Event-triggered minimax state estimation with a relative entropy constraint

Xu, Jiapeng; Tang, Yang; Yang, Wen; Li, Fangfei; Shi, Ling

doi:10.1016/j.automatica.2019.108592

Cited by 25 publications

(5 citation statements)

References 34 publications

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“…where the recursion starts from P s 0 = Θ 0 0 withx s 0 = 0. As is shown in some previous studies, such as the standard Kalman filter analysis in [16], P s k in (10), which denotes the estimation error covariance, converges to a steady state value by an exponential rate.…”

Section: Sensor Local Estimatementioning

confidence: 58%

“…The problem of real-time reachable set control for a class of singular Markov jump networked cascade systems with time-delay, disturbance and non-zero initial conditions, was considered in [15]. For subsequent results of similar studies, see also [16][17][18]. Although an optimal estimation can be found in this way, an event-triggered approach also needs to be studied for a better transmission effect.…”

Section: Introductionmentioning

confidence: 99%

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Periodic Event-Triggered Estimation for Networked Control Systems

et al. 2021

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This paper considers the problem of remote state estimation in a linear discrete invariant system, where a smart sensor is utilized to measure the system state and generate a local estimate. The communication depends on an event scheduler in the smart sensor. When the channel between the remote estimator and the smart sensor is activated, the remote estimator simply adopts the estimate transmitted by the smart sensor. Otherwise, it calculates an estimate based on the available information. The closed-form of the minimum mean-square error (MMSE) estimator is introduced, and we use Gaussian preserving event-based sensor scheduling to obtain an ideal compromise between the communication cost and estimation quality. Furthermore, we calculate a variation range of communication probability, which helps to design the policy of event-triggered estimation. Finally, the simulation results are given to illustrate the effectiveness of the proposed event-triggered estimator.

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Section: Sensor Local Estimatementioning

confidence: 58%

Section: Introductionmentioning

confidence: 99%

Periodic Event-Triggered Estimation for Networked Control Systems

et al. 2021

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“…According to entropy theory, if one event is more likely to occur than another, then the amount of information that can be determined based on observations of that event is small [ 40 , 41 ]. Conversely, more information can be obtained by observing rare events [ 42 , 43 ].…”

Section: Methodsmentioning

confidence: 99%

The Feasibility of Information-Entropy-Based Behavioral Analysis for Detecting Environmental Barriers

Lee

Hwang

Kim

2021

IJERPH

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The enhancement of physical activity is highly correlated with the conditions of the built environment. Walking is considered to be a fundamental daily physical activity, which requires an appropriate environment. Therefore, the barriers of the built environment should be identified and addressed. Barriers can act as external stimuli for pedestrians, so pedestrians may diversely respond to them. Based on this consideration, this study examines the feasibility of information-entropy-based behavioral analysis for the detection of environmental barriers. The physical responses of pedestrians were collected using an inertial measurement unit (IMU) sensor in a smartphone. After the acquired data were converted to behavioral probability distributions, the information entropy of each grid cell was calculated. The grid cells whereby the participants indicated that environmental barriers were present yielded relatively high information entropy values. The findings of this study will facilitate the design of more pedestrian-friendly environments and the development of diverse approaches that utilize citizens for monitoring the built environment.

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“…Devi et al [10] proposed an improved ARIMA model, and the trained time series model was used to predict future stock price fluctuations. However, the ARIMA model [11] requires a stable input time series, though in reality, most financial data is not necessarily stable [12]. Therefore, for some stock types with severe stock price fluctuations, the ARIMA model does not have universality.…”

Section: Introductionmentioning

confidence: 99%

Improved Financial Predicting Method Based on Time Series Long Short-Term Memory Algorithm

Li,

Zhou

2024

Mathematics

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With developments in global economic integration and the increase in future economic uncertainty, it is imperative to have the ability to predict future capital in relation to financial capital inflow and outflow predictions to ensure capital optimization is within a controllable range within the current macroeconomic environment and situation. This paper proposes an automated capital prediction strategy for the capital supply chain using time series analysis artificial intelligence methods. Firstly, to analyze the fluctuation and tail risk of the financial characteristics, the paper explores the financial characteristics for measuring the dynamic VaR from the perspectives of volatility, tail, and peak with the Bayesian peaks over threshold (POT) model. Following this, in order to make the modeling more refined, the forecast targets are split before modeling with seasonal Autoregressive Integrated Moving Average (ARIMA) models and Prophet models. Finally, the time series modeling of the wavelet Long Short-Term Memory (LSTM) model is carried out using a two-part analysis method to determine the linear separated wavelet and non-linear embedded wavelet parts to predict strong volatility in financial capital. Taking the user capital flow of the Yu’e Bao platform, the results prove the feasibility and prediction accuracy of the innovative model proposed.

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Event-triggered minimax state estimation with a relative entropy constraint

Cited by 25 publications

References 34 publications

Periodic Event-Triggered Estimation for Networked Control Systems

Periodic Event-Triggered Estimation for Networked Control Systems

The Feasibility of Information-Entropy-Based Behavioral Analysis for Detecting Environmental Barriers

Improved Financial Predicting Method Based on Time Series Long Short-Term Memory Algorithm

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