基于MSADE-IT2FNN模型的软测量建模方法及应用

Soft sensor modeling method based on MSADE-IT2FNN and its applications

针对复杂工业过程某些关键参数无法有效、实时在线检测的问题,提出一种基于多策略、自适应差分进化算法(MSADE)优化的区间二型模糊神经网络(IT2FNN)软测量建模方法。首先,为了解决差分进化算法采用单一策略、固定缩放因子和交叉概率导致后期搜索能力不足的问题,提出一种多策略、自适应的差分进化算法(MSADE),该算法利用IT2FNN模型的均方根误差(RMSE)作为适应度函数,通过搜索不同规则数下的RMSE值,从而确定IT2FNN的结构(规则数)和初始参数;然后,IT2FNN模型的参数利用梯度下降法进行学习。最后,将所提模型应用到Mackey-Glass混沌时间序列的预测和酿酒过程淀粉利用率的软测量建模问题中,仿真结果验证了提出方法的有效性和优越性。

Aiming at the problem that some key parameters of complex industrial processes unable to be detected effectively and online in real-time,a soft sensor modeling method based on Interval Type-2 Fuzzy Neural Network(IT2FNN)optimized by Multi-Strategy and Adaptive Differential Evolution algorithm(MSADE)was proposed.To solve the problem that the Differential Evolution(DE)algorithm used a single strategy,fixed scaling factor and crossover probability that leaded to the lack of late search ability,an MSADE algorithm was proposed.In this algorithm,the Root Mean Square Error(RMSE)of the IT2FNN model was used as fitness function to determine the structure(number of rules)and initial parameters of the IT2FNN by searching for the RMSE values under different numbers of rules.Then,the parameters of the IT2FNN model were learned by the gradient descent method.The proposed model was applied to the prediction of Mackey-Glass chaotic time series and the soft sensor of starch utilization rate in wine making process.The simulation results verified the effectiveness and superiority of the proposed method.