Application Of Least Squares Support Vector Machine Research Articles

Application of Least-Squares Support-Vector Machine Based on Hysteresis Operators and Particle Swarm Optimization for Modeling and Control of Hysteresis in Piezoelectric Actuators

Nanopositioning systems driven by piezoelectric actuators are widely used in different fields. However, the hysteresis phenomenon is a major factor in reducing the positioning accuracy of piezoelectric actuators. This effect makes the task of accurate modeling and position control of piezoelectric actuators challenging. In this paper, the learning and generalization capabilities of the model are efficiently enhanced to describe and compensate for the rate-independent and rate-dependent hysteresis using a kernel-based learning method. The proposed model is inspired by the classical Preisach hysteresis model, in which a set of hysteresis operators is used to address the problem of mapping, and then least-squares support-vector machines (LSSVM) combined with a particle swarm optimization (PSO) algorithm are used for identification. Two control schemes are proposed for hysteresis compensation, and their performance is evaluated through real-time experiments on a nanopositioning platform. First, an inverse model-based feedforward controller is designed based on the LSSVM model, and then a combined feedback/feedforward control scheme is designed using a classical control strategy (PID) to further enhance the tracking performance. For performance evaluation, different datasets with a variety of hysteresis loops are used during the simulation and experimental procedures. The results show that the proposed method is successful in enhancing the generalization capabilities of LSSVM training and achieving the best tracking performance based on the combination of feedforward control and PID feedback control. The proposed control scheme outperformed the inverse Preisach model-based control scheme in terms of both positioning accuracy and execution time. The control scheme that uses the LSSVM based on nonlinear autoregressive exogenous (NARX) models has significantly less computational complexity compared to our control scheme but at the expense of accuracy.

New Observations on the Application of LS-SVM in Slope System Reliability Analysis

The stability evaluation of earth slopes is a common practice in geotechnical designs. To account for uncertain characteristics of soil properties, probabilistic evaluation that requires repeated calculations of factor of safety (FoS) is inevitably encountered. Because FoS for most slopes in practice is computed numerically due to the lack of analytical solutions, various surrogate models are usually developed to ease the probabilistic evaluation. This paper investigates the probability of slope system failure with surrogate models based on the least-squares support vector machine (LS-SVM) regression. First, some limitations in the current application of LS-SVM to complex slopes with multiple failure modes are pointed out. In the context of Monte Carlo simulations (MCS) for probabilistic slope stability evaluation, the authors first discuss the importance of space filling of training data to the success of the LS-SVM and then propose an efficient routine for generating the training data by which the global prediction of FoS is reasonably guaranteed. The application of the LS-SVM is illustrated through two well-documented slope examples. Comparative studies are conducted to identify the effect of training data size and hyperparameters on the model performance. It is observed from this study that the LS-SVM model can reasonably capture the global characteristics of complex slopes only when all the relevant soil layers are treated probabilistically; otherwise, some local inconsistency could be encountered. Focusing on the probability of failure prediction defined by different FoS thresholds, it is shown that the LS-SVM is robust and a promising method for the evaluation of complex slopes.

Regressive approach for predicting bearing capacity of bored piles from cone penetration test data

In this study, the least square support vector machine (LSSVM) algorithm was applied to predicting the bearing capacity of bored piles embedded in sand and mixed soils. Pile geometry and cone penetration test (CPT) results were used as input variables for prediction of pile bearing capacity. The data used were collected from the existing literature and consisted of 50 case records. The application of LSSVM was carried out by dividing the data into three sets: a training set for learning the problem and obtaining a relationship between input variables and pile bearing capacity, and testing and validation sets for evaluation of the predictive and generalization ability of the obtained relationship. The predictions of pile bearing capacity by LSSVM were evaluated by comparing with experimental data and with those by traditional CPT-based methods and the gene expression programming (GEP) model. It was found that the LSSVM performs well with coefficient of determination, mean, and standard deviation equivalent to 0.99, 1.03, and 0.08, respectively, for the testing set, and 1, 1.04, and 0.11, respectively, for the validation set. The low values of the calculated mean squared error and mean absolute error indicated that the LSSVM was accurate in predicting the pile bearing capacity. The results of comparison also showed that the proposed algorithm predicted the pile bearing capacity more accurately than the traditional methods including the GEP model.

