scholarly journals New Strategy for Analog Circuit Performance Evaluation under Disturbance and Fault Value

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Aihua Zhang ◽  
Yongchao Wang ◽  
Chen Chen ◽  
Hamid Reza Karimi
Keyword(s):  
Performance Evaluation ◽  
Support Vector Regression ◽  
Analog Circuit ◽  
Support Vector ◽  
Training Set ◽  
Circuit Performance ◽  
New Strategy ◽  
Indirect Evaluation ◽  
Novel Strategy ◽  
Fuzzy Learning

Focus on this issue of disturbance and fault value is inevitable in data collection about analog circuit. A novel strategy is developed for analog circuit online performance evaluation based on fuzzy learning and double weighted support vector machine (DWMK-FSVM). First, the double weighted support vector regression machine is employed to be the indirect evaluation means, relied on the college analog electronic technology experiment to evaluate analog circuit. Second, the superiority of fuzzy learning also is addressed to realize active suppression to the fault values and disturbance parameters. Moreover, the multikernel RBF is employed by support vector regression machine to realize more flexibility online such as the bandwidths tuning. Numerical results, supported by the college analog circuit experiments, adopted OTL performance eight indexes, which were obtained via precision instrument evaluation in two years to construct training set and are then to be evaluated online based on DWMK-FSVM. Simulation results presented not only highlight precision of the evaluation strategy derived here but also illustrate its great robustness.

scholarly journals Research on Amplifier Performance Evaluation Based onδ-Support Vector Regression

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Xing Huo ◽  
Aihua Zhang ◽  
Hamid Reza Karimi
Keyword(s):  
Performance Evaluation ◽  
Support Vector Regression ◽  
Computer Calculation ◽  
Support Vector ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Evaluation Strategy ◽  
Support Vectors ◽  
Performance Indexes ◽  
Rbf Kernel

Focusing on the amplifier performance evaluation demand, a novel evaluation strategy based onδ-support vector regression (δ-SVR) is proposed in this paper. Lower computer calculation demand is considered firstly. And this is dealt with by the superiority ofδ-SVR which can be significantly improved on the number of support vectors. Moreover, the function ofδ-SVR employs the modified RBF kernel function which is constructed from an original kernel by removing the last coordinate and adding the linear term with the last coordinate. Experiment adopted the typical circuit Sallen-Key low pass filter to prove the proposed evaluation strategy via the eight performance indexes. Simulation results reveal that the need of the number ofδ-SVR support vectors is the lowest among the other two methods LSSVR andε-SVR under obtaining nearly the same evaluation result. And this is also suitable for promotion computational speed.

An Alternative Approach to Prediction of Critical Heat Flux: Projection Support Vector Regression

2014 ◽  
Author(s):  
Botao Jiang ◽  
Fuyu Zhao
Keyword(s):  
Heat Flux ◽  
Support Vector Regression ◽  
Critical Heat Flux ◽  
Cooling System ◽  
Prediction Method ◽  
Training Data ◽  
Support Vector ◽  
Training Set ◽  
Test Set ◽  
Alternative Approach

Critical heat flux (CHF) is one of the most crucial design criteria in other boiling systems such as evaporator, steam generators, fuel cooling system, boiler, etc. This paper presents an alternative CHF prediction method named projection support vector regression (PSVR), which is a combination of feature vector selection (FVS) method and support vector regression (SVR). In PSVR, the FVS method is first used to select a relevant subset (feature vectors, FVs) from the training data, and then both the training data and the test data are projected into the subspace constructed by FVs, and finally SVR is applied to estimate the projected data. An available CHF dataset taken from the literature is used in this paper. The CHF data are split into two subsets, the training set and the test set. The training set is used to train the PSVR model and the test set is then used to evaluate the trained model. The predicted results of PSVR are compared with those of artificial neural networks (ANNs). The parametric trends of CHF are also investigated using the PSVR model. It is found that the results of the proposed method not only fit the general understanding, but also agree well with the experimental data. Thus, PSVR can be used successfully for prediction of CHF in contrast to ANNs.

scholarly journals Comparison of Regression, Support Vector Regression (SVR), and SVR-Particle Swarm Optimization (PSO) for Rainfall Forecasting

2020 ◽  
Vol 5 (3) ◽  
pp. 235
Author(s):  
Fendy Yulianto ◽  
Wayan Firdaus Mahmudy ◽  
Arief Andy Soebroto
Keyword(s):  
Particle Swarm Optimization ◽  
Performance Evaluation ◽  
Support Vector Regression ◽  
Regression Function ◽  
High Accuracy ◽  
Rainfall Data ◽  
Support Vector ◽  
Rainfall Forecasting ◽  
Swarm Optimization ◽  
Rbf Kernel

