Soft computing based system modeling & control

Meena Tushir

ISBN 10: 365982366X / ISBN 13: 9783659823664

Published by LAP LAMBERT Academic Publishing, 2016

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Several clustering algorithms have been explained in the book including kernel based clustering algorithm. A kernel based clustering incorporates a kernel metric in place of the Euclidean distance used in the objective function. The kernel induced metric maps the data points to a high dimensional feature space, in which the data is more clearly separable, thereby increasing the accuracy of the proposed clustering technique. A fuzzy controller can also be designed using the clustering based approach. Clustering-based rule extraction methods help avoid combinatorial explosion of rules with increasing dimension of the input space. Also, because clustering step provides good initial rule parameter values, the subsequent rule parameter optimization process usually converges quickly and to a good solution.

Seller Inventory # 9783659823664

Bibliographic Details

• Title: Soft computing based system modeling & control
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2016
• Language: English
• ISBN 10: 365982366X
• ISBN 13: 9783659823664
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 6D millimeters 220 millimeters width by 150 millimeters height by 6 millimeters depth
• Weight: 161 grams

About this title

Synopsis

Several clustering algorithms have been explained in the book including kernel based clustering algorithm. A kernel based clustering incorporates a kernel metric in place of the Euclidean distance used in the objective function. The kernel induced metric maps the data points to a high dimensional feature space, in which the data is more clearly separable, thereby increasing the accuracy of the proposed clustering technique. A fuzzy controller can also be designed using the clustering based approach. Clustering-based rule extraction methods help avoid combinatorial explosion of rules with increasing dimension of the input space. Also, because clustering step provides good initial rule parameter values, the subsequent rule parameter optimization process usually converges quickly and to a good solution.

About the Author

Dr Meena Tushir has been working with the Department of Electrical & Electronics Engineering, MSIT, New Delhi, India since 2003 where she is currently holds the position of Associate Professor. Her current research interests include neural networks, fuzzy logic, modelling, identification, and control of non-linear systems.