語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Design of interpretable fuzzy systems
~
Cpalka, Krzysztof.
Design of interpretable fuzzy systems
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Design of interpretable fuzzy systems/ by Krzysztof Cpalka.
作者:
Cpalka, Krzysztof.
出版者:
Cham :Springer International Publishing : : 2017.,
面頁冊數:
xi, 196 p. :ill., digital ; : 24 cm.;
Contained By:
Springer eBooks
標題:
Fuzzy systems. -
電子資源:
http://dx.doi.org/10.1007/978-3-319-52881-6
ISBN:
9783319528816
Design of interpretable fuzzy systems
Cpalka, Krzysztof.
Design of interpretable fuzzy systems
[electronic resource] /by Krzysztof Cpalka. - Cham :Springer International Publishing :2017. - xi, 196 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.6841860-949X ;. - Studies in computational intelligence ;v. 50. .
Preface -- Acknowledgements -- Chapter1: Introduction -- Chapter2: Selected topics in fuzzy systems designing -- Chapter3: Introduction to fuzzy system interpretability -- Chapter4: Improving fuzzy systems interpretability by appropriate selection of their structure -- Chapter5: Interpretability of fuzzy systems designed in the process of gradient learning -- Chapter6: Interpretability of fuzzy systems designed in the process of evolutionary learning -- Chapter7: Case study: interpretability of fuzzy systems applied to nonlinear modelling and control -- Chapter8: Case study: interpretability of fuzzy systems applied to identity verification -- Chapter9: Concluding remarks and future perspectives -- Index.
This book shows that the term "interpretability" goes far beyond the concept of readability of a fuzzy set and fuzzy rules. It focuses on novel and precise operators of aggregation, inference, and defuzzification leading to flexible Mamdani-type and logical-type systems that can achieve the required accuracy using a less complex rule base. The individual chapters describe various aspects of interpretability, including appropriate selection of the structure of a fuzzy system, focusing on improving the interpretability of fuzzy systems designed using both gradient-learning and evolutionary algorithms. It also demonstrates how to eliminate various system components, such as inputs, rules and fuzzy sets, whose reduction does not adversely affect system accuracy. It illustrates the performance of the developed algorithms and methods with commonly used benchmarks. The book provides valuable tools for possible applications in many fields including expert systems, automatic control and robotics.
ISBN: 9783319528816
Standard No.: 10.1007/978-3-319-52881-6doiSubjects--Topical Terms:
528426
Fuzzy systems.
LC Class. No.: QA402
Dewey Class. No.: 511.313
Design of interpretable fuzzy systems
LDR
:02693nam a2200325 a 4500
001
959017
003
DE-He213
005
20170823141008.0
006
m d
007
cr nn 008maaau
008
201118s2017 gw s 0 eng d
020
$a
9783319528816
$q
(electronic bk.)
020
$a
9783319528809
$q
(paper)
024
7
$a
10.1007/978-3-319-52881-6
$2
doi
035
$a
978-3-319-52881-6
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
QA402
072
7
$a
UYQ
$2
bicssc
072
7
$a
COM004000
$2
bisacsh
082
0 4
$a
511.313
$2
23
090
$a
QA402
$b
.C882 2017
100
1
$a
Cpalka, Krzysztof.
$3
1251478
245
1 0
$a
Design of interpretable fuzzy systems
$h
[electronic resource] /
$c
by Krzysztof Cpalka.
260
$a
Cham :
$c
2017.
$b
Springer International Publishing :
$b
Imprint: Springer,
300
$a
xi, 196 p. :
$b
ill., digital ;
$c
24 cm.
490
1
$a
Studies in computational intelligence,
$x
1860-949X ;
$v
v.684
505
0
$a
Preface -- Acknowledgements -- Chapter1: Introduction -- Chapter2: Selected topics in fuzzy systems designing -- Chapter3: Introduction to fuzzy system interpretability -- Chapter4: Improving fuzzy systems interpretability by appropriate selection of their structure -- Chapter5: Interpretability of fuzzy systems designed in the process of gradient learning -- Chapter6: Interpretability of fuzzy systems designed in the process of evolutionary learning -- Chapter7: Case study: interpretability of fuzzy systems applied to nonlinear modelling and control -- Chapter8: Case study: interpretability of fuzzy systems applied to identity verification -- Chapter9: Concluding remarks and future perspectives -- Index.
520
$a
This book shows that the term "interpretability" goes far beyond the concept of readability of a fuzzy set and fuzzy rules. It focuses on novel and precise operators of aggregation, inference, and defuzzification leading to flexible Mamdani-type and logical-type systems that can achieve the required accuracy using a less complex rule base. The individual chapters describe various aspects of interpretability, including appropriate selection of the structure of a fuzzy system, focusing on improving the interpretability of fuzzy systems designed using both gradient-learning and evolutionary algorithms. It also demonstrates how to eliminate various system components, such as inputs, rules and fuzzy sets, whose reduction does not adversely affect system accuracy. It illustrates the performance of the developed algorithms and methods with commonly used benchmarks. The book provides valuable tools for possible applications in many fields including expert systems, automatic control and robotics.
650
0
$a
Fuzzy systems.
$3
528426
650
0
$a
Fuzzy logic.
$3
528464
650
0
$a
Fuzzy sets.
$3
559335
650
1 4
$a
Engineering.
$3
561152
650
2 4
$a
Computational Intelligence.
$3
768837
650
2 4
$a
Artificial Intelligence (incl. Robotics)
$3
593924
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer eBooks
830
0
$a
Studies in computational intelligence ;
$v
v. 50.
$3
720500
$3
770436
856
4 0
$u
http://dx.doi.org/10.1007/978-3-319-52881-6
950
$a
Engineering (Springer-11647)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入