径向基过程神经元网络及其应用研究

提出了一种径向基过程神经元网络,该网络模型为3层前向结构,由输入层、径向基过程神经元隐层和输出层组成.输入层到隐层的变换是非线性的,隐层到输出层的变换是线性的.隐层神经元完成对过程式输入信息的模式匹配和对时间的聚合运算,输出层对输入模式作出响应.在输入空间中引入函数正交基,将输入函数在正交基下展开,利用基函数的正交性,简化聚合运算过程.给出了相应的学习算法,并以旋转机械故障诊断问题为例验证了模型和方法的有效性.

Research and applications of radial basis process neural networks

A radial basis process neural networks model was proposed, which is a kind of three-layer forward structure constituted of input layer, radial basis function hidden layer and output layer. The transformation from input layer to hidden layer is nonlinear and that from hidden layer to output layer is linear. The neurons of hidden layer perform the pattern matching of process input information and aggregation operation of time and respond to the input patterns. Through inducting function orthogonal basis into input space, input function can be expanded under the orthogonal basis and aggregation operation process can be simplified by using the orthogonality of basis function. The corresponding learning algorithms were given and the effectiveness of this method was proved by rotation machinery fault diagnoses.