Spatio-temporal adaptive soft sensor for nonlinear time-varying and variable drifting processes based on moving window LWPLS and time difference model

2015 ◽  
Vol 11 (2) ◽  
pp. 209-219 ◽  
Author(s):  
Xiaofeng Yuan ◽  
Zhiqiang Ge ◽  
Zhihuan Song
Keyword(s):  
Soft Sensor ◽  
Time Difference ◽  
Time Varying ◽  
Moving Window ◽  
Difference Model ◽  
Spatio Temporal

scholarly journals An adaptive soft sensor deterioration evaluation and model updating method for time-varying chemical processes

2020 ◽  
Vol 26 (2) ◽  
pp. 135-149
Author(s):  
Longhao Li ◽  
Yongshou Dai
Keyword(s):  
Auxiliary Variable ◽  
Soft Sensor ◽  
Prediction Performance ◽  
Chemical Processes ◽  
Statistical Hypothesis ◽  
Time Varying ◽  
Moving Window ◽  
Auxiliary Variables ◽  
Sensor Model ◽  
Sensor Modeling

Due to the time-varying nature of chemical processes, soft sensor models deteriorate, and data prediction accuracy decreases. To address this problem, an adaptive soft sensor modeling method is proposed that not only evaluates the model deterioration by an adaptive moving window-constrained statistical hypothesis test, but also adaptively updates the modeling samples using moving window-cosine similarity. First, this method evaluates the model deterioration via positioning by constrained statistical hypothesis testing based on the differences between the prediction performance evaluation index data obtained from moving window stepping and the original prediction performance evaluation indexes. Additionally, the dynamic temporal variation in chemical processes causes changes in the impacts of the auxiliary variables on the dominant variable, and this effect limits the improvement in the prediction accuracy of the soft sensor model by updating only the auxiliary variable data. The moving window-cosine similarity method is combined to propose a strategy that updates both the modeled auxiliary variables and the auxiliary variable data. Finally, the parameters of the soft sensor model are optimized via particle swarm optimization (PSO) to improve the fitting performance. Simulated data of a continuous stirred tank reactor (CSTR) and actual data from a debutanizer column process (DCP) are used for model verification to evaluate the performance of the proposed adaptive soft sensor modeling method, and the results show its effectiveness.

scholarly journals Spatio-temporal integration in plant tropisms

2019 ◽  
Vol 16 (154) ◽  
pp. 20190038 ◽  
Author(s):  
Yasmine Meroz ◽  
Renaud Bastien ◽  
L. Mahadevan
Keyword(s):  
Response Function ◽  
Shoot Growth ◽  
Analytic Solution ◽  
Temporal Integration ◽  
Time Varying ◽  
Local Response ◽  
History Of ◽  
Non Local ◽  
Spatio Temporal ◽  
Plant Shoots

Tropisms, growth-driven responses to environmental stimuli, cause plant organs to respond in space and time and reorient themselves. Classical experiments from nearly a century ago reveal that plant shoots respond to the integrated history of light and gravity stimuli rather than just responding instantaneously. We introduce a temporally non-local response function for the dynamics of shoot growth formulated as an integro-differential equation whose solution allows us to qualitatively reproduce experimental observations associated with intermittent and unsteady stimuli. Furthermore, an analytic solution for the case of a pulse stimulus expresses the response function as a function of experimentally tractable variables, which we calculate for the case of the phototropic response of Arabidopsis hypocotyls. All together, our model enables us to predict tropic responses to time-varying stimuli, manifested in temporal integration phenomena, and sets the stage for the incorporation of additional effects such as multiple stimuli, gravitational sagging, etc.

Synchronization for Nonlinear Complex Spatio-Temporal Networks with Multiple Time-Invariant Delays and Multiple Time-Varying Delays

2018 ◽  
Vol 50 (2) ◽  
pp. 1051-1064 ◽  
Author(s):  
Chengdong Yang ◽  
Tingwen Huang ◽  
Kejia Yi ◽  
Ancai Zhang ◽  
Xiangyong Chen ◽  
...  
Keyword(s):  
Multiple Time ◽  
Time Varying ◽  
Temporal Networks ◽  
Spatio Temporal ◽  
Time Invariant

Turing and Benjamin–Feir instability mechanisms in non-autonomous systems

2020 ◽  
Vol 476 (2238) ◽  
pp. 20200003 ◽  
Author(s):  
Robert A. Van Gorder
Keyword(s):  
Autonomous Systems ◽  
Reaction Diffusion ◽  
Reaction Rates ◽  
Time Dependent ◽  
Model Parameters ◽  
Time Varying ◽  
Reaction Diffusion Systems ◽  
Diffusion Systems ◽  
Dependent Model ◽  
Spatio Temporal

The Turing and Benjamin–Feir instabilities are two of the primary instability mechanisms useful for studying the transition from homogeneous states to heterogeneous spatial or spatio-temporal states in reaction–diffusion systems. We consider the case when the underlying reaction–diffusion system is non-autonomous or has a base state which varies in time, as in this case standard approaches, which rely on temporal eigenvalues, break down. We are able to establish respective criteria for the onset of each instability using comparison principles, obtaining inequalities which involve the in general time-dependent model parameters and their time derivatives. In the autonomous limit where the base state is constant in time, our results exactly recover the respective Turing and Benjamin–Feir conditions known in the literature. Our results make the Turing and Benjamin–Feir analysis amenable for a wide collection of applications, and allow one to better understand instabilities emergent due to a variety of non-autonomous mechanisms, including time-varying diffusion coefficients, time-varying reaction rates, time-dependent transitions between reaction kinetics and base states which change in time (such as heteroclinic connections between unique steady states, or limit cycles), to name a few examples.

scholarly journals A Simple Time-Varying Sensitivity Analysis (TVSA) for Assessment of Temporal Variability of Hydrological Processes

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2463 ◽  
Author(s):  
Yelena Medina ◽  
Enrique Muñoz
Keyword(s):  
Sensitivity Analysis ◽  
Temporal Variability ◽  
Time Windows ◽  
Computational Cost ◽  
Time Varying ◽  
Moving Window ◽  
Time Sensitivity ◽  
Annual Time ◽  
Low Computational Cost ◽  
High Computational Cost

Time-varying sensitivity analysis (TVSA) allows sensitivity in a moving window to be estimated and the time periods in which the specific components of a model can affect its performance to be identified. However, one of the disadvantages of TVSA is its high computational cost, as it estimates sensitivity in a moving window within an analyzed series, performing a series of repetitive calculations. In this article a function to implement a simple TVSA with a low computational cost using regional sensitivity analysis is presented. As an example of its application, an analysis of hydrological model results in daily, monthly, and annual time windows is carried out. The results show that the model allows the time sensitivity of a model with respect to its parameters to be detected, making it a suitable tool for the assessment of temporal variability of processes in models that include time series analysis. In addition, it is observed that the size of the moving window can influence the estimated sensitivity; therefore, analysis of different time windows is recommended.

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