<title>Modified sampling frequency-sensitive network based on evolutionary programming for pattern clustering</title>

Abstract

In this paper, a modified evolutionary programming-based sampling frequency-sensitive network is proposed for pattern clustering. Many researchers study the neural networks for pattern clustering recently. The Kohenen Feature Maps (KFM) network and BP neural network are examples. But there are some problems with these models. For example, the network has a complicated structure and large amount of neurons. The neural network usually gets in unexpected local optimal solution. The results of pattern classification often correlate with the initial conditions. The fixed neural network structure is the major disadvantage for pattern clustering where the optimal number of patterns is unknown. Willie Chang presented a sampling frequency-sensitive network in 1997. The model ahs the advantages of simple structure and simple learning rules. But it also has fixed the architecture. The algorithm is porposed in this paper which effectively uses the sampling frequency-sensitive network and the powerful parallel search optimization tool EP (evolutionary programming) which is presented by Fogel,D.B.. The modified Hubert index and cluster splitting and merging algorithm are used in network architecture evolution. The rule of minimum mean square error is used to get the optimal parameters. The proposed method has an advantage of that the optical solution of neural network architecture and parameters can be get simultaneously. So the classification network can get the optimal number of clusters and the optimal vector quantization. The results of the experiment are given to prove that the neural network architecture can be changed for real world problems and get the optimal results.