间歇过程的全局收敛性神经模糊模型

Fuzzy Neural Model with Global Convergence for Batch Process

为了克服难以获得间歇过程的精确数学模型这一难题,本文采用一种具有非线性模糊规则后件的模糊神经网络建立间歇过程的模型,在此基础上提出一种基于Lyapunov方法的全局收敛性参数学习算法,并给出了严格的数学证明.本文提出的算法具有较好的非线性逼近和参数自学习能力,为间歇生产过程的建模提供了一条新途径.最后,将提出的神经模糊模型用于一个典型间歇过程的建模研究中,仿真结果表明了该算法的有效性,体现了模型潜在的实用价值.

To overcome the difficulty that an accurate mathematic model is hard to obtain, a batch process model based on fuzzy neural network with nonlinear fuzzy rule consequence is proposed. Then a parameters learning algorithm based on Lyapunov method with global convergence is also presented with rigorous proof. The proposed algorithm possesses the high approximation and better self-learning ability, thus it provides a new way for the modeling of batch processes. Lastly, to verify the efficiency of the proposed algorithm, it is applied to a benchmark batch process. The simulation results show the efficiency and potential practical value of the proposed model.