Applying Fuzzy System Theory To Channel Equalization And Economy Early-warning

Since fuzzy system have the characteristics of nonlinear and the capability of describing fuzzy concept and human knowledge, it has had widely and various applications in pattern recognition and many other fields. Due to the reason that channel equalization and economy early-warning could be seemed as kinds of problems of patter recognition, we set the models of pattern recognition based on fuzzy system theory for these two problems.In this paper, a N pseudo recursive fuzzy c-means algorithm (N-PRFCM) is designed. It can cluster data which can't reach at the same time lively. It is used in nonlinear equalization. The algorithm is real–time, quasi-blind and having adaptive cluster centers. Because the models between channel equalization and economy early-warning are not completely the same, we use TSK fuzzy neural net (FNN) to do economy early-warning. TSK FNN which composed of TSK fuzzy system and pi-sigma neural net has the advantages of both neural net and fuzzy system. But, usually, the quantity of economic indexes is great, so the corresponding fuzzy rules and neurons have also a great quantity. It means that we need a big data sample to train TSK FNN. But the time since economy early-warning introduced into China is very short, we don't have the big data sample. So, we introduced another fuzzy method called Nearest Neighborhood Clustering Method with Adaptive Center (ACNN) later in this paper. ACNN is an improvement of Nearest Neighborhood Clustering Method. It's more robust. Based on the algorithm, we designed ACNN fuzzy system and introduced forgetting factor in order to make our system have time variation characteristic.