Petridis V, Paterakis E, Kehagias A
Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, GR-540 06 Thessaliniki, Greece.
IEEE Trans Neural Netw. 1998;9(5):862-76. doi: 10.1109/72.712158.
We introduce a hybrid neural-genetic multimodel parameter estimation algorithm. The algorithm is applied to structured system identification of nonlinear dynamical systems. The main components of the algorithm are 1) a recurrent incremental credit assignment (ICRA) neural network, which computes a credit function for each member of a generation of models and 2) a genetic algorithm which uses the credit functions as selection probabilities for producing new generations of models. The neural network and genetic algorithm combination is applied to the task of finding the parameter values which minimize the total square output error: the credit function reflects the closeness of each model's output to the true system output and the genetic algorithm searches the parameter space by a divide-and-conquer technique. The algorithm is evaluated by numerical simulations of parameter estimation for a planar robotic manipulator and a waste water treatment plant.
我们介绍了一种混合神经遗传多模型参数估计算法。该算法应用于非线性动力系统的结构系统识别。该算法的主要组成部分为：1）递归增量信用分配（ICRA）神经网络，它为一代模型中的每个成员计算一个信用函数；2）遗传算法，它使用信用函数作为生成新一代模型的选择概率。神经网络和遗传算法的组合应用于寻找使总平方输出误差最小化的参数值的任务：信用函数反映每个模型的输出与真实系统输出的接近程度，遗传算法通过分治技术搜索参数空间。通过对平面机器人操纵器和污水处理厂进行参数估计的数值模拟对该算法进行了评估。
