摘要
为了提高模糊神经网络(FNN)的收敛速度和泛化能力,提出一种基于混合梯度下降算法(HG)的模糊神经网络(HG-FNN).HG-FNN通过设计FNN参数调整过程的自适应学习率,利用链式法则获取FNN参数学习过程的梯度,在实现FNN参数自校正的同时,给出HG-FNN的收敛性证明,保证HG-FNN的收敛速度和泛化能力.最后,将所设计的HG-FNN应用于非线性系统建模与污水处理过程关键水质参数预测,实验比较结果显示,HG-FNN不仅具有较快的收敛速度,而且具有较好的泛化能力.
To improve the convergence speed and generalization ability of the fuzzy neural network（FNN）, a fuzzy neural network, based on the hybrid gradient（HG） descent algorithm, is proposed in this paper. This HG-FNN can obtain the adaptive learning rate of the parameter adjustment process. Then, the chain rule is used to calculate the gradient descent of the learning process to adjust the parameters of FNN. Meanwhile, the convergence proof of HG-FNN is given in details to ensure the convergence speed and the precision of FNN. Finally, the proposed HG-FNN is used to model the nonlinear systems and predict the effluent qualities of wastewater treatment process. The results show that the proposed HG-FNN owns faster convergence speed, as well as with suitable generalization ability than other FNNs.
出处
《控制与决策》
EI
CSCD
北大核心
2017年第9期1635-1641,共7页
Control and Decision
基金
国家自然科学基金项目(61533002
61622301)
北京市自然科学基金项目(4172005)
科技部水专项(2017ZX07104)
关键词
模糊神经网络
混合梯度
自适应学习率
非线性系统建模
fuzzy neural network
hybrid gradient
adaptive learning rate
nonlinear systems modeling
