Singh, Yunis Ahmad Lone 2020
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Introduction to Machine Learning,ic, which means the fuzzification process, defuzzification process, defining memberships, etc. is all done manually. With intelligent systems, it is always better to learn most of the things from the data, rather than hard-coding it directly. This area of Fuzzy Inference Systems is where most of the
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Fuzzy Neural Networks,hat you always get the same kind of inputs. . in neural networks results in networks having Fuzzy Signals, Fuzzy Weights, etc., in which case you are dealing with . This chapter looks at the different architectures of Fuzzy Neural Networks and the components that define them. You will later learn ab
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Advanced Fuzzy Networks,o know some of the core components that are used in building these systems. This chapter starts by discussing the Fuzzy Clustering method. Then it moves on to genetic algorithms and wraps up by reviewing the most commonly used architectures belonging to the domain of advanced Fuzzy Networks.
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Soziale und wirtschaftliche Voraussetzungen can be performed on them. This chapter also includes a basic introduction to membership functions, which are then explained in detail in the next chapter. Wherever required, Python code is provided for execution purposes.
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Soziale und wirtschaftliche Voraussetzungenh define the membership values of each element present in a Fuzzy Set. You learned about the different types of membership functions. Later, you learned about the Fuzzy Rules and reasoning approaches that utilize the concepts of membership functions to give various Fuzzy Solutions.
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