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The nuclear function parameter and penalty parameter is a pivotal factor which decides performance of Least Squares Support Vector Machines (LSSVM). Common used parameters selection method for LSSVM is cross-validation, which is complicated calculation and takes a very long time. To solve these problems, a new approach based on an adaptive genetic algorithm (AGA) was proposed, which automatically...
In this paper, we propose a hybrid cost controller to deal with complicated attributes and high controlling risk of construction cost based on fuzzy least squares support vector machines. In this controller, fuzzy membership and least squares support vector machines are combined together. Considering the specificity of project sample data, complex fuzzy membership function is employed to improve their...
This paper applies quadratic Renyi entropy to enterprise financial distress prediction and puts forward a learning algorithm of least squares support vector machines (LS-SVM) based on quadratic Renyi entropy. By respectively analysis and comparison of the algorithm with the traditional LS-SVM, the standard SVM, MLR and BP-ANN, we can see that this algorithm is significantly superior to other algorithms...
Least squares support vector machines (LS-SVM) based on a group of different kernel functions (Linear-Polynomial-Radial Basis Function- Exponential Radial Basis Function) for modeling nonlinear systems is introduced in this paper. A method for selecting the hyperparameters of LS-SVM is presented in details for both the regularization parameter (??) and the width parameter (??). To test the validity...
In this paper, a novel learning method based on kernelized fuzzy clustering and least squares support vector machines (LSSVM) is presented to improve the generalization ability of a Takagi-Sugeno-Kang (TSK) fuzzy modeling. Firstly, the fuzzy partition of the product space of input and output is obtained by kernelized fuzzy clustering. Then, a computationally efficient numerical method is proposed...
Dissolved gas analysis (DGA) is essential to the fault diagnosis of oil-immersed power transformer. After thoroughly analyzing the gas production mechanism of power transformer faults, it has been found that there are no explicit mapping functions between the single fault of power transformer and the content of gas. To handle this problem, a multi-class classification model for power transformer fault...
Accurate traffic parameters such as traffic flow, travel speeds and occupancies, are crucial to effective management of intelligent transportation systems (ITS). Some traffic data from loop detectors settled in arterial streets are incomplete, and the importance of effectively imputing the missing values emerges. In this paper, a technique called least squares support vector machines (LS-SVMs) is...
The selection for hyper-parameters including kernel parameters and the regularization is important to the performance of least squares support vector machines (LS-SVM). The existed parameters selection algorithms, such as the analytical, algebraic techniques and particle swarm optimization (PSO) algorithm, have their own shortcomings. In this paper, the problem of model selection for LS-SVM is discussed...
Support vector machines (SVM) can overcome the disadvantage of traditional anomaly detection, which need large sample data and have great effect in real-time detection, but has the disadvantage of slow training velocity. Least squares support vector machines (LS-SVM) can overcome the disadvantage of slow training velocity, but makes the solution lose sparsity and robustness. So a weighted LS-SVM (WLS-SVM)...
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