Dynamic Simulation of Stock Market Based on SVM With Different Parameter Optimization Methods

2017 ◽  
pp. 131-138 ◽  
Author(s):  
Wen Chen ◽  
Yixiang Tian
Keyword(s):  
Stock Market ◽  
Dynamic Simulation ◽  
Parameter Optimization ◽  
Optimization Methods

scholarly journals Exploration of Semantic Geo-Object Recognition Based on the Scale Parameter Optimization Method for Remote Sensing Images

2021 ◽  
Vol 10 (6) ◽  
pp. 420
Author(s):  
Jun Wang ◽  
Lili Jiang ◽  
Qingwen Qi ◽  
Yongji Wang
Keyword(s):  
Parameter Optimization ◽  
Scale Parameter ◽  
Human Perception ◽  
Optimization Methods ◽  
Optimization Method ◽  
Initiation Point ◽  
Scale Parameters ◽  
Object Based ◽  
Geographic Object ◽  
Multiresolution Segmentation

Image segmentation is of significance because it can provide objects that are the minimum analysis units for geographic object-based image analysis (GEOBIA). Most segmentation methods usually set parameters to identify geo-objects, and different parameter settings lead to different segmentation results; thus, parameter optimization is critical to obtain satisfactory segmentation results. Currently, many parameter optimization methods have been developed and successfully applied to the identification of single geo-objects. However, few studies have focused on the recognition of the union of different types of geo-objects (semantic geo-objects), such as a park. The recognition of semantic geo-objects is likely more crucial than that of single geo-objects because the former type of recognition is more correlated with the human perception. This paper proposes an approach to recognize semantic geo-objects. The key concept is that a single geo-object is the smallest component unit of a semantic geo-object, and semantic geo-objects are recognized by iteratively merging single geo-objects. Thus, the optimal scale of the semantic geo-objects is determined by iteratively recognizing the optimal scales of single geo-objects and using them as the initiation point of the reset scale parameter optimization interval. In this paper, we adopt the multiresolution segmentation (MRS) method to segment Gaofen-1 images and tested three scale parameter optimization methods to validate the proposed approach. The results show that the proposed approach can determine the scale parameters, which can produce semantic geo-objects.

Parameter optimization methods for estimating dynamic origin-destination trip-tables

1997 ◽  
Vol 31 (2) ◽  
pp. 141-157 ◽  
Author(s):  
Hanif D. Sherali ◽  
Namita Arora ◽  
Antoine G. Hobeika
Keyword(s):  
Parameter Optimization ◽  
Optimization Methods

scholarly journals Comparison of parameter optimization methods for quantitative susceptibility mapping

2020 ◽  
Vol 85 (1) ◽  
pp. 480-494 ◽  
Author(s):  
Carlos Milovic ◽  
Claudia Prieto ◽  
Berkin Bilgic ◽  
Sergio Uribe ◽  
Julio Acosta‐Cabronero ◽  
...  
Keyword(s):  
Parameter Optimization ◽  
Optimization Methods ◽  
Susceptibility Mapping ◽  
Quantitative Susceptibility Mapping

Parameter optimization in modelling unsteady compound channel flows

1992 ◽  
Vol 19 (3) ◽  
pp. 441-446 ◽  
Author(s):  
Habib Abida ◽  
Ronald D. Townsend
Keyword(s):  
Parameter Optimization ◽  
Optimization Methods ◽  
Flood Plain ◽  
Flood Routing ◽  
Compound Channel ◽  
Flood Events ◽  
Channel Section ◽  
Simplex Methods ◽  
Boundary Roughness ◽  
Good Agreement

Optimization methods are used to estimate data for routing floods through open compound channels (main channels with flood plain zones). These data include the irregular channel section geometry and the varying boundary roughness. Differences between simulated and observed stages and discharges are minimized using three optimization algorithms: Powell's method, Rosenbrock's algorithm, and the Nelder and Meade simplex method. Powells' method performed poorly; however, both the Rosenbrock and simplex methods yielded good results. The estimated data using the Rosenbrock and simplex methods were used to route different flood events observed in a laboratory channel. Simulated peak stages and discharges were in good agreement with those estimated using actual routing data. Key words: compound channel, flood routing, lateral momentum transfer, optimization, unsteady flow.

scholarly journals An effective parameter optimization with radiation balance constraint in CAM5 (version 5.3)

2019 ◽  
Author(s):  
Li Wu ◽  
Tao Zhang ◽  
Yi Qin ◽  
Wei Xue
Keyword(s):  
Parameter Optimization ◽  
General Circulation ◽  
Performance Indicator ◽  
Optimization Problems ◽  
Model Performance ◽  
Optimization Methods ◽  
General Circulation Models ◽  
Atmospheric Model ◽  
Radiation Balance ◽  
Performance Metric

Abstract. Uncertain parameters in physical parameterizations of General Circulation Models (GCMs) greatly impact model performance. In recent years, automatic parameter optimization has been introduced for tuning model performance of GCMs but most of the optimization methods are unconstrained optimization methods under a given performance indicator, so that the calibrated model may break through essential constraints that models have to keep, such as the radiation balance at top of model, which is known for its importance to the conservation of model energy. In this study, an automated and efficient parameter optimization with the radiation balance constraint is presented and applied in Community Atmospheric Model (CAM5) in terms of a synthesized performance metric using global means of radiation, precipitation, relative humidity, and temperature. The tuned parameters are from the parameterization schemes of convection and cloud. And the radiation constraint is defined as the deviation of the net longwave flux at top of model (FLNT) and net solar flux at top of model (FSNT) less than 1 W m−2. Results show that the synthesized performance under the optimal parameters is 6.3 % better than the control run (CNTL) as well as the radiation imbalance is as low as 0.1 W m−2. The proposed method provides the insight for physics-guided optimization under the premise of a profound understanding of models and it can be easily applied to optimization problems with other prerequisite constraints in GCMs.

scholarly journals An effective parameter optimization with radiation balance constraint in CAM5 (version 5.3)

2020 ◽  
Vol 13 (1) ◽  
pp. 41-53
Author(s):  
Li Wu ◽  
Tao Zhang ◽  
Yi Qin ◽  
Wei Xue
Keyword(s):  
Parameter Optimization ◽  
General Circulation ◽  
Performance Indicator ◽  
Optimization Problems ◽  
Model Performance ◽  
Optimization Methods ◽  
General Circulation Models ◽  
Atmospheric Model ◽  
Absolute Difference ◽  
Radiation Balance

Abstract. Uncertain parameters in physical parameterizations of general circulation models (GCMs) greatly impact model performance. In recent years, automatic parameter optimization has been introduced for tuning model performance of GCMs, but most of the optimization methods are unconstrained optimization methods under a given performance indicator. Therefore, the calibrated model may break through essential constraints that models have to keep, such as the radiation balance at the top of the model. The radiation balance is known for its importance in the conservation of model energy. In this study, an automated and efficient parameter optimization with the radiation balance constraint is presented and applied in the Community Atmospheric Model (CAM5) in terms of a synthesized performance metric using normalized mean square error of radiation, precipitation, relative humidity, and temperature. The tuned parameters are from the parameterization schemes of convection and cloud. The radiation constraint is defined as the absolute difference of the net longwave flux at the top of the model (FLNT) and the net solar flux at the top of the model (FSNT) of less than 1 W m−2. Results show that the synthesized performance under the optimal parameters is 6.3 % better than the control run (CNTL) and the radiation imbalance is as low as 0.1 W m−2. The proposed method provides an insight for physics-guided optimization, and it can be easily applied to optimization problems with other prerequisite constraints in GCMs.

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