Novel online training algorithm based on immune principles for RBF neural network and its application in the thermal process wide range modeling

作者：Lin Jin Xing^*; Shen Jiong; Li Yi Guo

来源：Proceedings of the CSEE, 2006, 26(9): 14-19.

• Immunity principle
• Non linear dynamical system
• On line learning
• Radial basis function neural network
• System identification
• Thermal process

A novel online training algorithm for RBF neural network based on immune principles is presented to overcome the weaknesses of the traditional neural network training algorithm in the long time-period identification of non-linear dynamical system. With the analysis of the comparability between the learning of RBF neural network and immune system, this algorithm uses immune memory, clone selection and cell languish mechanisms to adjust the nodes in hidden layer dynamically, and determine their core function's center and width. As a result, the RBF neural network could have the function of on-line learning and memory of new samples. The improved neural network is then used for modeling the main steam pressure of 300 MW thermal power unit with varying operating region, and the results show that the new algorithm can not only simplify the structure of neural network but also adapt the time-varying character of dynamical system effectively.

• 出版日期2006
• 单位东南大学