The Property of PID Elman Neural Network and Its Application in Identification of Hydraulic Unit

Ji, Qiaoling; Qi, Weimin

doi:10.1109/icca.2007.4376670

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…The first group of context units stores the feedback from the outputs of the hidden layer while the second group stores the feedback of the output layer. Ji and Qi [12] proposed ProportionalIntergral-Derivative (PID) Elman neural network (Fig. 4) In comparison to PI Elman network proposed by Gao, Gao, and Ovaska, this PID Elman network has an additional group of context units called memory layer.…”

Section: Recurrent Neural Networkmentioning

confidence: 98%

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Nonlinear dynamic system identification using recurrent neural network with multi-segment piecewise-linear connection weight

Thammano

Ruxpakawong

2010

Memetic Comp.

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This paper introduces a new concept of the connection weight to the standard recurrent neural networksElman and Jordan networks. The architecture of the modified networks is the same as that of the original recurrent neural networks. However, unlike the original recurrent neural networks whose connection weight is a single real number, in the modified networks the weight of each connection is multivalued, depending on the value of the input data involved. The backpropagation learning algorithm is also modified to suit the proposed concept. The modified networks have been benchmarked against the feedforward neural network and the original recurrent neural networks. The experimental results on twelve benchmark problems show that the modified networks are clearly superior to the other three methods.

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Section: Recurrent Neural Networkmentioning

confidence: 98%

“…Repeat steps 4 through 10 for all remaining input patterns in the training data set (12). Evaluate the weight updates occurred at the lower bound and the upper bound of each sth segment in the last training iteration whether the lower bound and the upper bound were updated in the same direction.…”

mentioning

confidence: 99%

Nonlinear dynamic system identification using recurrent neural network with multi-segment piecewise-linear connection weight

Thammano

Ruxpakawong

2010

Memetic Comp.

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“…This network structure, which was called the Modified Recurrent Network (MRN), was exploited to control nonlinear dynamical systems. Ji and Qi [7] proposed a proportional-integral-derivative (PID) MLEN which has two context layers to improve the approximation accuracy of the original MLEN. Ge et al [8] utilized the MELN to control the speed of an ultrasonic motor.…”

Section: Introductionmentioning

confidence: 99%

A Simplified Recurrent Neural Network Trained by Gbest-Guided Gravitational Search Algorithm to Control Nonlinear Systems

Lutfy¹,

Jassim²

2018

ETJ

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This paper presents a feedback control strategy using a Simplified Recurrent Neural Network (SRNN) for nonlinear dynamical systems. As an enhancement for a previously reported modified recurrent network (MRN), the proposed SRNN structure is used as an intelligent Proportional-Integral-Derivative (PID)-like controller. More precisely, the enhancement in the SRNN structure was realized by employing unity weight values between the context and the hidden layers in the original MRN structure. The newly developed Gbest-guided Gravitational Search Algorithm (GGSA) was adopted for optimizing the parameters of the SRNN structure. To show the efficiency of the proposed PID-like SRNN controller, three different nonlinear systems were considered as case studies, including a control valve, and a complex difference eq.. From an extensive set of evaluation tests, which includes a control performance test, a disturbance rejection test, and a generalization test, the proposed PID-like SRNN controller demonstrated its effectiveness with regards to precise control and good robustness and generalization abilities. Furthermore, compared to other Neural Network (NN) structures, including the original MRN and the Multilayer Perceptron (MLP) NN, the SRNN structure attained superior results due to the utilization of a reduced set of parameters. From another study, the GGSA accomplished the best optimization results in terms of control precision and convergence speed compared to the original Gravitational Search Algorithm (GSA).

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Gas Discharge Tube's Electromagnetic Pulse Response Prediction Based on Elman Neural Network

Wang

Nie

Sun

et al. 2018

Proceedings of the 3rd International Conference on Robotics, Control and Automation

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The Property of PID Elman Neural Network and Its Application in Identification of Hydraulic Unit

Cited by 3 publications

References 4 publications

Nonlinear dynamic system identification using recurrent neural network with multi-segment piecewise-linear connection weight

Nonlinear dynamic system identification using recurrent neural network with multi-segment piecewise-linear connection weight

A Simplified Recurrent Neural Network Trained by Gbest-Guided Gravitational Search Algorithm to Control Nonlinear Systems

Gas Discharge Tube's Electromagnetic Pulse Response Prediction Based on Elman Neural Network

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