An improved elman neural network controller based on quasi‐ARX neural network for nonlinear systems
An improved elman neural network controller based on quasi‐ARX neural network for nonlinear systems
复制标题
基于拟ARX神经网络的非线性系统改进elman神经网络控制器
DOI：
10.1002/tee.21998
复制
发表时间：
2014
影响因子：
1
通讯作者：
Jinglu Hu
中科院分区：
文献类型：
--
作者：
I. Sutrisno;Mohammad Abu Jami'in;Jinglu Hu
针对非线性系统，提出了一种基于粒子群优化的改进Elman神经网络（IENN）控制器。该控制器由准ARX神经网络（QARXNN）预测模型和切换机制组成。采用切换机制保证了预测模型的良好运行。基于IENN，采用粒子群算法的BP学习算法设计主控制器。利用粒子群算法对BP过程中的学习率进行调整，提高学习能力。利用李雅普诺夫稳定性定理研究了控制器的自适应学习率。通过数值仿真验证了所提控制器的性能。将该方法与模糊切换和0/1切换方法进行了比较，证明了该方法在稳定性、准确性和鲁棒性方面的有效性。©2014日本电气工程师学会。约翰·威利父子出版公司。
An improved Elman neural network (IENN) controller with particle swarm optimization (PSO) is presented for nonlinear systems. The proposed controller is composed of a quasi‐ARX neural network (QARXNN) prediction model and a switching mechanism. The switching mechanism is used to guarantee that the prediction model works well. The primary controller is designed based on IENN using the backpropagation (BP) learning algorithm with PSO. PSO is used to adjust the learning rates in the BP process for improving the learning capability. The adaptive learning rates of the controller are investigated via the Lyapunov stability theorem. The proposed controller performance is verified through numerical simulation. The method is compared with the fuzzy switching and 0/1 switching methods to show its effectiveness in terms of stability, accuracy, and robustness. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.