Extended Kalman Filter Based Modified Elman-Jordan Neural Network for Control and Identification of Nonlinear Systems

Şen, Gökçen Devlet; Günel, Gülay Öke; Güzelkaya, Müjde

doi:10.1109/asyu50717.2020.9259812

Cited by 9 publications

(8 citation statements)

References 17 publications

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“…Where, 𝐼 𝑖 , 𝐹𝑂𝑃𝐼𝐷, 𝐻 𝑗 , ℎ 𝑘 , 𝑂 𝑙 , 𝐶 𝑐 , 𝐽 𝑚 , 𝑆 𝑟 , 𝑊 𝑗𝑖 , 𝑉 𝑘𝑗 , 𝑍 𝑙𝑘 , 𝑄 𝑗𝑐 , 𝑅 𝑘𝑐 , 𝐸 𝑘𝑟 , 𝑓 𝑗 , 𝑔 𝑘 , 𝛼 , 𝛽, 𝜂 , 𝑎𝑛𝑑 𝑠𝑡: layer1, layer2, layer3, layer4, layer5, layer6, layer7, layer8, the weight between layers (2&3), the weight between layer(3&4), the weight between layer(4&5), the weight between layer(3&6), the weight between layer(3&7), the weight between layer(4&8), the sigmoid activation function, the feedback gain to the self-connection of layer (6&7&8), and step size respectively [28]. A FOPPIDNNC4 is shown in Fig.…”

Section: Fractional Order Proportional Integral Derivative Neural Net...mentioning

confidence: 99%

FOPID Neural Network Controller Design for Nonlinear CSTR System

2023

IJIES

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In this paper, four control structures are provided for continuous ctirred-tank reactor (CSTR) system's fractional/integer order proportional integral derivative neural network controllers. The revised neural network weights and the controller's parameters are optimized using the optimization technique called ant colony optimization (ACO). The proposed controllers' resistance to changes in the initial state, outside disturbances, and parameter modifications is also tested. The fractional order proportional integral derivative neural network controllers provide the best assurance and also enhance the system's robustness to changes in the initial state, external disturbances, and parameter variations, according to the results of MATLAB code.The fractional order proportional integral derivative neural network controller1(FOPIDNNC1) is the best structure among all those with the minimum cost function equal to 0.011588 for the set-point variations, 0.015325 for uncertainty parameter, 0.018274 for disturbances rejection, and the best structure fractional order proportional integral derivative neural network controller3 (FOPIDNNC3) among all those with the minimum cost function equal to 0.008733 for tracking.

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Section: Fractional Order Proportional Integral Derivative Neural Net...mentioning

confidence: 99%

FOPID Neural Network Controller Design for Nonlinear CSTR System

2023

IJIES

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“…Therefore, according to the error positive feedback correction of reverse transmission, the error is continuously controlled within the adaptive threshold, and satisfactory results can be obtained. Under artificial conditions, the neural network cannot rely on a thin database to complete, so the limitation is that a large number of data must be mastered and collected to complete the operating conditions of BP neural network modeling [24,[6][7].…”

Section: Back Propagation Neural Networkmentioning

confidence: 99%

“…where Λ c is the constant with respect to Λ . Let γ λ   , and utilizing (24) in (22), this paper obtains…”

Section: The Pgaf With Variable Step Sizementioning

confidence: 99%

“…where the approximate PDF of   q  at the i+1-th it- and utilizing (24) in (22), this paper obtains…”

Section: The Pgaf With Variable Step Sizementioning

confidence: 99%

“…is the constant with respect to Λ . Let d utilizing (24) in (22), this paper obtains (25) approximate PDF of   q  at the i+1-th itn be written as   i+1 q  , and…”

Section: The Pgaf With Variable Step Sizementioning

confidence: 99%

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An Adaptive Fuzzy Neural Network Based On Progressive Gaussian Approximate Filter with Variable Step Size

Zhu

2022

ITC

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The nonlinear filtering problem is a hot spot in robot navigation research. As an advanced method that can effectively improve the robustness and accuracy of the system, the progressive Gaussian approximate filter with variable step size (PGAFVS) still has some shortcomings, how to resolve the nonlinear filtering problem in the application of tightly coupled integration under the premise of the prior uncertainty and further promote robustness high measurement accuracy, which becomes the purpose of this paper. This paper formulates the processing of trajectory tracking measurement noise problem as a Kalman filtering procedure and the measurement noise covariance matrix in controller, is jointly estimated based on the progressive Gaussian approximate filter (PGAF), after that, PGAFVS can be deduced. Then we proposed an adaptive fuzzy and backpropagation neural network controller based on PGAFVS (AFNPGA-VS) that can improve the application of tightly coupled integration under the premise of the prior uncertainty and further promote robustness high measurement accuracy. The simulation results show that the proposed algorithm outperforms the state-of-the-art methods.

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