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粒子群算法
    Application of PSO Algorithm in Wavelet Neural Network
    粒子群算法在小波神经网络中的应用
    A Study of Application of An Improved PSO Algorithm in BP Network
    一种改进的粒子群算法在BP网络中的应用研究
    PSO Algorithm Based Optimal Supply Function Model for Power Producer
    基于粒子群算法求解电力市场发电商最优供给函数模型
    Improved PSO Algorithm Study and Its Application on Network Routing
    改进粒子群算法研究及其在网络路由中的应用
    Research on Heat Burden Prediction and Control of Substation Based on PSO Algorithm
    基于粒子群算法热力站供热负荷预测与控制研究
    The experiment results show that the PSO algorithm has better capability for identifying nonlinear system and better generalization ability than the BP algorithm.
    仿真实验结果表明,粒子群算法具有比BP算法更强的非线性系统辨识能力和更好的泛化能力。
    To improve the searching performance of Particle Swarm Optimization (PSO), a modified PSO algorithm with flying time adaptively adjusted was proposed and named FAA-PSO algorithm.
    为改善粒子群优化算法的搜索性能,提出一种飞行时间自适应调整的粒子群算法(FAA-PSO)。
    A PSO algorithm with velocithy mutation operator
    含速度变异算子的粒子群算法
    Steepest-Descent Optimization Swarm Reference Based PSO Algorithm
    基于最速下降最优解参考的粒子群算法
    For general nonlinear optimization with constraints, a neural network model and a revised PSO algorithm are proposed.
    针对一般的带有等式约束和不等式约束的非线性优化问题,提出了神经动力学求解模型,对多模的非线性优化问题改进了粒子群算法以获得更好近似全局最优解。
    The experimental results show that the IPSO algorithm is superior to the conventional PSO algorithm.
    紧接着本文将传统粒子群算法和改进的粒子群算法应用于结构损伤检测领域。
    Particle swarm optimization (PSO) algorithm is a computation intelligence technique, inspired by social behavior of birds flocking. PSO algorithm possesses the advantages of simplified, rather quick convergence speed, global optimization performance, and less controlling parameters, et al.
    粒子群算法是一种源于鸟群捕食行为的计算智能技术,具有算法简单、收敛速度较快、全局优化能力较强、控制参数较少等优点。
    Because of this weak, an improved PSO algorithm, hybrid algorithm of Powell method and particle swarm optimization (PPSO) is proposed.
    针对该算法存在的缺点,提出一种改进的粒子群算法—Powell-粒子群混合算法,将Powell方法有机地融入粒子群算法
    Secondly, a modified PSO algorithm with flying time adaptive adjusted is proposed.
    其次,提出一种飞行时间自适应调整的粒子群算法
    Thirdly, for constrained optimization problems, a new PSO algorithm with two particle swarms is presented.
    再次,对约束优化问题,采用粒子群算法对其进行进化求解;
    Then a new crossed PSO algorithm is proposed. The new algorithm intersects two different PSO algorithms, and the performance of the search process is boosted.
    在此基础上提出一种新的交叉粒子群算法,该算法交叉运行两个不同粒子群算法,提高了算法的搜索性能。
    Furthermore, the stretching technique is used in the neighbor of the local minimum to find the global minimum and another new crossed-stretching PSO algorithm is obtained.
    进一步,在获得局部极小点情形下对函数采用拉伸等措施,给出了交叉-拉伸粒子群算法来搜索全局极小点,该算法也可用来处理多个最优解的问题。
    For improving the predicting results, two improved PSO algorithm are presented also in this paper: Velocity Mutation PSO and hybrid PSO.
    在此基础上,进一步提出了混合粒子群算法和速度变异粒子群算法两种改进算法提高优化性能。
    A new intelligent algorithm , particle swarm optimization (PSO) algorithm, is adopted to solve the reactive power optimization. Optimization results in PSO and GA algorithm are compared to show the superiority of PSO algorithm in this problem.
    本文介绍了一种新的智能算法――粒子群算法,并用它来解决电力系统无功优化问题,所得结果与基于遗传算法的优化结果进行比较,论证了粒子群算法在解决这一问题上的优越性。
    Thirdly, to overcome the disadvantage of easy trapping into local optimal value for the general PSO algorithm, two modified methods of PSO algorithm are presented.
    另外,针对基本粒子群算法容易陷入局部最优值的缺点,提出了两种改进的粒子群算法
 

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