Abstract Wang-Mendel algorithm is commonly used as a classic method to generate fuzzy rule base. But rules with low confidence are usually extracted when noise appears in the sample data set,while its efficiency also often drops fast when the scale of sample data increases. To solve those problems,two methods,cooperation relationship and self-organizing mapping (SOM) neural network,are introduced. Cooperation relationship among sample data improves the accuracy of rules and approximation ability to the original model. On the other hand,SOM can well preprocess sample data for denoising and reduce its scale through a self-adaptive learning procedure of weights network. Then an improved Wang-Mendel algorithm is proposed based on cooperation relationship degree of sample data and SOM. The experimental results,including trigonometric function approximation and artificial driving simulation of a train operation control system,show its completeness,robustness and operating efficiency.
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