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Multilayered feed forward neural network based on particle swarmopti mizer algorithm
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作者 潘峰 陈杰 +1 位作者 涂序彦 付继伟 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2005年第3期682-686,共5页
BP is a commonly used neural network training method, which has some disadvantages, such as local minima, sensitivity of initial value of weights, total dependence on gradient information. This paper presents some met... BP is a commonly used neural network training method, which has some disadvantages, such as local minima, sensitivity of initial value of weights, total dependence on gradient information. This paper presents some methods to train a neural network, including standard particle swarm optimizer (PSO), guaranteed convergence particle swarm optimizer (GCPSO), an improved PSO algorithm, and GCPSO-BP, an algorithm combined GCPSO with BP. The simulation results demonstrate the effectiveness of the three algorithms for neural network training. 展开更多
关键词 BP PSO guaranteed convergence particle swarm optimizer gcpso gcpso-BP.
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改进的粒子群算法用于图像分割 被引量:2
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作者 苏彩红 吴菁 朱学峰 《佛山科学技术学院学报(自然科学版)》 CAS 2007年第3期15-18,共4页
提出了一种将保证收敛粒子群算法与最大类间方差法相结合的快速阈值分割法。该方法根据最大类间方差法的原理以分离度大小作为判断粒子优劣的准则,即分离度越大粒子就越好,并采用粒子群算法对图像进行多目标优化搜索。实验表明,该算法... 提出了一种将保证收敛粒子群算法与最大类间方差法相结合的快速阈值分割法。该方法根据最大类间方差法的原理以分离度大小作为判断粒子优劣的准则,即分离度越大粒子就越好,并采用粒子群算法对图像进行多目标优化搜索。实验表明,该算法在继承标准粒子群算法易于实现、实时性好等优点的同时,还避免了标准PSO算法存在的早熟收敛问题,具有更强的寻优能力。 展开更多
关键词 GCPS0 最大类间方差法 阈值分割
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Comprehensive optimization design of aerodynamic and electromagnetic scattering characteristics of serpentine nozzle 被引量:4
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作者 Yubo HE Qingzhen YANG Xiang GAO 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2021年第3期118-128,共11页
Comprehensive optimization design of serpentine nozzle with trapezoidal outlet was studied to improve its aerodynamic and electromagnetic scattering performance.Serpentine nozzles with different center offsets and dif... Comprehensive optimization design of serpentine nozzle with trapezoidal outlet was studied to improve its aerodynamic and electromagnetic scattering performance.Serpentine nozzles with different center offsets and different ratios of the bases of the trapezoidal outlet were generated based on curvature control regulation.Computational Fluid Dynamics(CFD)simulations have been conducted to obtain the flow field in the nozzle,and Forward-Backward Iterative Physical Optics(FBIPO)method was applied to study the electromagnetic scattering characteristics of the nozzle.Guarantee Convergence Particle Swarm Optimization(GCPSO)algorithm based on Radial Basis Function(RBF)neural network was used to optimize the geometry of the nozzle in consideration of its aerodynamic and electromagnetic scattering characteristics.The results show that the GCPSO method based on RBF can be used to optimize the aerodynamic characteristics of the internal flow and the scattering characteristics of the cavity of the serpentine nozzle with irregular outlet.The optimized model has a higher center offset and a lower ratio of the bases of the trapezoidal outlet after optimization compared to the original model.The optimized model leads to a slight change in aerodynamic performance,with a total pressure recovery coefficient increase of 0.31%and a discharge coefficient increase of 0.41%.In addition,the Radar Cross Section(RCS)decreases also by around 83.33%and the overall performance is significantly improved,with a decrease of the optimized objective function by around 38.74%. 展开更多
关键词 Forward-Backward Iterative Physical Optics(FBIPO) Guarantee Convergence Particle Swarm Optimization(gcpso) Nozzle design Optimization design Radar Cross Section(RCS) Serpentine nozzle
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