Abstract
The goal of this paper is to introduce a new neural network architecture called Sigmoid Diagonal Recurrent Neural Network (SDRNN) to be used in the adaptive control of nonlinear dynamical systems. This is done by adding a sigmoid weight victor in the hidden layer neurons to adapt of the shape of the sigmoid function making their outputs not restricted to the sigmoid function output. Also, we introduce a dynamic back propagation learning algorithm to train the new proposed network parameters. The simulation results showed that the (SDRNN) is more efficient and accurate than the DRNN in both the identification and adaptive control of nonlinear dynamical systems.
Highlights
IntroductionThe remarkable learning capability of neural networks is leading to their wide application in identification and adaptive control of nonlinear dynamical systems [1,2,3,4,5,6] and the tracking accuracy depends on neural networks structure, which should be chosen properly [7,8,9,10,11,12,13].Feedforward Neural Network (FNN) [4] is a static mapping and can not reflect the dynamics of the nonlinear systems without using Tapped Delay Lines (TDL) [7,9]
The goal of this paper is to introduce a new neural network architecture called Sigmoid Diagonal Recurrent Neural Network (SDRNN) to be used in the adaptive control of nonlinear dynamical systems
Based on Locally Recurrent Globally Feedforward network architectures (LRGF) many researchers focused in (DRNN) which doesn't contain interlink between hidden layer neurons leading to the network structure complexity reduction [15,8,9,10]
Summary
The remarkable learning capability of neural networks is leading to their wide application in identification and adaptive control of nonlinear dynamical systems [1,2,3,4,5,6] and the tracking accuracy depends on neural networks structure, which should be chosen properly [7,8,9,10,11,12,13].Feedforward Neural Network (FNN) [4] is a static mapping and can not reflect the dynamics of the nonlinear systems without using Tapped Delay Lines (TDL) [7,9]. Based on Locally Recurrent Globally Feedforward network architectures (LRGF) many researchers focused in (DRNN) which doesn't contain interlink between hidden layer neurons leading to the network structure complexity reduction [15,8,9,10]. In all these architectures, the hidden layer neurons output restricted to the sigmoid function output which represents a major disadvantage in the network behavior and significantly reduces its performance accuracy. Simulation results show that SDRNN is more suited than the (DRNN) for identification and adaptive control of nonlinear dynamical systems [9]
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