Comparing with general methods to acquire fuzzy if-then rules,such as C-means and the subtractive clustering algorithm, a fuzzy neural network model for identifying 11 kinds of mineral waters by its taste signals is proposed. In the model,a classification rule extracting algorithm based on discretization methods in rough sets is developed to extract fewer but robust classification rules,which are ease to be translated to fuzzy if-then rules to construct a fuzzy neural network system.
This thesis mainly analyses and compares several construct algorithms for concept lattice, and study decision rule acquisition based on concept lattice, meanwhile some significant results are abtained.
A rule extraction tech nique is then applied in order to extract explicit knowledge from the trained neural networks and represent it in the form of fuzzy if-then rules.
Our proposed network adapts from an initial analog phase, which has good training behavior, to a discrete phase for automatic rule extraction.
First, Radial Basis Function Neural Networks (RBFNN) learning techniques are explored, as is usual in the literature, since the local nature of this paradigm makes it a suitable platform for performing rule extraction.
By using support vectors from a learned SVM it is possible in our approach to use any standard Radial Basis Function (RBF) learning technique for the rule extraction, whilst avoiding the overlapping between classes problem.
Linguistic Rule Extraction From a Simplified RBF Neural Network
With the new definition of approximation to an object set and the concept of attribute value pair, rough-sets-based methodology for certain rule acquisition in an incomplete information system is developed.
Rough sets theory has been employed for diagnostic rule acquisition of rotating machinery with consideration of conflicting objects of decision table.
Rule acquisition is guided by the constraints of physiological process models represented in the system.
An optional activity in this phase is Business rule acquisition.
An important source for the rule acquisition was the old system.