IRIS flower data is a class of multi variable data set, which is widely applied in data classification. This paper aims at the parameter optimization problem of least squares support vector machine (LS-SVM) in data classification, an improved particle swarm optimization(IMPSO) algorithm is introduced into the LS-SVM model for improving the learning performance and generalization ability of LS-S...

Flow number of asphalt–aggregate mixtures as an explanatory factor has been proposed in order to assess the rutting potential of asphalt mixtures. This study proposes a multiple–kernel based support vector machine (MK–SVM) approach for modeling of flow number of asphalt mixtures. The MK–SVM approach consists of weighted least squares–support vector machine (WLS–SVM) integrating two kernel funct...

Indoor positioning using location fingerprints, which are received signal strength (RSS) from wireless access points (APs), has become a hot research topic during the last a few years. Traditional pattern classification based fingerprinting localization methods suffer high computational burden and require a large number of classifiers to determine the object location. To handle this problem, ax...

Emotion, as a psychophysiological state, plays an important role in human communications and daily life. Emotion studies related to the physiological signals are recently the subject of many researches. In This study a hybrid feature based approach was proposed to examine affective states. To this effect, Electrocardiogram (ECG) signals of 47 students were recorded using pictorial emotion elici...

To meet the demand for the early location of fire in large-span space buildings, an accurate fire location method is proposed based on machine vision technology. A nonlinear implicit camera calibration method is proposed by combining an improved particle swarm optimization (PSO) method with least squares support vector machine (LS-SVM) to solve the problem that it is difficult to establish accu...

Support vector machines have been very successful in pattern recognition and function estimation problems. In this paper we introduce the use of least squares support vector machines (LS-SVM's) for the optimal control of nonlinear systems. Linear and neural full static state feedback controllers are considered. The problem is formulated in such a way that it incorporates the N-stage optimal con...

In this paper we study nonlinear ARX models in relation to a class of kernel based models which make use of kernel induced feature spaces, a methodology which is common in the area of support vector machines (SVMs). Methods are proposed for extending the use of least squares support vector machine (LS-SVM) models towards a robust setting. In order to understand the robustness of these estimator...

Gas concentration, which is a chaotic time series in essence, is a key factor of the coal mine safety. An accurate forecast of gas concentration is required to guarantee safety and has very highly social and economic benefits. Least squares support vector machine (LS-SVM) has been receiving increasing interest in areas ranging from its original application in pattern recognition to other applic...

In order to solve the problems of low integration, reliability, and high cost caused by mechanical sensors used in bearingless permanent magnet synchronous motor (BPMSM) control systems, a novel speed displacement sensorless method using least-squares support vector machine (LS-SVM) left inverse system is proposed this paper. Firstly, suspension force generation principle BPMSM introduced, math...

Kernel Logistic Regression (KLR) is one of the statistical models that have been proposed for classification in the machine learning and data mining communities, and also one of the effective methodologies in the kernel-machine techniques. The parameters of KLR model are usually fitted by the solution of a convex optimization problem that can be found using the well known Iteratively Reweighted...