A new method of Data Mining based on fuzzy neural network

The fuzzy neural network technology is one of the hot topics of Data Mining. According to the Max Similarity Rule, this paper sets forth the cross entropy theory with formulae deduction in detail and a new activation function. Compare with the BP algorithm (error back propagation), which based on the error square sum rule and Sigmoid or Hyperbolical function, the classify algorithm based on the cross entropy theory and the new activation function can speed up the learning process and at same time without worry about to put it into the non-convergence state or lose in local small point. The new activation function not only has the value range from 0 to 1, but can also tune up the learning speed by adjusting its slope. So it can improve the algorithm´s dynamic performance and make the processing of the FNN get into convergence as soon as possible, which can improve the algorithm´s efficiency. In order to identify the user in the Web mining, an idea, which uses the biometric recognition technology, is put forward. At the same time, a method based on the Hidden Markov Model to build an iris identification system is proposed either. The robust of iris matching can be achieved by only depending on the orientation field of the iris and less sensitive to the noise and the distortions of the iris image than the conventional approaches in which need many iris details. This method is more efficient by sampling the pretreatment processes.