自适应子波神经网络数据挖掘方法

分析了人工神经网络所依据的生物学基础,探索改进与完善网络模型的途径,将子波与巳有的神经网络模型相结合,提出一种基于自适应子波神经网络的数据控制方法,并依此来构造数据挖掘过程的机器学习机制,以求提高对问题的处理能力,数据挖掘的仿真实例表明,与一般的人工神经网络相比,用自适应子波神经网络进行数据挖掘不仅是有效的,而且也是可行的。

Use of the self-adaptation wavelet neural network for data mining

Data mining refers to extracting or "mining" knowledge from large amounts of data. By performing data mining, interesting knowledge, regularities, or high level in formation can be extracted from databases and viewed or browsed from different angles. The discovered knowledge can be applied to decision marking, process control information management, query processing, and so on. Therefore, data mining is considered as one of the most important frontiers in database systems. Based on the existing artificial neural network, a novel learning algorithm for the self adaptation wavelet neural network for data mining is proposed, which makes it easy for a user to directly utilize the characteristic information of a pending problem and to simplify the original structure through adjusting the activation function with the prior knowledge. The theoretical analysis and the simulating test for the data mining problem show that, compared with the artificial neural network, the use of the self adaptation wavelet neural network for data mining is not only effective but also feasible.