Parameters Of Support Vector Machine Model Research Articles

Prediction of Ultimate Axial Capacity of Square Concrete-Filled Steel Tubular Short Columns Using a Hybrid Intelligent Algorithm

It is crucial to study the axial compression behavior of concrete-filled steel tubular (CFST) columns to ensure the safe operation of engineering structures. The restriction between steel tubular and core concrete in CFSTs is complex and the relationship between geometric and material properties and axial compression behavior is highly nonlinear. These challenges have prompted the use of soft computing methods to predict the ultimate bearing capacity (abbreviated as Nu) under axial compression. Taking the square CFST short column as an example, a mass of experimental data is obtained through axial compression tests. Combined with support vector machine (SVM) and particle swarm optimization (PSO), this paper presents a new method termed PSVM (SVM optimized by PSO) for Nu value prediction. The nonlinear relationship in Nu value prediction is efficiently represented by SVM, and PSO is used to select the model parameters of SVM. The experimental dataset is utilized to verify the reliability of the PSVM model, and the prediction performance of PSVM is compared with that of traditional design methods and other benchmark models. The proposed PSVM model provides a better prediction of the ultimate axial capacity of square CFST short columns. As such, PSVM is an efficient alternative method other than empirical and theoretical formulas.

An improved support vector machine and its application in P2P lending personal credit scoring

With the help of Internet technologies, P2P(Peer-to-Peer) lending industry has witnessed the rapid development of loan market. From the reason presented above, credit assessment becomes more and more important to the healthy development of P2P load marked. In order to improve accurate predictions of credit assessment, there is necessary to a kind of credit risk evaluation model based on SVM(Support Vector Machines). The performance of SVM depends, to a great extent, on parameters we chose, therefore, our prior work is optimize them. This paper employs an IFOA(Improved Fruit Fly Optimization algorithm) to optimize parameters of SVM model and uses modified model to analyze P2P load data. In the article, we analyze data with four different ways (Linear Regression, Classical SVM, FOA-SVM and IFOA-SVM), and results show that the one presented in this paper has better accurate predictions.

Combination of Support Vector Machine and K-Fold cross validation to predict compressive strength of concrete in marine environment

Abstract Support Vector Machine (SVM) model optimized by K-Fold cross validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, Artificial Neural Network (ANN) and Decision Tree (DT) were built to compare the prediction precision with SVM model. The result shows SVM model has the best prediction performance. For the original SVM model, there is a problem that input parameters of SVM model are incomplete. Based on the analysis of an amount of research papers, number of input parameters increases from original 8 to 19. After the complementation of input parameters, the prediction precision of SVM model gets a good promotion. The average relative error declines from 34.8% to 27.6%, and the median relative error is reduced from 24.70% to 20.8%. The number of data with relative errors less than 40% has reached 396, almost 80% of 500 testing data.

Residual life prediction method for remanufacturing sucker rods based on magnetic memory testing and a support vector machine model

It is crucial for remanufacturability to be able to determine the residual life of remanufacturing cores. In the light of weaknesses in the available methods for fatigue damage evaluation and residual life prediction for remanufacturing sucker rods, a novel prediction approach using an optimised support vector machine (SVM) model based on metal magnetic memory (MMM) testing is proposed here. Firstly, tension-tension fatigue experiments on pre-cut groove sucker rod specimens are conducted to investigate the variations in the magnetic memory signals after different numbers of cycles and seven characteristic parameters are extracted to characterise the degree of fatigue damage. Then, a residual life prediction model for remanufacturing sucker rods based on a SVM model is established, where the SVM model parameters, including the radial basis function (RBF) kernel parameter and the penalty factor, are optimised in turn by a genetic algorithm (GA), partical swarm optimisation (PSO) and grid search optimisation (GSO). The results show that the proposed PSO-based approach significantly improves the prediction accuracy, compared with a basic SVM approach, and also yields more stable and accurate results when compared with the GA and the GSO method, providing a new and feasible approach for predicting remaining life.

