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The problem in parameter selection of least squares support vector machine (LS-SVM) restricts the development of LS-SVM, In order to choose the optimal parameters of LS-SVM automatically, we proposed an improved particle swarm optimization (PSO) algorithm which can not only increase the convergent speed but also improve the overall searching ability of the algorithm. The improved PSO algorithm can...
The prediction model of indoor thermal comfort PMV index based on least squares support vector machine (LS-SVM) is established by using the nonlinear relationship between human thermal comfort and its influencing factors and the characteristic that particle swarm has of fast global optimization. Adopting the parameters of least squares support vector machine optimized by Particle Swarm algorithm,...
Fault diagnosis of blast furnace is a hot topic and has a very important practical significance and value. At the same time, rapid diagnosis of blast furnace fault is a difficult problem. In this paper, a novel strategy based on CLS-SVM is proposed to solve this problem. A modified discrete particle swarm optimization is applied to optimize the feature selection and the LS-SVM parameters. Fitness...
Intelligent heuristic algorithms have been paid more and more attention in solving large-scale, complex optimization problems. Membrane computing is a new branch of natural computing with the features of distribution and great parallelism. PSO is also a simple and effective intelligent computing method. Considering the features of membrane computing and PSO, a hybrid algorithm MCBPSO is proposed in...
Ship motion prediction is essential for the safety of shipboard helicopter. If roll/pitch/heave exceeds some prescribed operating limit, potential crashes may occur. In order to prolong the prediction length, a hybrid algorithm based on particle swarm optimization and simulated annealing (HPSO) is proposed to choose the parameters of least square support vector machine (LSSVM). The HPSO-LSSVM method...
According to process requirements of Roller-hearth Normalizing Furnace, and non-linear characteristics of the temperature, the paper proposes a new nonlinear system prediction control algorithm instead of the tradition, which the accuracy of model is not high. The new control algorithm uses least squares support vector machine (LSSVM) optimized by improved particle swarm optimization (APSO) to establish...
In accordance with optimization control and modeling of polymyxin fermentation process, least square support vector machine(LSSVM) model was established, and a method was proposed to find the better parameter value by using quantum-behaved particle swarm optimization(QPSO) which has better search ability. The QPSO-LSSVM model was trained and tested with polymyxin fermentation data-set. The results...
The paper works out an online self-learning control plant for multiple support vector machine inverse control. The multiple support vector machine model applies subtractive clustering algorithm by which the input space is divided into several small local spaces. By means of least squares support vector machine, the sub-models are established. The prediction output of each sub-model is connected by...
In efficiency analysis of weapon system, in order to capture and represent the decision maker's preferences and then to select the most desirable alternative, sensitivity analysis method of operational effectiveness based on LS-SVM is proposed. Firstly, the principle of effectiveness evaluation method based on LS-SVM is discussed. Secondly, to extract learning samples from the MADM problem, an approach...
To improve the training efficiency of least squares-support vector machine (LS-SVM) method, a new algorithm was proposed for developing the multivariate regression model using near-infrared (NIR) spectra and named as PCA-PSO-LS-SVM. Coupled with principal component analysis (PCA) and particle swarm optimization (PSO), this algorithm can take advantage of spectral dimension reduction and parameter...
To improved the prediction accuracy of the flow stress, a hybrid model based on the Hybrid Least Squares Support Vector Machine (HLS-SVM) and Mathematical Models (MM) was proposed. In HLS-SVM model, the optimal parameters of LS-SVM were obtained by self-adaptive Particle Swarm Optimization (PSO)based on Simulated Annealing (SA). Simulation experiment results revealed that this model could correctly...
Classification problem is an important and complex problem in machine learning. Support vector machine (SVM) has recently emerged as a powerful technique for solving problems in classification, but its performance mainly depends on the parameters selection of it. Parameters selection for SVM is very complex in nature and quite hard to solve by conventional optimization techniques such as least Squares...
Reducing dimension processing is needed in feature samples because the repeated and secondary features would reduce the classification ability and increase computation complexity. In this paper, a feature selection method, named MPSO (Modified Particle Swarm Optimization), is proposed. The original group velocity of a particle swarm was changed into two separate and parallel particle swarm velocity,...
This paper deals with the application of least squares support vector regression (LS-SVR) with radial basis function (RBF) kernel in dam crack forecasting. In the process of LS-SVR, we performed the standard grid search and particle swarm optimization (PSO) to tune hyperparameters of LS-SVR. The results demonstrate that our PSO approach can identify optimal or near optimal parameters faster than the...
In efficiency analysis of weapon system, in order to achieve and represent the preferences of decision maker and make the most optimum selection, an efficiency evaluation method of weapon equipment based on LS-SVM with parameters optimization is proposed. Firstly, the principle of efficiency evaluation method based on LS-SVM is discussed. Secondly, to learn samples from the MADM problem extractly,...
In order to overcome the deficiencies of artificial neural networks (ANN), such as low convergence rate, local optimal solution, over-fitting and difficult determination of structure, a proposed QPSO-LS-SVMs method is applied to fault diagnosis of power transformer. It takes five characteristic gases dissolved in transformer oil as its inputs and seven transformer states as its outputs, constructs...
The synchronization error of feed axis has direct relation with contour error in interpolation motions. Recognition of synchronization error of dual linear motor driven axis with gantry frame has an important role in compensating the error to avoid the effect of contour precision. In the contribution, a combination of Particle Swarm Optimization (PSO) algorithm and Least Squares Support Vector Regression...
The least squares support vector machine (LSSVM) use quadratic loss function to replace the non-sensitive loss function and equality constraints to replace inequality constraints. LSSVM is widely used in pattern recognition and function regression, but its performance mainly depends on the parameters selection of it. Kernel parameter selection is very important, and which decide the fault diagnosis...
In order to improve the pressure sensor's current stability and temperature drift performance, a soft sensor regression model was modeled based on least square support vector machine (LS-SVM). According to the difficulty in selecting penalty factor and kernel parameter which are called hyper-parameters in LS-SVM when modeling, particle swarm optimization (PSO) algorithm and ergodicity search algorithm...
The evaluation of competitive power is very important for bidder in power system, how to improve the accuracy and efficiency of evaluation is the keystone people pay attention to, and many researches have been done around it. A combined model of least squares support vector machines optimized by an improved particle swarm optimization algorithm is proposed in this paper to do evaluate the competitive...
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