Gradient-Based Particle Filter Algorithm for an ARX Model With Nonlinear Communication Output

Chen, Jing; Liu, Yanjun; Ding, Feng; Zhu, Quanmin

doi:10.1109/tsmc.2018.2810277

Cited by 31 publications

(14 citation statements)

References 38 publications

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“…46 Many identification methods based on the particle filtering technique have been developed for linear or nonlinear systems with unknown outputs. 47,48 For example, for an ARX model with nonlinear communication output, Chen et al proposed a stochastic gradient particle filter algorithm; 49 for a controlled autoregressive system with quantized output, Ding et al derived a novel particle filtering-based recursive prediction-error stochastic gradient algorithm. 50 However, most of these identification methods are recursive, and do not make full use of the measured input-output data.…”

Section: Introductionmentioning

confidence: 99%

Iterative identification methods for a class of bilinear systems by using the particle filtering technique

2021

Adaptive Control & Signal

118

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This article mainly studies the iterative parameter estimation problems of a class of nonlinear systems. Based on the auxiliary model identification idea, this article utilizes the estimated parameters to construct an auxiliary model, and uses its outputs to replace the unknown noise-free process outputs, and develops an auxiliary model least squares-based iterative (AM-LSI) identification algorithm.For further improving the parameter estimation accuracy, we use a particle filter to estimate the unknown noise-free process outputs, and derive a particle filtering least squares-based iterative (PF-LSI) identification algorithm. During each iteration, the AM-LSI and PF-LSI algorithms can make full use of the measured input-output data. The simulation results indicate that the proposed algorithms are effective for identifying the nonlinear systems, and can generate more accurate parameter estimates than the auxiliary model-based recursive least squares algorithm.

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Section: Introductionmentioning

confidence: 99%

Iterative identification methods for a class of bilinear systems by using the particle filtering technique

2021

Adaptive Control & Signal

118

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“…The training rule of the neural network is to minimize its loss function to reach the optimization of the weights and biases. The commonly used optimization algorithm is the gradient descent (GD) algorithm 30‐34 . However, when using the GD algorithm to train the network, it often requires plenty of time and computing resources.…”

Section: Introductionmentioning

confidence: 99%

The Savitzky‐Golay filter based bidirectional long short‐term memory network for SOC estimation

Jiao

Wang

2021

Int J Energy Res

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Summary This paper investigates a Savitzky‐Golay filter based bidirectional long short‐term memory network (SG‐BiLSTM) by using the Adam algorithm for the state of charge (SOC) estimation of lithium batteries. In this hybrid method, a BiLSTM network is constructed to estimate SOC by using the discharge current and the terminal voltage as inputs, the Adam algorithm is adopted to update the weights and biases of the BiLSTM, and the SG filter is introduced to process the estimated SOCs. In the experimental part, the urban dynamometer driving schedule (UDDS) profile is performed on a battery test platform for data acquisition. In the simulation part, the root mean squared error (RMSE) and the coefficient of determination (R2) is used to evaluate the model performance under different cases. The estimation results indicate that: the SG‐BiLSTM has faster convergence speed and higher estimation accuracy when compared with other methods; the SG‐BiLSTM shows strong robustness when applied to the data set with random noises added; appropriately increasing the hidden neurons helps to improve the model performance, but excessive increase will lead to overfitting.

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“…ARX model is an AutoRegressive model with eXogenous terms [31]. Because of its simplicity and easy parameterization, the ARX model has been widely used to model a lot of real systems, such as micro-turbines, data improving, fault detection, biomedical signals, COVID-19 case forecasting and communication systems [1,3,7,28,34,44]. Much research has been performed to identify ARX models in the last five decades.…”

Section: Introductionmentioning

confidence: 99%

Identification of the ARX Model with Random Impulse Noise Based on Forgetting Factor Multi-error Information Entropy

Jing

2021

Circuits Syst Signal Process

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Entropy has been widely applied in system identification in the last decade. In this paper, a novel stochastic gradient algorithm based on minimum Shannon entropy is proposed. Though needing less computation than the mean square error algorithm, the traditional stochastic gradient algorithm converges relatively slowly. To make the convergence faster, a multi-error method and a forgetting factor are integrated into the algorithm. The scalar error is replaced by a vector error with stacked errors. Further, a simple step size method is proposed and a forgetting factor is adopted to adjust the step size. The proposed algorithm is utilized to estimate the parameters of an ARX model with random impulse noise. Several numerical solutions and case study indicate that the proposed algorithm can obtain more accurate estimates than the traditional gradient algorithm and has a faster convergence speed.

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Gradient-Based Particle Filter Algorithm for an ARX Model With Nonlinear Communication Output

Cited by 31 publications

References 38 publications

Iterative identification methods for a class of bilinear systems by using the particle filtering technique

Iterative identification methods for a class of bilinear systems by using the particle filtering technique

The Savitzky‐Golay filter based bidirectional long short‐term memory network for SOC estimation

Identification of the ARX Model with Random Impulse Noise Based on Forgetting Factor Multi-error Information Entropy

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