An Effective ABC‐SVM Approach for Surface Roughness Prediction in Manufacturing Processes

It is difficult to accurately predict the response of some stochastic and complicated manufacturing processes. Data‐driven learning methods which can mine unseen relationship between influence parameters and outputs are regarded as an effective solution. In this study, support vector machine (SVM) is applied to develop prediction models for machining processes. Kernel function and loss function are Gaussian radial basis function and ε‐insensitive loss function, respectively. To improve the prediction accuracy and reduce parameter adjustment time of SVM model, artificial bee colony algorithm (ABC) is employed to optimize internal parameters of SVM model. Further, to evaluate the optimization performance of ABC in parameters determination of SVM, this study compares the prediction performance of SVM models optimized by well‐known evolutionary and swarm‐based algorithms (differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), and ABC) and analyzes ability of these optimization algorithms from their optimization mechanism and convergence speed based on experimental datasets of turning and milling. Experimental results indicate that the selected four evaluation indicators values that reflect prediction accuracy and adjustment time for ABC‐SVM are better than DE‐SVM, GA‐SVM, and PSO‐SVM except three indicator values of DE‐SVM for AISI 1045 steel under the case that training set is enough to develop the prediction model. ABC algorithm has less control parameters, faster convergence speed, and stronger searching ability than DE, GA, and PSO algorithms for optimizing the internal parameters of SVM model. These results shed light on choosing a satisfactory optimization algorithm of SVM for manufacturing processes.

Predicting Freeway Travel Time Using Multiple- Source Heterogeneous Data Integration

Freeway travel time is influenced by many factors including traffic volume, adverse weather, accidents, traffic control, and so on. We employ the multiple source data-mining method to analyze freeway travel time. We collected toll data, weather data, traffic accident disposal logs, and other historical data from Freeway G5513 in Hunan Province, China. Using the Support Vector Machine (SVM), we proposed the travel time predicting model founded on these databases. The new SVM model can simulate the nonlinear relationship between travel time and those factors. In order to improve the precision of the SVM model, we applied the Artificial Fish Swarm algorithm to optimize the SVM model parameters, which include the kernel parameter σ, non-sensitive loss function parameter ε, and penalty parameter C. We compared the new optimized SVM model with the Back Propagation (BP) neural network and a common SVM model, using the historical data collected from freeway G5513. The results show that the accuracy of the optimized SVM model is 17.27% and 16.44% higher than those of the BP neural network model and the common SVM model, respectively.

A Forecast Model of the Number of Containers for Containership Voyage

Container ships must pass through multiple ports of call during a voyage. Therefore, forecasting container volume information at the port of origin followed by sending such information to subsequent ports is crucial for container terminal management and container stowage personnel. Numerous factors influence container allocation to container ships for a voyage, and the degree of influence varies, engendering a complex nonlinearity. Therefore, this paper proposes a model based on gray relational analysis (GRA) and mixed kernel support vector machine (SVM) for predicting container allocation to a container ship for a voyage. First, in this model, the weights of influencing factors are determined through GRA. Then, the weighted factors serve as the input of the SVM model, and SVM model parameters are optimized through a genetic algorithm. Numerical simulations revealed that the proposed model could effectively predict the number of containers for container ship voyage and that it exhibited strong generalization ability and high accuracy. Accordingly, this model provides a new method for predicting container volume for a voyage.

Prediction of wave transmission over submerged reef of tandem breakwater using PSO-SVM and PSO-ANN techniques

ABSTRACT Protection of the damaged breakwater from the high-intensity wave action has become inevitable. Submerged reef can act as a protective structure in reducing the wave action. Further, placed the reef structures on the sea side of a conventional rubble mound breakwater will reduce the effects of wave action. The conventional breakwater and reef structure combination is a tandem breakwater. Keeping in mind the end goal to decrease the complexities associated in model scaling, time constraints and cost in conducting the experiments, an attempt is made to apply soft computing techniques such as an Artificial Neural Network (ANN) and Support Vector Machine (SVM) to model various problems of real case scenario, where mathematical modelling is also difficult. In the present study, Particle Swarm Optimization (PSO) optimizes various parameters of ANN and SVM model in predicting the wave transmission over a submerged reef of the tandem breakwater. The performance of proposed hybrid models such as PSO-ANN and PSO-SVM is evaluated using statistical indices. The results show that PSO-SVM tool performs better in predicting the wave transmission compared to PSO-ANN.

