This paper studies the effect of amplitude-phase errors on the antenna performance. Via builting on a worst-case error tolerance model, a simple and practical worst error tolerance analysis based on the chaos-genetic ...This paper studies the effect of amplitude-phase errors on the antenna performance. Via builting on a worst-case error tolerance model, a simple and practical worst error tolerance analysis based on the chaos-genetic algorithm （CGA） is proposed. The proposed method utilizes chaos to optimize initial population for the genetic algorithm （GA） and introduces chaotic disturbance into the genetic mutation, thereby improving the ability of the GA to search for the global optimum. Numerical simulations demonstrate that the accuracy and stability of the worst-case analysis of the proposed approach are superior to the GA. And the proposed algorithm can be used easily for the error tolerant design of antenna arrays.展开更多
Permanent magnet synchronous motor (PMSM) is widely used in mining, and there exists chaotic behav- ior when it runs. In order to dispel its adverse effect on security in mining, the chaotic system of PMSM was analyze...Permanent magnet synchronous motor (PMSM) is widely used in mining, and there exists chaotic behav- ior when it runs. In order to dispel its adverse effect on security in mining, the chaotic system of PMSM was analyzed. With noise disturbances, the complex dynamic characteristics of chaos were also analyzed, and proved the objective existence of chaos. As we all know, it is very difficult for conventional PMSM control to meet the design requirements, therefore, in order to ensure the robustness of the system, the chaotic orbits were stabilized to arbitrary chosen fixed points and periodic orbits by means of sliding mode method. Finally MATLAB simulations were presented to confirm the validity of the controller. The results show that the PMSM with the sliding mode control has a good dynamic performance and steady state accuracy.展开更多
In order to raise the detection precision of the extended binary phase shift keying （EBPSK） receiver, a detector based on the improved particle swarm optimization algorithm （IMPSO） and the BP neural network is des...In order to raise the detection precision of the extended binary phase shift keying （EBPSK） receiver, a detector based on the improved particle swarm optimization algorithm （IMPSO） and the BP neural network is designed. First, the characteristics of EBPSK modulated signals and the special filtering mechanism of the impacting filter are demonstrated. Secondly, an improved particle swarm optimization algorithm based on the logistic chaos disturbance operator and the Cauchy mutation operator is proposed, and the EBPSK detector is designed by utilizing the IMPSO-BP neural network. Finally, the simulation of the EBPSK detector based on the MPSO-BP neural network is conducted and the result is compared with that of the adaptive threshold-based decision, the BP neural network, and the PSO-BP detector, respectively. Simulation results show that the detection performance of the EBPSK detector based on the IMPSO-BP neural network is better than those of the other three detectors.展开更多
基金supported by the National Natural Science Foundation of China (60901055)
文摘This paper studies the effect of amplitude-phase errors on the antenna performance. Via builting on a worst-case error tolerance model, a simple and practical worst error tolerance analysis based on the chaos-genetic algorithm （CGA） is proposed. The proposed method utilizes chaos to optimize initial population for the genetic algorithm （GA） and introduces chaotic disturbance into the genetic mutation, thereby improving the ability of the GA to search for the global optimum. Numerical simulations demonstrate that the accuracy and stability of the worst-case analysis of the proposed approach are superior to the GA. And the proposed algorithm can be used easily for the error tolerant design of antenna arrays.
基金supported in part by the National Natural Science Foundation of China (No. 50879072)the Fundamental Research Funds for the Central Universities of CUMT (No.2010QNB33)The National Undergraduate Innovation Programof CUMT (No. 101029013)
文摘Permanent magnet synchronous motor (PMSM) is widely used in mining, and there exists chaotic behav- ior when it runs. In order to dispel its adverse effect on security in mining, the chaotic system of PMSM was analyzed. With noise disturbances, the complex dynamic characteristics of chaos were also analyzed, and proved the objective existence of chaos. As we all know, it is very difficult for conventional PMSM control to meet the design requirements, therefore, in order to ensure the robustness of the system, the chaotic orbits were stabilized to arbitrary chosen fixed points and periodic orbits by means of sliding mode method. Finally MATLAB simulations were presented to confirm the validity of the controller. The results show that the PMSM with the sliding mode control has a good dynamic performance and steady state accuracy.
基金The National Natural Science Foundation of China (No.60872075)the National High Technology Research and Development Program of China (863 Program) (No. 2008AA01Z227)
文摘In order to raise the detection precision of the extended binary phase shift keying （EBPSK） receiver, a detector based on the improved particle swarm optimization algorithm （IMPSO） and the BP neural network is designed. First, the characteristics of EBPSK modulated signals and the special filtering mechanism of the impacting filter are demonstrated. Secondly, an improved particle swarm optimization algorithm based on the logistic chaos disturbance operator and the Cauchy mutation operator is proposed, and the EBPSK detector is designed by utilizing the IMPSO-BP neural network. Finally, the simulation of the EBPSK detector based on the MPSO-BP neural network is conducted and the result is compared with that of the adaptive threshold-based decision, the BP neural network, and the PSO-BP detector, respectively. Simulation results show that the detection performance of the EBPSK detector based on the IMPSO-BP neural network is better than those of the other three detectors.
