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粒子群算法
    When dealing with supervised recursive neural networks, this article describes the BPTS training algorithm as well as its improved algorithms and the best PSO algorithm in training forward neural network is also used to train recursive neural networks.
    以此为基础,在有监督的迭代神经网络上,讨论了BPTS训练算法及其改进算法,同时将在前向神经网络训练中表现最好的粒子群算法应用于有监督的迭代神经网络训练,得出了一些有益的结论;
    To improve further the performance of PSO(Particle Swarm Optimization), a modified PSO algorithm is proposed and called CSV-PSO algorithm. Based on the best fitness of the particles, the ranges of both search space and velocity of the particles are contracted dynamically with the evolution of particle swarm in CSV-PSO algorithm.
    为了改善粒子群优化(PSO)算法的搜索性能,提出一种改进的粒子群算法CSV PSO算法·该算法在粒子群进化的过程中根据粒子群的最佳适应值动态地压缩粒子群的搜索空间与粒子群飞行速度范围;
    Simulation study results on fresh temperature in power plants, which is characterized by parameter uncertainty, liable to disturbances and time-lay, show that PSO algorithm is distinguished by its ability of quick searching and of reducing calculation work required, thus providing a very efficient way of optimizing the parameters of fuzzy controllers, and herewith markedly imroving control quality of the fresh steam temperature control system under all loading conditions.
    通过对具有严重参数不确定性、多扰动以及大迟延的电厂主蒸汽温度被控对象的仿真研究,表明粒子群算法寻优速度快,计算量小,对模糊控制器参数的优化设计是非常有效的,使得主汽温控制系统在不同负荷下均获得很好的调节品质。
    This article mainly introduces the advantage of PSO algorithm compared with Genetic Algorithm (GA) system by the experiments.
    论文主要介绍粒子群算法在该系统中的优点,并与其他算法的比较。
    Then taking the Toyota's mixed-model scheduling function as the target function,the adapted discrete PSO algorithm is used to solve that problem,and the satisfactory feasible solutions are achieved.
    然后以丰田公司的汽车组装调度函数作为目标函数,利用改进的离散粒子群算法进行求解。
    Finally,we give numerical examples and the performance analyses of the algorithm. Comparing with the genetic algorithm,we show that the PSO algorithm is effective and practical for solving Nash equilibrium.
    给出了算法的数值例子并分析了该算法的计算性能,通过粒子群算法与遗传算法的比较显示了粒子群算法求解博弈纳什均衡解的高效性.
    This paper offers an Adaptive PSO Algorithm. A fuzzy system is implemented to dynamically adapt the inertia weight of the PSO,here two variables are selected as inputs to the fuzzy system (the current best performance evaluation and the current inertia weight),the output variable is the change of inertia weight.
    在模糊神经网络中采用传统的梯度下降优化方法,其搜索速度慢,并易于陷于局部最小的缺点,提出一种自适应粒子群算法,采用由一个模糊推理机来动态地修改速度参数,模糊推理机的两个输入分别是当前速度参数,以及规范化的当前最好性能估计,输出是速度参数的增量;
    Base on the target frequency characteristics,we establish the optimization model and solve it by high speed PSO algorithm.
    它根据预期频率特性的设计要求,建立优化模型,并通过快速粒子群算法求解其优化值.
    Simulation results show that the proposed algorithm is superior to the general PSO algorithm and two GA-based algorithms in searching for the best solution to the DM problem.
    仿真实验结果表明,本文算法对最优空战决策方案的搜索性能明显优于普通粒子群算法及其他两种遗传算法。
    This paper introduces a conceptual overview and detailed explanation of the PSO algorithm, as well as how it can be used for antenna array design, and presents several results optimized by PSO, which show the abroad application foreground of PSO in the antenna array design.
    本文介绍了粒子群算法的原理和流程,研究了如何将这种方法运用于天线阵的方向图综合上,给出了PSO算法在综合阵列方向图的应用实例,表明粒子群算法在天线阵列综合中具有广泛的应用前景。
    In this paper,we have presented a new PSO algorithm,improved velocity mutation particle swarm optimizer(iPSOVMO).
    论文提出了一种新的PSO算法——改进的速度变异粒子群算法(iPSOVMO)。
    In order to improve its performance,the paper puts forward a hybrid algorithm which blends the PSO algorithm and simulated annealing algorithm.
    为了提高优化性能,将粒子群算法和模拟退火算法结合,得到了粒子群-模拟退火混合调度算法。
    Numerical results for the forward position analysis of in-parallel manipulators show that the PSO algorithm possesses the performances of rather quick convergence speed and high precision.
    数字实例表明,对于并联机构位置正解问题,粒子群算法收敛速度较快,精度较高。
    First, The standard RBF network is introduced and analyzed in this paper. Then the Particle swarm optimization algorithm is introduced and a RBF neural network based on PSO algorithm is proposed to optimize the network parameters. Finally the PSO- RBF network is applied to Coiling Temperature Control (CTC) to predict coiling temperature.
    首先介绍标准RBF神经网络并指出存在的问题,然后介绍粒子群算法并将其用于确定RBF网络参数(连接权,隐节点中心和宽度),最后把PSO-RBF应用到卷取温度控制(CTC)中,对卷取温度进行预测。
    An example shows that,the method effectively releases the difficulty of former PSO algorithm in finding the feasible solution of optimization problem.
    通过算例表明,该算法有效地解决了以往粒子群算法在求解优化问题时难以找到可行解的困难。
    The numerical results of detecting damages at one or multiple sites for a two-storey rigid frame and the convergence curves of PSO algorithms show that the proposed improved PSO algorithm is more effective to solve the constrained optimization problem than the traditional PSO algorithm with an inertia factor.
    两层刚架单损伤和多损伤识别的数值结果和收敛曲线表明了改进后的粒子群算法优于传统的带惯性因子的粒子群算法
    The vast number of experiment results show that the new swarm intelligence algorithm has much stronger global searchingability compared to the classical PSO algorithm.
    通过测试函数的仿真实验证明了量子谐振子粒子群算法的全局收敛能力优于一般粒子群算法
    We solve the upper model by PSO algorithm and lower model by the non-equilibri- urn logit traffic assignment algorithm based on paths generated by the shortest path algorithm.
    模型求解中,上层问题采用粒子群算法,而下层问题则采用路径生成式logit非平衡交通分配算法。
    A simplified off-lattice model of protein is studied with a PSO algorithm. Interaction between atoms is represented by a physical-based potential.
    用粒子群算法对蛋白质的简化模型进行研究,体系中各原子间的相互作用由一物理势表示.
    The design results indicate the improved PSO algorithm is simple and easy to implement,needs fewer adjustment parameters,guarantees global convergence and rapid search for quality optimal solutions,thus producing good effects for the reliability optimal design of the laminated structure of composite materials.
    结果表明,改进粒子群算法不但具备基本算法的简单易实现、需调整参数少的特性,而且能够在确保全局收敛性的基础上,快速搜索到高质量的优化解,对复合材料层合结构的可靠性优化设计十分有效。
 

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