Invasive weed optimization for optimizing one-agar-for-all classification of bacterial colonies based on hyperspectral imaging

Near-infrared hyperspectral imaging together with versatile chemometric algorithms including invasive weed optimization (IWO) were employed for optimizing fast classification of bacterial colonies on agar plates. Hyperspectral images of colonies from six strains of bacteria were collected, and classification models were established by applying partial least squares-discriminant analysis and support vector machine (SVM) on the original as well as difference spectra. The parameters of SVM models were optimized by comparing genetic algorithm, particle swarm optimization and the proposed IWO. The results showed that difference spectra amplified the variations among the spectra of the six strains thus potential for improving classification accuracy. The best full wavelength classification model was IWO-SVM model which produced overall correct classification rates (OCCRs) of 100.0% and 97.0% for calibration and prediction, respectively. Besides, competitive adaptive reweighted sampling (CARS), GA and successive projections algorithm (SPA) were utilized to select important wavelengths to establish simplified models. Among them, the simplified IWO-SVM model based on the feature wavelengths selected by CARS gave the best classification rates of 97.2% and 96.0% for calibration and prediction, respectively. The study demonstrated that IWO was a useful tool for optimizing calibration models thus potential for usage in many other applications.

The prediction of liquid-liquid equilibria for benzene/alkane/ionic liquids mixtures using intelligent models

Liquid-liquid extraction of aromatics from aliphatic hydrocarbons is a main process in petrochemical industry. Therefore, accurate predicting the liquid-liquid equilibria (LLE) for ternary systems of aromatic/alkane/ionic liquid can result in better liquid-liquid extraction. In this study, three intelligence methods including artificial neural network (ANN), support vector machine (SVM) and least square support vector machine (LSSVM) have been applied to predict the thermodynamic phase behavior of LLE for benzene/alkane/ionic liquid ternary systems. Optimization techniques such as particle swarm optimization (PSO), genetic algorithm (GA) and shuffled complex evolution (SCE) have been used to obtain adjustable parameters of SVM and LSSVM models. The results of prediction operation demonstrate that there was good agreement between the estimation of intelligent models and the experimental data of LLE.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction.

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

Automatic inspection and classification for thin-film transistor liquid crystal display surface defects based on particle swarm optimization and one-class support vector machine

Thin-film transistor liquid crystal display surface micro-defects are difficult to be detected using traditional threshold or edge detection methods. This article puts forward a non-destructive detection method using particle swarm optimization with one-class support vector machine to inspect thin-film transistor liquid crystal display surface micro-defects. An image acquisition system is constructed to acquire the surface micro-defects images of thin-film transistor liquid crystal display. Background textures are removed by the image preprocessing algorithm based on one-dimensional discrete Fourier transform. Moreover, the wavelet transform algorithm is used to eliminate the influence of uneven illumination. Effective characteristic parameters describing thin-film transistor liquid crystal display surface micro-defects are selected by the principal component analysis method. Classification model is developed based on one-class support vector machine using radial basis function. To validate the method above, other parameter optimization algorithms, including normal algorithm, genetic algorithm, and grid search algorithm, are used to optimize the support vector machine model parameters: penalty parameter C and kernel parameter g. In contrast, particle swarm optimization is proved to get the optimal model parameter, and the recognition accuracy of 91.7% is obtained from the particle swarm optimization–one-class support vector machine model. The results indicate the proposed system and method can accurately inspect thin-film transistor liquid crystal display surface detects.

Vector Machines Regression Applied in Penicillin Fermentation Process Control

According to the characteristics of biochemical processes, this paper studied the small samples of the data process. Small amount of data can be quickly measured variable modeling. Introduces the support vector machine (SVM) modeling as small sample theory, the least squares support vector machine (LS-SVM) algorithm based on improved SVM applied to the typical fermented penicillin biochemical processes in the past. Online prediction model only off-line testing of important process variables based on the simulation results simulation platform show that the method is only by learning the few batches of sample data. Establish a penicillin product concentration, cell concentration and substrate concentration. This paper analyzes the characteristics of the penicillin fermentation process, and the use of SVM method for modeling, give potency relationship between the factors and its influence. Through experiments, the influence of SVM model parameters to adjust performance. Through a variety of models from the established field data can be found, SVM is better than ANN modeling.

