東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Deep neuro-fuzzy systems with Python...

Singh, Himanshu.

FindBook

Google Book

Amazon

博客來

Deep neuro-fuzzy systems with Python = with case studies and applications from the industry /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep neuro-fuzzy systems with Python/ by Himanshu Singh, Yunis Ahmad Lone.
其他題名:	with case studies and applications from the industry /
作者:	Singh, Himanshu.
其他作者:	Lone, Yunis Ahmad.
出版者:	Berkeley, CA :Apress : : 2020.,
面頁冊數:	xv, 260 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Neural networks (Computer science) -
電子資源:	https://doi.org/10.1007/978-1-4842-5361-8
ISBN:	9781484253618

Deep neuro-fuzzy systems with Python = with case studies and applications from the industry /
Singh, Himanshu.

Deep neuro-fuzzy systems with Pythonwith case studies and applications from the industry /[electronic resource] :by Himanshu Singh, Yunis Ahmad Lone. - Berkeley, CA :Apress :2020. - xv, 260 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9781484253618

Standard No.: 10.1007/978-1-4842-5361-8doiSubjects--Topical Terms:

532070
Neural networks (Computer science)

LC Class. No.: QA76.87 / .S564 2020

Dewey Class. No.: 006.32

Deep neuro-fuzzy systems with Python = with case studies and applications from the industry /
LDR:02512nmm a2200325 a 4500 001 2214906
003 DE-He213
005 20200324104834.0
006 m d
007 cr nn 008maaau
008 201118s2020 cau s 0 eng d
020 $a 9781484253618 $q (electronic bk.)
020 $a 9781484253601 $q (paper)
024 7 $a 10.1007/978-1-4842-5361-8 $2 doi
035 $a 978-1-4842-5361-8
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.87 $b .S564 2020
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.32 $2 23
090 $a QA76.87 $b .S617 2020
100 1 $a Singh, Himanshu. $3 3385890
245 1 0 $a Deep neuro-fuzzy systems with Python $h [electronic resource] : $b with case studies and applications from the industry / $c by Himanshu Singh, Yunis Ahmad Lone.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2020.
300 $a xv, 260 p. : $b ill., digital ; $c 24 cm.
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.
650 0 $a Neural networks (Computer science) $3 532070
650 0 $a Fuzzy systems. $3 535881
650 0 $a Python (Computer program language) $3 729789
650 1 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Python. $3 3201289
650 2 4 $a Open Source. $3 2210577
700 1 $a Lone, Yunis Ahmad. $3 3445840
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-1-4842-5361-8
950 $a Professional and Applied Computing (Springer-12059)