An instrumental least squares support vector machine for nonlinear system identification

Least-Squares Support Vector Machines (LS-SVMs), originating from Statistical Learning and Reproducing Kernel Hilbert Space (RKHS) theories, represent a promising approach to identify nonlinear systems via nonparametric estimation of the involved nonlinearities in a computationally and stochastically attractive way. However, application of LS-SVMs and other RKHS variants in the identification context is formulated as a regularized linear regression aiming at the minimization of the ℓ2 loss of the prediction error. This formulation corresponds to the assumption of an auto-regressive noise structure, which is often found to be too restrictive in practical applications. In this paper, Instrumental Variable (IV) based estimation is integrated into the LS-SVM approach, providing, under minor conditions, consistent identification of nonlinear systems regarding the noise modeling error. It is shown how the cost function of the LS-SVM is modified to achieve an IV-based solution. Although, a practically well applicable choice of the instrumental variable is proposed for the derived approach, optimal choice of this instrument in terms of the estimates associated variance still remains to be an open problem. The effectiveness of the proposed IV based LS-SVM scheme is also demonstrated by a Monte Carlo study based simulation example.

Investment Strategy Optimization Using Technical Analysis and Predictive Modeling in Emerging Markets

This research examines the efficacy of technical analysis and predictive modeling in defining the optimal strategy for investing in the stocks indices of emerging markets. Trading strategies are set regarding different technical indicators based on moving averages and volatility of the value and returns on stock indices. Simple trading rules are generated using two moving averages – a long period and a short period moving average, and Moving Average Convergence-Divergence (MACD) and Relative Strength Index (RSI). Selected technical indicators are used as features in defining predictive model based on Least Squares Support Vector Machines (LS-SVMs). A LS-SVM classifier has been used in order to predict trend of the stock indices’ value whereby the obtained outputs of the LS-SVM model are binary signals that can be used for defining the trading strategy. Comparing the results obtained from traditional statistical methods for predicting the trend of financial series and proposed LS-SVM model, it can be concluded that machine learning techniques capture the non-linear models which are dominant in the financial markets in more adequate way. Outperforming the results of Buy & Hold strategy and technical trading strategies, application of LS-SVM in decision making process on investing on the financial market significantly can contribute to maximization of profitability on investment.

Gasoline Price Forecasting: An Application of LSSVM with Improved ABC

Optimizing the hyper-parameters of Least Squares Support Vector Machines (LSSVM) is crucial as it will directly influence the predictive power of the algorithm. To tackle such issue, this study proposes an improved Artificial Bee Colony (IABC) algorithm which is based on conventional mutation. The IABC serves as an optimizer for LSSVM. Realized in gasoline price forecasting, the performance is guided based on Mean Absolute Percentage Error (MAPE) and Root Mean Square Percentage Error (RMSPE). The conducted simulation results show that, the proposed IABC- LSSVM outperforms the results produced by ABC-LSSVM and also the Back Propagation Neural Network.

Categorization of fabric design using multi-class least-square support vector machine

Purpose – The purpose of this paper is to give an approach for categorization of diverse textile designs using their textural features as extracted from their gray images by means of multi-class least-square support vector machines (LS-SVM). Design/methodology/approach – In this work, the authors endeavor to devise a pattern recognition system based on LS-SVM which performs a multi-class categorization of three basic woven designs namely plain, twill and sateen after analyzing their features. Findings – The result establishes that LS-SVM is able to classify the fabric design with a reasonable degree of accuracy and it outperforms the standard SVM. Originality/value – The algorithmic simplicity of LS-SVM resulting from replacement of inequality constraints by equality ones and ability of handling noisy data by accommodating an error variable in its algorithm make it eminently suitable for textile pattern recognition. This paper offers a maiden application of LS-SVM in textile pattern recognition.

Application of LS-SVM to Prediction of Bearing Capacity of Cement-Flyash-Gravel Pile Composite Foundation

There are a lot of factors that influence the bearing capacity of composite foundation, and the relationship between them is complex and nonlinear. Based on study of main factors that have great influence on bearing capacity of cement-flyash-gravel (CFG) pile composite foundation, the least squares support vector machine (LS-SVM) model of bearing capacity of composite foundation was established. The results show that the model has excellent learning ability and generalization and can provide accurate data prediction only with fewer observed sample. It is proved that the new method is a promising method for the determination of bearing capacity of CFG pile and other rigid piles composite foundation.

Parameters Selection and Application of LS-SVM Based on Chaotic Ant Swarm Algorithm

Parameters selection plays an important role for the performance of least squares support vector machines (LS-SVM). In this paper, a novel parameters selection method for LS-SVM is presented based on chaotic ant swarm (CAS) algorithm. Using this method, the optimization model is established, within which the fitness function is the mean square error (MSE) index, and the constraints are the ranges of the designing parameters. The proposed method is used in the identification for inverse model of the nonlinear systems, and simulation results are given to show the efficiency.