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| 1 | Construction of compact RBF network by refining coarse clusters and widths显示文摘It is known that centers, widths, and weights are three mainly considered factors in constructing a radial basis function(RBF) network.This paper aims at constructing a compact RBF network with two main steps.In the first step, the coarse clusters computed from triangle inequalities are refined to obtain the locations of centers by the defined maximum degree spanning tree(MDST).Meanwhile the coarse widths are obtained.In the second step, a learning algorithm referred to as anisotropic gradient descent method is presented to further refine the above coarse widths.Experiments of the proposed algorithm show its great performance in times series prediction and classification. | Zeng Delu Zhou Zhiheng Xie Shengli | 2009 | Journal of Systems Engineering and Electronics2009，20，6： | 1 |
| 2 | RBF neural network and active circles based algorithm for contours extraction显示文摘For the contours extraction from the images, active contour model and self-organizing map based approach are popular nowadays. But they are still confronted with the problems that the optimization of energy function will trap in local minimums and the contour evolutions greatly depend on the initial contour selection. Addressing to these problems, a contours extraction algorithm based on RBF neural network is proposed here. A series of circles with adaptive radius and center is firstly used to search image feature points that are scattered enough. After the feature points are clustered, a group of radial basis functions are constructed. Using the pixels’ intensities and gradients as the input vector, the final object contour can be obtained by the predicting ability of the neural network. The RBF neural network based algorithm is tested on three kinds of images, such as changing topology, complicated background, and blurring or noisy boundary. Simulation results show that the proposed algorithm performs contours extraction greatly. | Zhou Zhiheng Zeng Delu Xie Shengli | 2007 | Progress in Natural Science:Materials International2007，17，6： | 0 |
| 3 | Vector bundle constraint for particle swarm optimization and its application to active contour modeling显示文摘Active contour modeling (ACM) has been shown to be a powerful method in object boundary extraction. In this paper, a new ACM based on vector bundle constraint for particle swarm optimization (VBCPSO-ACM) is proposed. Different from the traditional particle swarm optimization (PSO), in the process of velocity update, a vector bundle is predefined for each particle and velocity update of the particle is restricted to its bundle. Applying this idea to ACM, control points on the contour are treated as particles in PSO and the evolution of the contour is driven by the particles. Meanwhile, global searching is shifted to local searching in ACM by decreasing the number of neighbors and inertia. In addition, the addition and deletion of particles on the active contour make this new model possible for representing the real boundaries more precisely. The proposed VBCPSO-ACM can avoid self-intersection during contour evolving and also extract inhomogeneous boundaries. The simulation results proved its great performance in performing contour extraction. | Zeng Delu Zhou Zhiheng Xie Shengli | 2007 | Progress in Natural Science:Materials International2007，17，10： | 0 |
| 4 | An Adaptive Hybrid Optimization Strategy for Resource Allocation in Network Function Virtualization显示文摘With the rapid development of Network Function Virtualization(NFV),the problem of low resource utilizationin traditional data centers is gradually being addressed.However,existing research does not optimize both localand global allocation of resources in data centers.Hence,we propose an adaptive hybrid optimization strategy thatcombines dynamic programming and neural networks to improve resource utilization and service quality in datacenters.Our approach encompasses a service function chain simulation generator,a parallel architecture servicesystem,a dynamic programming strategy formaximizing the utilization of local server resources,a neural networkfor predicting the global utilization rate of resources and a global resource optimization strategy for bottleneck andredundant resources.With the implementation of our local and global resource allocation strategies,the systemperformance is significantly optimized through simulation. | Chumei Wen Delu Zeng | 2024 | Computer Modeling in Engineering & Sciences2024，138，2： | 0 |
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