Modeling of Nonlinear System with Diagonal Recurrent Neural Network
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摘要: 分析了非线性系统神经网络建模的规律,利用对角回归神经网络(DRNN)实现了非线性动态系统的辨识.辨识结构采用串并联模式,网络权值的调整为考虑时变因素的调整算法.与静态神经网络相比,基于DRNN的辨识方法显示出很强的处理动态问题的能力,无需辨别系统阶次,辨识结构简单,收敛速度快.仿真结果表明该方法是有效可行的.Abstract: Based on an analysis on the modeling principles of nonlinear system, the identification of a nonlinear system was realized with Diagonal Recurrent Neural Networks (DRNN). Serial-parallel identification architecture was applied in the modeling. Time variation was taken into account in the adjustment algorithm of weights. Compared with static neural network, the method based on DRNN displays better ability to deal with a dynamic system, due to its advantages such as without the need of system order number, a smaller neural network structure and a faster convergence. Simulation results testified the feasibility and validity of the proposed method.
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Key words:
- neural networks /
- non-linear systems /
- systems identification
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