改进的RBF神经网络模式分类方法应用研究

经典的Bayes分类方法一般需要事先对样本的分布特性作出假设,当假设模型与样本实际分布情况不相符时,就难以得到较高的分类精度。当处理同类别多区域样本分布问题,例如变标签问题时,距离判别、Fisher判别、k－近邻分类、分段线性分类等统计分析方法遇到困难。双螺旋问题不仅使统计方法受到挑战,更使人们对一般前向多层神经网络的能力提出疑问。本文提出了改进的RBF神经网络结构、核函数个数、位置与宽度优化算法。该算法的计算复杂性与一般前向三层LBF网络所用的误差反传算大致相同。核函数生成既考虑了训练集样本自身的类别因素,又考虑了错分样本与邻近类别的关系。一个核函数的最终保留与否根据其对提高测试集分类正确率的贡献大小来决定。同时实验验证了两层LBF网络对提高改进的RBF网络分类正确率的极端重要性。大量应用实例表明,与前向三层RBF网络和前向三层LBF网络相比,该IRBF网络具有收敛速度快、分类精度高、易于得到最小结构、在学习过程中不易陷入局部极小点等优点,有利于实现实时分析。

Applications of Pattern Classification Methods Based on Improved RBF Neural Ne tworks

Generally speaking, the classical Bayes classification methods must hypothesize what distribution a random variable be subject to before analyzing. It is impossible to get a high correct rate if the selected model is not agreement with the true one. The statistic approaches, for example, distance, Fisher, k nearest neighbor, wise linear classifiers, fail to solve multi regional distributions such as the alternate table problems. Not only does the two spiral problem give a challenge to the statistic methods again, but also brings a doubt about the abilities of the general feedforward multi layered LBF neural networks. This paper presents an adaptive algorithm of optimally determining the structures, number, positions and widths of kernel functions of the improved radial basis function(IRBF) neural networks. The algorithm has approximately computational complexity comparing with the back propagation fellow used in the general feedforward three layered LBF networks. Whether or not a kernel function comes into being depends on the relationships between some misclassification patterns and their neighbor classes. The extreme importance of the two layered LBF networks is testified by many experiments. Whether or not a kernel be finally continued to have is determined by its contribution to improve the classification correct rate of the test set. A lot of applications show this kind of IRBF networks have advantages over the feedforward three layered RBF and LBF ones at such aspects as convergence rates and classification precision, achievements of optimal structures, capabilities of getting rid of local points. This kind of networks are able to work well in a real time way.