改进粒子群优化Takagi-Sugeno模糊径向基函数神经网络的非线性系统建模

Nonlinear system modeling based on Takagi-Sugeno fuzzy radial basis function neural network optimized by improved particle swarm optimization

针对复杂非线性系统建模的难点问题,提出了一种基于改进的粒子群优化算法(PSO)优化的T-S模糊径向基函数(RBF)神经网络的新型系统建模算法。该算法将T-S模糊模型良好的可解释性及RBF神经网络的自学习能力相结合,构成T-S模糊RBF神经网络用于系统建模,并采用动态调整惯性权重的改进的PSO算法结合递推最小二乘算法实现网络参数的优化调整。首先,利用所提算法进行了非线性多维函数的逼近仿真,仿真结果均方差(MSE)为0.00017,绝对值误差不大于0.04,逼近精度较高;又将该算法用于建立动态流量软测量模型,并进行了相关的实验研究,动态流量测量结果平均绝对误差小于0.15 L/min,相对误差为1.97%,基本满足测量要求,并优于已有算法。上述仿真及实验研究结果表明,所提算法对于复杂非线性系统具有较高的建模精度和良好的自适应性。

For the difficulty of complex non-linear system modeling, a new system modeling algorithm based on the Takagi-Sugeno （T-S） Fuzzy Radial Basis Function （RBF） neural network optimized by improved Particle Swarm Optimization （PSO） algorithm was proposed. In this algorithm, the good interpretability of T-S fuzzy model and the self-learning ability of RBF neural network were combined together to form a T-S fuzzy RBF neural network for system modeling, and the network parameters were optimized by the improved PSO algorithm with dynamic adjustment of the inertia weight combined with recursive least square method. Firstly, the proposed algorithm was used to do the approximation simulation of a non-linear multi-dimensional function, the Mean Square Error （MSE） of the approximation model was 0. 000 17, the absolute error was not greater than O. 04, which shows higher approximation precision; the proposed algorithm was also used to build a dynamic flow soft measurement model and to finish related experimental study, the average absolute error of the dynamic flow measurement results was less than 0. 15 L/min, the relative error is 1.97%, these results meet measurement requirements well and are better than the resuhs of the existing algorithms. The above simulation results and experimental results show that the proposed algorithm is of high modeling precision and good adaptability for complex non-linear system.