Partially Coupled Stochastic Gradient Identification Methods for Non-Uniformly Sampled Systems

Ding, Feng; Liu, Guangjun; Liu, Xiaoping Peter

doi:10.1109/tac.2010.2050713

Cited by 258 publications

(30 citation statements)

References 26 publications

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“…Moreover, the theoretical derivation should be carried out to support the neuro-based filter. The proposed method can combine other identification approaches [63][64][65][66] to study the modeling and filtering problems of other dynamic time series and stochastic systems with colored noises [67][68][69][70], and can be applied to other fields [71][72][73][74], such as signal modeling and control systems [75][76][77][78][79] studied in other literature [80].…”

Section: Discussionmentioning

confidence: 99%

A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model

Bai

Wang

Jin

et al. 2020

Sensors

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The control effect of various intelligent terminals is affected by the data sensing precision. The filtering method has been the typical soft computing method used to promote the sensing level. Due to the difficult recognition of the practical system and the empirical parameter estimation in the traditional Kalman filter, a neuron-based Kalman filter was proposed in the paper. Firstly, the framework of the improved Kalman filter was designed, in which the neuro units were introduced. Secondly, the functions of the neuro units were excavated with the nonlinear autoregressive model. The neuro units optimized the filtering process to reduce the effect of the unpractical system model and hypothetical parameters. Thirdly, the adaptive filtering algorithm was proposed based on the new Kalman filter. Finally, the filter was verified with the simulation signals and practical measurements. The results proved that the filter was effective in noise elimination within the soft computing solution.

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Section: Discussionmentioning

confidence: 99%

A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model

Bai

Wang

Jin

et al. 2020

Sensors

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“…Therefore, we used the ARIMA model to model and predict the trend component, and obtained more accurate results than neural network GRU. The proposed methods proposed in this paper can combine other identification approaches [38][39][40][41][42] to study the modeling and prediction problems of other dynamic time series and stochastic systems with colored noises [43][44][45][46][47], and can be applied to other fields [48][49][50][51][52] such as signal modeling and control systems [53][54][55][56].…”

Section: Discussionmentioning

confidence: 99%

Integrated Predictor Based on Decomposition Mechanism for PM2.5 Long-Term Prediction

et al. 2019

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Featured Application: This work can be used in the intelligent system for the smart city, smart agriculture, etc.Abstract: It is crucial to predict PM2.5 concentration for early warning regarding and the control of air pollution. However, accurate PM2.5 prediction has been challenging, especially in long-term prediction. PM2.5 monitoring data comprise a complex time series that contains multiple components with different characteristics; therefore, it is difficult to obtain an accurate prediction by a single model. In this study, an integrated predictor is proposed, in which the original data are decomposed into three components, that is, trend, period, and residual components, and then different sub-predictors including autoregressive integrated moving average (ARIMA) and two gated recurrent units are used to separately predict the different components. Finally, all the predictions from the sub-predictors are combined in fusion node to obtain the final prediction for the original data. The results of predicting the PM2.5 time series for Beijing, China showed that the proposed predictor can effectively improve prediction accuracy for long-term prediction. Appl. Sci. 2019, 9, 4533 2 of 14 models [14] and vector auto-regression (VAR) [15]. ARIMA is a linear modeling method that provides accurate predictions for approximate linear relationships. However, its prediction performance is not good enough for nonlinear prediction problems.To capture the complex nonlinearity of PM2.5, a back propagation (BP) neural network has been widely applied in time-series data prediction [16][17][18]. Other shallow nonlinear networks based on a machine-learning mechanism such as the improved gray neural network model [19] and the radial basis function (RBF) neural network [20] have also been used to predict time-series data. Xu et al. [3] proposed a supplementary leaky integrator echo state network (SLI-ESN), which added the historical state term of the historical moment to the calculation of a leaky integrator reservoir. Compared with an echo state network (ESN), a leaky integrator ESN (LI-ESN), an extreme learning machine (ELM), a hierarchical ELM (H-ELM), a stacked auto-encoder (SAE), and a traditional SLI-ESN, the proposed SLI-ESN of Xu et al.[3] could achieve good prediction results, but its long-term predictions were not satisfactory.As a shallow network still cannot effectively extract the complex nonlinearity of the data, decomposition must be used; that is, the data must be decomposed into multiple components to reduce its complexity, and then multiple sub-models are used to improve the prediction performance. For example, García et al. [21] decomposed a long-term data series into smaller seasonal component patterns. Jesús et al. [22] divided a pollen-concentration data series into seasonal and random parts, used partial least-squares regression (PLSR) to fit the residuals, and established an airborne pollen time-series model to predict the daily pollen concentration. Ming et al.[23] extracted accurate seasonal signals, a...

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“…Finally, it is worth noting that the proposed iterative methods can be extended to study identification problems for multivariable systems [32,33], nonlinear systems [34][35][36][37][38], multirate systems [39][40][41] and non-uniformly sampled-data systems [42][43][44][45][46]. …”

Section: Discussionmentioning

confidence: 99%