The Research Of Artificial Neural Networks In Date Mining

The two problems of data mining using neural networks are the long training time and being understood and explicit representation of the acquired knowledge. The rule extraction of neural networks is discussed, which is an effective method to avoid shortcoming of being "black boxes". Techniques based on decompositional and input-output mapping approaches are studied, their fundamental concepts and pedagogical rule extraction are compared in detail.The BP algorithm is kind of algorithm which is in use widely in multilayer neural network, because the BP algorithm is a kind of gradient descended searching algorithm in essence, it has weaknesses such as inefficient, slow convergent speed and easy getting into local minimum, insurable to find global extreme value point for multi-modal and non-differential function in larger searching zone, which restrict neural network's application in all fields. So from the principle, analysis the reasons of BP algorithm to be dropped in local minimization and propose a global optima strategy.The global optima strategy is discussed from two aspects, one of improved strategy based on model, and another based on algorithm. A comparative study on some typical improved models of BP networks, which based on Gradient descent and numerical optimization are proposed. Experiments results show that the modified BP arithmetic not only has shorted study time, high efficiency, but also meet with the error goal, improve the generalization capability. So it can averted from getting into local minimum in some degree and achieve global optimization.