Rainfall is one of the factors that influence climate change in an area and is very difficult to predict, while rainfall information is very important for the community. Forecasting can be done using existing historical data with the help of mathematical computing in modeling. The Support Vector Regression (SVR) method is one method that can be used to predict non-linear rainfall data using a regression function. In calculations using the regression function, choosing the right SVR parameters is needed to produce forecasting with high accuracy. Particle Swarm Optimization (PSO) method is one method that can be used to optimize the parameters of the existing SVR method, so that it will produce SVR parameter values with high accuracy. Forecasting with rainfall data in Poncokusumo region using SVR-PSO has a performance evaluation value that refers to the value of Root Mean Square Error (RMSE). There are several Kernels that will be used in predicting rainfall using Regression, SVR, and SVR-PSO with Linear Kernels, Gaussian RBF Kernels, ANOVA RBF Kernels. The results of the performance evaluation values obtained by referring to the RMSE value for Regression is 56,098, SVR is 88,426, SVR-PSO method with Linear Kernel is 7.998, SVR-PSO method with Gaussian RBF Kernel is 27.172, and SVR-PSO method with ANOVA RBF Kernel is 2.193. Based on research that has been done, ANOVA RBF Kernel is a good Kernel on the SVR-PSO method for use in rainfall forecasting, because it has the best forecasting accuracy with the smallest RMSE value.

scholarly journals A New Adaptive LSSVR with Online Multikernel RBF Tuning to Evaluate Analog Circuit Performance

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Aihua Zhang ◽  
Chen Chen ◽  
Hamid Reza Karimi
Keyword(s):  
Analog Circuit ◽  
Feature Space ◽  
Support Vector ◽  
Fast Time ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Circuit Performance ◽  
Evaluation Strategy ◽  
Performance Indexes ◽  
Testing Speed

Focusing on the analog circuit performance evaluation demand of fast time responding online, a novel evaluation strategy based on adaptive Least Squares Support Vector Regression (LSSVR) which employs multikernel RBF is proposed in this paper. The superiority of the multi-kernel RBF has more flexibility to the kernel function online such as the bandwidths tuning. And then the decision parameters of the kernel parameters determine the input signal to map to the feature space deduced that a well plant model by discarding redundant features. Experiment adopted the typical circuit Sallen-Key low pass filter to prove the proposed evaluation strategy via the eight performance indexes. Simulation results reveal that the testing speed together with the evaluation performance, especially the testing speed of the proposed, is superior to that of the traditional LSSVR andε-SVR, which is suitable for promotion online.

Accurate On-line Support Vector Regression

2003 ◽  
Vol 15 (11) ◽  
pp. 2683-2703 ◽  
Author(s):  
Junshui Ma ◽  
James Theiler ◽  
Simon Perkins
Keyword(s):  
Support Vector Regression ◽  
Numerical Experiments ◽  
Cross Validation ◽  
Classification Algorithm ◽  
Support Vector ◽  
Training Set ◽  
Warm Start ◽  
On Line ◽  
Line Setting

Batch implementations of support vector regression (SVR) are inefficient when used in an on-line setting because they must be retrained from scratch every time the training set is modified. Following an incremental support vector classification algorithm introduced by Cauwenberghs and Poggio (2001), we have developed an accurate on-line support vector regression (AOSVR) that efficiently updates a trained SVR function whenever a sample is added to or removed from the training set. The updated SVR function is identical to that produced by a batch algorithm. Applications of AOSVR in both on-line and cross-validation scenarios are presented.Inbothscenarios, numerical experiments indicate that AOSVR is faster than batch SVR algorithms with both cold and warm start.

Image Denoising Method Based on v-Support Vector Regression and Noise Detection

2013 ◽  
Vol 756-759 ◽  
pp. 4126-4132
Author(s):  
Chang You Wang ◽  
Zhao Long Gao
Keyword(s):  
Support Vector Regression ◽  
Median Filter ◽  
Visual Quality ◽  
Decision Function ◽  
Support Vector ◽  
Noise Detection ◽  
Training Set ◽  
Denoising Method ◽  
Mean Filter ◽  
Filtering Effect

Aimed at the correlation between noise pixels and neighboring pixels, a new method based on the-support vector regression (-SVR) is proposed to remove the salt & pepper noise in corrupted images. The new algorithm first takes a decision whether the pixel under test is noise or not by comparing the block uniformity of the 3x3 window with one of the entire image, secondly adjusts adaptively the size of filtering window which is used to determine the training set according to the number of noise points in the window, thirdly determines the decision function that is used to predict the gray value of the noise pixels by means of training set, finally removes the noises in terms of the decision function based on-SVR. Experimental results clearly indicate that the proposed method has a better filtering effect than the existing methods such as standard mean filter, standard median filter, adaptive median filter by means of visual quality and quanti-tative measures.

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