Papers by Keyword: Chaos Particle Swarm Optimization

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Authors: Zhi Bin Xiong
Abstract: This paper proposes a hybrid algorithm based on chaos optimization and particle swarm optimization (PSO) to improve the performance of the neural networks (NN) on evaluating credit risk. The hybrid algorthm not only maintains the advantage of simple structure, but also improves the convergence of the traditional PSO algorithm, and enhances the global optimization capability and accuracy of the algorithm. The test results indicate that the performance of the proposed model is better than the ones of NN model using the BP algorithm and traditional PSO algorithm.
Authors: Chun Xia Cai, Xiang Dong Liu, Ning Dong, Hong Juan Li
Abstract: This paper mainly proposes a chaos particle swarm optimization (CPSO) to estimate the parameters of piezoelectric actuator (PEA), in which Bouc-Wen model is employed to describe the hysteresis phenomenon. By introducing chaotic disturbance to the simple PSO, this method can obtain optimal solution with high calculation efficiency. Finally some experiments are conducted to demonstrate its feasibility and effectiveness.
Authors: Xiao Hong Zhang, Hong Mei Ning
Abstract: Fuzzy C-mean algorithm (FCM) has been well used in the field of color image segmentation. But it is sensitive to initial clustering center and membership matrix, and likely converges into the local minimum, which causes the quality of image segmentation lower. By use of the properties-ergodicity, randomicity of chaos, a new image segmentation algorithm is proposed, which combines the chaos particle swarm optimization (CPSO) and FCM clustering. Some experimental results are shown that this method not only has the ability to prevent the particles to convergence to local optimum, but also has faster convergence and higher accuracy for segmentation. Using the feature distance instead of Euclidian distance, robustness of this method is enhanced.
Authors: Tian Bing Ma, Fei Du
Abstract: A chaos particle swarm algorithm has been used to search for the optimal placement and size of the piezoelectric sensors and actuators (S/As) bonded on smart beams as well as the optimal feedback control gains. The criterion based on the minimization of stored and needed control energy was adopted for the optimization of the control system. The optimal distributions of the piezoelectric patches and feedback control gains based on specific controlled vibration modes have also been put forward. The results showed that the control effect could be significantly improved with optimized distribution of piezoelectric patches and gains.
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