Support Vector Machine Modeling Using Particle Swarm Optimization Approach for the Retrieval of Atmospheric Ammonia Concentrations

This study was performed in order to improve the estimation accuracy of atmospheric ammonia (NH3) concentration levels in the Greater Houston area during extended sampling periods. The approach is based on selecting the appropriate penalty coefficient C and kernel parameter σ 2. These parameters directly influence the regression accuracy of the support vector machine (SVM) model. In this paper, two artificial intelligence techniques, particle swarm optimization (PSO) and a genetic algorithm (GA), were used to optimize the SVM model parameters. Data regarding meteorological variables (e.g., ambient temperature and wind direction) and the NH3 concentration levels were employed to develop our two models. The simulation results indicate that both PSO-SVM and GA-SVM methods are effective tools to model the NH3 concentration levels and can yield good prediction performance based on statistical evaluation criteria. PSO-SVM provides higher retrieval accuracy and faster running speed than GA-SVM. In addition, we used the PSO-SVM technique to estimate 17 drop-off NH3 concentration values. We obtained forecasting results with good fitting characteristics to a measured curve. This proved that PSO-SVM is an effective method for estimating unavailable NH3 concentration data at 3, 4, 5, and 6 parts per billion (ppb), respectively. A 4-ppb NH3 concentration had the optimum prediction performance of the simulation results. These results showed that the selection of the set-point values is a significant factor in compensating for the atmospheric NH3 dropout data with the PSO-SVM method. This modeling approach will be useful in the continuous assessment of NH3 sensor discrete data sources.

The combination of adaptive database SDM and multi-output SVM for eddy current testing

Purpose – Eddy current testing (ECT) is a nondestructive testing method for the detection of flaws that uses electromagnetic induction to find defects in conductive materials. In this method, eddy currents are generated in a conductive material by a changing magnetic field. A defect is detected when there is a disruption in the flow of the eddy current. The purpose of this paper is to develop a new noniterative inversion methodology for detecting degradation (defect characterization) such as cracking, corrosion and erosion from the measurement of the impedance variations. Design/methodology/approach – The methodology is based on multi-output support vector machines (SVM) combined with the adaptive database schema design method (SDM). The forward problem was solved numerically using finite element method (FEM), with its accuracy experimentally verified. The multi-output SVM is a statistical learning method that has good generalization capability and learning performance. FEM is used to create the adaptive database required to train the multi-output SVM and the genetic algorithm is used to tune the parameters of multi-output SVM model. Findings – The results show the applicability of multi-output SVM to solve eddy current inverse problems instead of using traditional iterative inversion methods which can be very time-consuming. With the experimental results the authors demonstrate the accuracy which can be provided by the multi-output SVM technique. Practical implications – The work allows extending the capability of the experimentation ECT defect characterization system developed at LGEP. Originality/value – A new inversion method is developed and applied to ECT defect characterization. This new concept introduces multi-output SVM in the context of ECT. The real data together with estimated one obtained by multi-output SVM model are compared in order to evaluate the effectiveness of the developed technique.

A Novel Feature Extraction Approach Using Window Function Capturing and QPSO-SVM for Enhancing Electronic Nose Performance.

In this paper, a novel feature extraction approach which can be referred to as moving window function capturing (MWFC) has been proposed to analyze signals of an electronic nose (E-nose) used for detecting types of infectious pathogens in rat wounds. Meanwhile, a quantum-behaved particle swarm optimization (QPSO) algorithm is implemented in conjunction with support vector machine (SVM) for realizing a synchronization optimization of the sensor array and SVM model parameters. The results prove the efficacy of the proposed method for E-nose feature extraction, which can lead to a higher classification accuracy rate compared to other established techniques. Meanwhile it is interesting to note that different classification results can be obtained by changing the types, widths or positions of windows. By selecting the optimum window function for the sensor response, the performance of an E-nose can be enhanced.

