Rule Extraction from Compact Pareto-optimal Neural Networks

2008 ◽  
pp. 71-90
Author(s):  
Yaochu Jin ◽  
Bernhard Sendhoff ◽  
Edgar Körner
Keyword(s):  
Neural Networks ◽  
Rule Extraction ◽  
Pareto Optimal

A search technique for rule extraction from trained neural networks

1999 ◽  
Vol 20 (3) ◽  
pp. 273-280 ◽  
Author(s):  
R. Krishnan ◽  
G. Sivakumar ◽  
P. Bhattacharya
Keyword(s):  
Neural Networks ◽  
Rule Extraction ◽  
Search Technique

Global Approximation: Performance Comparison of Different Methods, With an Application to Road Vehicle System Engineering

1999 ◽  
Author(s):  
Massimiliano Gobbi ◽  
Giampiero Mastinu
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Linear Interpolation ◽  
Performance Comparison ◽  
Pareto Optimal ◽  
Approximation Methods ◽  
Global Approximation ◽  
Road Vehicle ◽  
Pareto Optimal Set ◽  
Design Variables

Abstract Optimisation of complex mechanical systems has often to be performed by resorting to global approximation. In usual global approximation practice, the original mathematical model is substituted by another mathematical model which gives approximately the same relationships between design variables and performance indexes. This is made to ensure much faster simulations which are of crucial importance to find optimal solutions. In this paper the performances of four global approximation methods (Neural Networks, Kriging, Quadratic Approximation, Linear Interpolation) are compared, with reference to an actual optimal design problem. The performances of a road vehicle suspension system are optimised by varying the system’s design variables. The Pareto-optimal set is derived symbolically. The performances of the different approximation methods taken into consideration are assessed by comparing the numerical- and the analytical-Pareto-optimal results. It is found that Neural Networks obtain the best accuracy.

scholarly journals Hybrid Neural-global Minimization Method of Logical Rule Extraction

1999 ◽  
Vol 3 (5) ◽  
pp. 348-356 ◽  
Author(s):  
Wlodzislaw Duch ◽  
◽  
Rafal Adamczak ◽  
KrzysAof Grabczewski ◽  
Grzegorz Zal
Keyword(s):  
Neural Networks ◽  
Hybrid Approach ◽  
Real Life ◽  
Rule Extraction ◽  
Global Minimization ◽  
Minimization Method ◽  
Life Problems ◽  
Multilayered Perceptron ◽  
Logical Functions ◽  
Logical Rules

Methodology of extraction of optimal sets of logical rules using neural networks and global minimization procedures has been developed. Initial rules are extracted using density estimation neural networks with rectangular functions or multilayered perceptron (MLP) networks trained with constrained backpropagation algorithm, transforming MLPs into simpler networks performing logical functions. A constructive algorithm called CMLP2LN is proposed, in which rules of increasing specificity are generated consecutively by adding more nodes to the network. Neural rule extraction is followed by optimization of rules using global minimization techniques. Estimation of confidence of various sets of rules is discussed. The hybrid approach to rule extraction has been applied to a number of benchmark and real life problems with very good results.

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