人工免疫的神经网络预报方法及其应用

An artificial immunity-based predictive method for neural networks and its applications

针对RBF(radial basis function)神经网络在预测铁水含硅量中出现的预测精度低,收敛速度慢的问题,提出了一种基于免疫识别原理的径向基函数神经网络的学习算法。该算法利用人工免疫原理确定高斯基函数的中心和宽度参数,同时将所识别的数据作为抗原,抗体作为抗原的压缩映射并作为神经网络的隐层中心,利用递推最小二乘法(recursion least square,RLS)确定连接权值,提高了RBF神经网络的收敛速度和精度。应用该模型于某大型钢铁厂高炉铁水硅含量预报的实例中,实验结果表明,该模型具有更高的预测精度和更短的训练时间。

A radial basis function （RBF） neural network learning algorithm based on immune recognition was proposed to improve the low forecast precision and the slow convergence speed of such networks. In the algorithm, artificial immunity was used to determine the center and width parameters of the Gauss basis function. The recognized data were regarded as antigens and the compression mapping of antigens were taken as antibodies, i. e., the centers of the hidden layer. The recursion least square algorithm （RLs） was employed to determine the output layer weights. The algorithm improved the convergence speed and precision of the RBF neural networks. The model was applied to the blast furnace of a large iron and steel company. The results show that the model has forecast precision far superior to existing models and requires less training time than they do.