Title : 
Generating Weighted Fuzzy Production Rules using Neural Networks
         
        
            Author : 
Fan, Tie-gang ; Wang, Shu-tian ; Chen, Jun-Min
         
        
            Author_Institution : 
Fac. of Math. & Comput. Sci., Hebei Univ., Baoding
         
        
        
        
        
        
            Abstract : 
Weighted fuzzy production rules enhance the knowledge representation power of rule. This paper proposes a way to generate weighted fuzzy production rules using neural networks. First the knowledge in the data is transformed into neural network. Through analysis of the weights of the neural network, a matrix of importance index is constructed. Then weighted fuzzy production rules are extracted from the neural network. In order to reflect the knowledge implied in the neural network accurately, a corresponding reasoning algorithm is constructed. The effective of the approach is demonstrated by the experiment
         
        
            Keywords : 
data mining; fuzzy set theory; inference mechanisms; knowledge representation; learning (artificial intelligence); matrix algebra; neural nets; knowledge representation; matrix algebra; neural network; reasoning algorithm; rule extraction; weighted fuzzy production rule generation; Artificial neural networks; Cybernetics; Data mining; Fuzzy neural networks; Fuzzy reasoning; Fuzzy sets; Knowledge representation; Machine learning; Machine learning algorithms; Neural networks; Production; Rules extraction; neural networks; reasoning algorithm; weighted fuzzy production rules;
         
        
        
        
            Conference_Titel : 
Machine Learning and Cybernetics, 2006 International Conference on
         
        
            Conference_Location : 
Dalian, China
         
        
            Print_ISBN : 
1-4244-0061-9
         
        
        
            DOI : 
10.1109/ICMLC.2006.258366