中文摘要：

针对数据挖掘算法中常用的机器学习型算法进行研究。机器学习型算法特色是运用了人工智能技术,能在大量样本集训练和学习后自动找出运算需要的参数和模式。以机器学习型算法中的人工神经网络为例研究数据挖掘技术,针对学习速度慢、抗干扰能力弱以及容易陷入局部最小值等缺点和传统的遗传算法存在算法早熟以及局部寻优能力弱等问题,提出一种通过改进常规遗传算法的染色体结构和遗传算子,并且通过引入自适应交叉和变异概率来对BP神经网络结构参数进行优化的改进型遗传优化BP神经网络模型。最后通过煤矿空压机故障诊断系统这一实例来研究改进型算法的数据挖掘技术的性能。研究结果表明,改进后的算法建立的诊断模型相比常规神经网络的诊断模型诊断准确率更好,诊断效率更快。

英文摘要：

The machine learning algorithm commonly used in data mining algorithm is studied in this paper. AIT（artificial intelligence technology）is adopted in machine learning algorithm,which can automatically find out the parameters and modes required by operation after a large number of sample set training and learning. The artificial neural network in machine learning algorithm is taken as an example to research the data mining technology. Since the traditional genetic algorithm has the shortcomings of prematurity and weak local optimizing capacity,the improved genetic optimization BP neural network model is proposed by improving the chromosome structure and genetic operator,and by introducing adaptive crossover and mutation probability to optimize neural network structure parameters and solve the problems of slow learning speed,weak anti-jamming capability,and easily falling into local minimum value. Finally,the performance of the improved algorithm is studied by using the fault diagnosis system of air compressor. The research results show that the improved diagnostic model,compared with the conventional neural network diagnosis model,has better diagnostic accuracy and higher diagnostic efficiency.