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.

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.

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.

Overhead Line Fault Location Using Wavelet Packet Decomposition and Support Vector Regression

2012 ◽  
Vol 516-517 ◽  
pp. 1396-1399
Author(s):  
Chun Guo Fei ◽  
Bai Li Su
Keyword(s):  
Transmission Line ◽  
Support Vector Regression ◽  
Fault Location ◽  
Wavelet Packet ◽  
Support Vector ◽  
Wavelet Packet Decomposition ◽  
Overhead Line ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass

This paper realizes the fault location in overhead line by using wavelet packet decomposition (WPD) and support vector regression (SVR). All various types of faults at different locations and various fault inception angles on a 735kV-360km overhead line power system are used. The system only utilizes voltage signals with single-end measurements. WPD is used to extract distinctive features from 1/2 cycle of post fault signals after noises have been eliminated by low pass filter. A SVR is trained with features obtained from WPD and consequently used in precise location of fault on the transmission line. The simulation results show, fault location on transmission line can be determined rapidly and correctly irrespective of fault impedance.

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 A Novel Approach To Diagnosis Of Analog Circuit Incipient Faults Based On KECA And OAO LSSVM

2015 ◽  
Vol 22 (2) ◽  
pp. 251-262 ◽  
Author(s):  
Chaolong Zhang ◽  
Yigang He ◽  
Lei Zuo ◽  
Jinping Wang ◽  
Wei He
Keyword(s):  
Analog Circuit ◽  
Support Vector ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Op Amp ◽  
Novel Approach ◽  
Low Pass ◽  
Diagnosis Accuracy ◽  
Incipient Fault

Abstract Correct incipient identification of an analog circuit fault is conducive to the health of the analog circuit, yet very difficult. In this paper, a novel approach to analog circuit incipient fault identification is presented. Time responses are acquired by sampling outputs of the circuits under test, and then the responses are decomposed by the wavelet transform in order to generate energy features. Afterwards, lower-dimensional features are produced through the kernel entropy component analysis as samples for training and testing a one-against-one least squares support vector machine. Simulations of the incipient fault diagnosis for a Sallen-Key band-pass filter and a two-stage four-op-amp bi-quad low-pass filter demonstrate the diagnosing procedure of the proposed approach, and also reveal that the proposed approach has higher diagnosis accuracy than the referenced methods.

scholarly journals Flood and Drought Prediction Using the Machine Learning Algorithm Support Vector Regression

2019 ◽  
Vol 9 (1) ◽  
pp. 5194-5199
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Predictive Model ◽  
Learning Algorithm ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Predictive Skill ◽  
Drought Prediction ◽  
Rbf Kernel ◽  
Search Approach

Flood and drought are frequently happening natural disasters in most of the countries. These disasters can cause considerable damage to agriculture, ecology and economy of the country. Mitigating the impacts of flood and drought is a valuable help to the human being. The main cause of these disasters is precipitation. If the past precipitation data are analyzed properly, the future flood and drought events can be easily found. Prediction using the Standard Precipitation Index (SPI) is a way to find the wet or dry condition of a region or country. In this paper the SPI values with different lead times are calculated for a long period of time. These SPI indices are analysed by a predictive model using the machine learning algorithm called Support Vector Regression (SVR) with RBF (Radial Basis Function) kernel. In this model the Grid Search approach is used for optimization. The forecast result of this predictive model shows the predictive skill of the SVR-RBF kernel.

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