scholarly journals Genetic Algorithm Based Improved Sub-Optimal Model Reduction in Nyquist Plane for Optimal Tuning Rule Extraction of PID and PIlambdaDi Controllers via Genetic Programming

2011 ◽  
Author(s):  
Saptarshi Das ◽  
Indranil Pan ◽  
Shantanu Das ◽  
Amitava Gupta
Keyword(s):  
Genetic Algorithm ◽  
Genetic Programming ◽  
Model Reduction ◽  
Rule Extraction ◽  
Optimal Model ◽  
Optimal Tuning

scholarly journals H2-optimal model reduction of MIMO systems

2008 ◽  
Vol 21 (12) ◽  
pp. 1267-1273 ◽  
Author(s):  
P. Van Dooren ◽  
K.A. Gallivan ◽  
P.-A. Absil
Keyword(s):  
Model Reduction ◽  
Mimo Systems ◽  
Optimal Model

A genetic algorithm-based rule extraction system

2012 ◽  
Vol 12 (1) ◽  
pp. 238-254 ◽  
Author(s):  
Bikash Kanti Sarkar ◽  
Shib Sankar Sana ◽  
Kripasindhu Chaudhuri
Keyword(s):  
Genetic Algorithm ◽  
Rule Extraction ◽  
Extraction System

A New Approach to the Extraction of ANN Rules and to Their Generalization Capacity Through GP

2004 ◽  
Vol 16 (7) ◽  
pp. 1483-1523 ◽  
Author(s):  
Juan R. Rabuñal ◽  
Julián Dorado ◽  
Alejandro Pazos ◽  
Javier Pereira ◽  
Daniel Rivero
Keyword(s):  
Genetic Programming ◽  
Rule Extraction ◽  
Human Beings ◽  
Activation Functions ◽  
New Approach ◽  
Previous Algorithm ◽  
Internal Distribution

Various techniques for the extraction of ANN rules have been used, but most of them have focused on certain types of networks and their training. There are very few methods that deal with ANN rule extraction as systems that are independent of their architecture, training, and internal distribution of weights, connections, and activation functions. This article proposes a methodology for the extraction of ANN rules, regardless of their architecture, and based on genetic programming. The strategy is based on the previous algorithm and aims at achieving the generalization capacity that is characteristic of ANNs by means of symbolic rules that are understandable to human beings.

Sign in / Sign up

Export Citation Format

Share Document