双进双出磨煤机模糊神经网络建模与仿真

Fuzzy neural network modeling and simulation of BBD ball mill

针对一般经验公式或者回归分析方法难以得到准确的双进双出磨煤机运行模型的问题,在对双进双出磨煤机结构及运行机理深入分析、得出相关参数的基础上,提出了一种基于Takagi-Sugeno结构的双进双出磨煤机模糊神经网络建模方法,并应用粒子群算法对网络的各初始权值和阈值进行优化.在M atlab环境下,与传统非线性方程建立模型的方法相比较,本方法仿真效果更优越,同时也证明了模型的有效性.

It is difficult to obtain the accurate operating model of BBD ball mill using the general empirical formula or regression analysis method. Through analyzing the structure, operation mechanism and relevant parameters of the BBD ball mill, a fuzzy neural network modeling method based on the Takagi-Sugeno structure was proposed for the BBD ball mill. The initial weight and threshold of the network were optimized using the particle swarm optimization algorithm. In the Matlab environment, this method exhibits the better simulation effect compared with the traditional modeling method by the non-linear equations. The simulation results prove the validity of the present model.