神经网络规则优化建模与应用

Neural network rule optimization modeling and its application

神经网络应用于复杂系统时存在隐含层节点确定和参数随机选择的困难,对此研究探索了一种规则优化建模方法。首先,应用粗决策树耦合算法实现增量式的动态规则提取;其次,基于获取的动态规则计算最简规则数,作为确定网络隐含层节点的依据,实现网络规则建模;再次,优化网络初始参数,规避局部极小问题并提高模型训练速度和精度;最后,将优化模型应用于空气质量预报中,性能测试和对比分析结果显示,该模型收敛速度快且误差控制在4%以内,学习速度和预报精度明显优于传统模型。该研究成果实现了动态增量模式下的规则模型构建与优化,为动态数据处理提供了一种新的研究思路。

It is difficult to determine hidden layer nodes and select parameters randomly when neural network is applied to complex systems.To solve this problem,a new rule modeling method is explored.Firstly,the incremental dynamic rule extraction is realized by the rough set&decision tree coupling algorithm.Secondly,the obtained dynamic rules are used to calculate the minimum rule number,which can be used as the basis to determine the hidden layer nodes and realize network rule modeling.Thirdly,the initial network parameters are optimized to avoid local minimum and improve the training speed and precision of the model.Finally,the optimization model is applied to the air quality forecast.Performance test and comparative analysis show that the model has a fast convergence speed and controls the error within 4%,and its learning speed and prediction accuracy are obviously better than the traditional model.The research results realize the rule model construction and optimization under dynamic incremental mode and provide a new research idea for dynamic data processing.