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This paper proposes a regrouping particle swarm optimization-based neural network (RegPSONN) for rolling bearing fault diagnosis. The proposed method applied neural network for rolling bearing conditions classification, and regrouping particle swarm optimization (RegPSO) is utilized for network training, and ten time-domain feature parameters are selected to establish the input vector. To evaluate...
In the vehicle routing problem (VRP), it is usually difficult to confederate the cargo and arrange vehicles path. Due to performance reasons, the traditional shortest path algorithm can not be applied to the large scale of VRP. On the basis of the VRP mathematical model, this paper constructs a mixed climbing particle swarm algorithms to solve the problem. First, through coding, the VRP problem is...
The huge increase of hyperspectral data dimensionality and information redundancy has brought high computational cost as well as the over-fitting risk of classification. In this paper, we present an automatic band selection and classification method based on a novel wrapper Multiple Improved particle swarm cooperative optimization and support vector machine model (MIPSO-SVM). The MIPSO-SVM model optimizes...
In this paper, a novel method for improving flexible neural tree is proposed to classify the leukemia cancer data. The hybrid flexible neural tree with pre-defined instruction sets can be created and evolved. The structure and parameter of hybrid flexible neural tree are optimized using probabilistic incremental program evolution (PIPE) and particle swarm optimization (PSO) algorithm. The experimental...
Multi-scale kernel function learning is a special case of multi-kernel learning, namely combines several multi-scale kernels. This approach is more flexible. It provides more comprehensive choice of scale than the mixed kernel learning. In this paper, the model's parameters of multi-scale Gaussian kernel were used as elementary particles. The parameters of multi-scale Gaussian kernel were global optimized...
The particle swarm optimization (PSO) algorithmis a generally used optimal algorithm, which exhibits good performance on optimization problems in complex search spaces. However, traditional PSO model suffers from a local minima, and lacks of effective mechanism to escape from it. This is harmful to its overall performance. This paper presents an improved PSO model called the stochastic perturbing...
The performance of a hydrological model heavily depends on choosing suitable model parameters. A framework for automatic calibration of a hydrological model named the Xinanjiang model with multiobjectives has been presented. In the calibration framework, a MOPSO algorithm was employed to find the non-dominated front in the objective space, and an entropy-based TOPSIS ranking method was used to rank...
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