Development of enhanced groundwater arsenic prediction model using machine learning approaches in Southeast Asian countries

Groundwater contamination with arsenic (As) is one of the major issues in the world, especially for Southeast Asian (SEA) countries where groundwater is the major drinking water source, especially in rural areas. Unfortunately, quantification of groundwater As contamination is another burden for those countries because it requires sophisticated equipment, expensive analysis, and well-trained technicians. Here, we collected approximately 350 groundwater samples from three different SEA countries, including Cambodia, Lao PDR, and Thailand, in an attempt to quantify total As concentrations and conventional water quality variables. After that, two machine learning models (i.e. artificial neural network (ANN) and support vector machine (SVM)) were applied to predict groundwater As contamination using conventional water quality parameters. Prior to modeling approaches, the pattern search algorithm in MATLAB software was used to optimize the ANN and SVM model parameters, attempting to find the best parameters set for modeling groundwater As concentrations. Overall, the SVM showed the superior prediction performance, giving higher Nash–Sutcliffe coefficients than ANN in both the training and validation periods. We hope that the model developed by this study could be a suitable quantification tool for groundwater As contamination in SEA countries.

Uncertainty analysis of support vector machine for online prediction of five-day biochemical oxygen demand

Summary Uncertainty is considered as one of the most important limitations for applying the results of artificial intelligence techniques (AI) in water quality management to obtain appropriate control strategies. In this research, a proper methodology was proposed to determine the uncertainty of support vector machine (SVM) for the prediction of five-day biochemical oxygen demand (BOD5). In this regard, the SVM model was calibrated using different records for many times (here, 1000 times), to investigate model performance according to calibration pattern changes. Therefore, to implement the random selection of calibration patterns for several times, an alternative database was required. By this methodology, the parameters of SVM model will be obtained 1000 times, giving various predicted BOD5 values each time. To evaluate the SVM model’s uncertainty, the percentage of observed data bracketed by 95 percent predicted uncertainties (95PPU) and the band width of 95 percent confidence intervals (d-factor) were selected. Findings indicated that the SVM model was more sensitive to capacity parameter (C) than to kernel parameter (Gamma) and error tolerance (Epsilon). Besides, results showed that the SVM model had acceptable uncertainty in BOD5 prediction. It is notified that the novelty of the presented methodology is beyond a mere application to water resources, and can also be used in other fields of sciences and engineering.

An Evolutionary Optimizer of libsvm Models

This user guide describes the rationale behind, and the modus operandi of a Unix script-driven package for evolutionary searching of optimal Support Vector Machine model parameters as computed by the libsvm package, leading to support vector machine models of maximal predictive power and robustness. Unlike common libsvm parameterizing engines, the current distribution includes the key choice of best-suited sets of attributes/descriptors, in addition to the classical libsvm operational parameters (kernel choice, kernel parameters, cost, and so forth), allowing a unified search in an enlarged problem space. It relies on an aggressive, repeated cross-validation scheme to ensure a rigorous assessment of model quality. Primarily designed for chemoinformatics applications, it also supports the inclusion of decoy instances, for which the explained property (bioactivity) is, strictly speaking, unknown but presumably “inactive”, thus additionally testing the robustness of a model to noise. The package was developed with parallel computing in mind, supporting execution on both multi-core workstations as well as compute cluster environments. It can be downloaded from http://infochim.u-strasbg.fr/spip.php?rubrique178.

Prediction of Classification of Rock Burst Risk Based on Genetic Algorithms with SVM

Due to the complex features of rock burst hazard assessment systems, a support vector machine (SVM) model for predicting of classification of rock burst was established based on the SVM theory and the actual characteristics of the project in this study. The main factors of rock burst, such as coal seam, dip, buried depth, structure situation, change of pitch angle, change of coal thickness, gas concentration, roof management, pressure relief and shooting were defined as the criterion indices for rock burst prediction in the proposed model. In order to determine reasonable and efficient the parameters of SVM, Firstly, the appropriate fitness function for genetic algorithms (GA) operation was determined, and then optimization parameters of SVM model were selected by real coded GA, therefore, the genetic algorithms and support vector machine (GSVM) model was established. A GSVM model was obtained through training 23 sets of measured data, the cross-validation method was introduced to verify the stability of GSVM model and the ratio of mis-discrimination is 0. Moreover, the proposed model was used to predict 12 new samples rock burst, the correct rate of prediction results is 91.6667% and are identical with actual situation. The results show that the genetic algorithm can speed up SVM parameter optimization search, the proposed model has a high credibility in the study of rock burst prediction of risk classification, which can be applied to practical engineering.