基于人工免疫RBF神经网络的时间序列预测方法研究

研究了一种基于免疫识别原理的径向基函数神经网络学习算法,该算法将所识别的数据作为抗原,抗体为抗原的压缩映射并作为神经网络模型的隐层中心,采用最小二乘法确定权值,提高了RBF神经网络收敛速度和精度.将人工免疫RBF神经网络应用于时间序列预测中,实例仿真结果证明了算法的有效性和可行性,为时间序列预测提供了一种新途径.

Time series prediction based on artificial immune RBF neural network

A Radial Basis Function(RBF) neural network learning algorithm based on immune recognition principle is studied.In the algorithm,the input data are regarded as antigens and the compression mapping of antigens as antibodies,i.e.,the hidden layer centers.The weights of the output layer are determined by adopting the least square algorithm,which can improve convergence speed and precision of the RBF neural network.A new time series prediction method based on artificial immune RBF neural network is also presented,and its application is discussed.The simulation experiment indicates that this method is effective in time series prediction.