Description: 应用随机微粒群算法学习一个神经网络的权值.网络训练和测试数据采自一实际非线性系统.-Application of stochastic particle swarm optimization learning a neural network weights. Network training and test data collected from a practical nonlinear systems. Platform: |
Size: 3072 |
Author:duyl |
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Description: 应用随机微粒群算法求解一个六元的非线性方程组.-Application of random particle swarm algorithm for a six-million nonlinear equations. Platform: |
Size: 2048 |
Author:duyl |
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Description: 粒子群的优化算法，不仅可以方便地解决无约束优化问题，也可以方便的解决有约束的非线性优化问题。-Particle Swarm Optimization algorithm, not only can easily solve the unconstrained optimization problem can also be convenient to solve constrained nonlinear optimization problem. Platform: |
Size: 5120 |
Author:lxd |
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Description: 用粒子群算法来优化RBF神经网络权值，使神经网络有更好的非线性函数逼近能力-Using particle swarm optimization to optimize the RBF neural network weights, so that neural network has better ability of nonlinear function approximation Platform: |
Size: 3072 |
Author:史峰 |
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Description: 标准粒子群算法求解非线性方程，用MATLAB实现，并出仿真结果-The standard particle swarm algorithm for solving nonlinear equations, using MATLAB to achieve, and the simulation results Platform: |
Size: 5120 |
Author:cathy |
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Description: 基于改进粒子群优化算法的非线性摄像机标定
Non2Linear Camera Calibration Based on
an Imp roved PSO Algorit hm
王德超,涂亚庆-Improved particle swarm optimization based on nonlinear calibration Non2Linear Camera Calibration Based on an Imp roved PSO Algorit hm Wang Chao, Tu Ya Qing Platform: |
Size: 277504 |
Author:姜欣 |
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Description: Particle swarm optimization has been used to solve many optimization problems since it was
proposed by Kennedy and Eberhart in 1995 [4]. After that, they published one book [9] and
several papers on this topic [5][7][13][15], one of which did a study on its performance using
four nonlinear functions adopted as a benchmark by many researchers in this area. In PSO,
each particle moves in the search space with a velocity according to its own previous best
solution and its group’s previous best solution. The dimension of the search space can be any
positive integer. Platform: |
Size: 6144 |
Author:ezuezaimie |
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Description: Particle swarm optimization has been used to solve many optimization problems since it was
proposed by Kennedy and Eberhart in 1995 [4]. After that, they published one book [9] and
several papers on this topic [5][7][13][15], one of which did a study on its performance using
four nonlinear functions adopted as a benchmark by many researchers in this area [14]. In PSO,
each particle moves in the search space with a velocity according to its own previous best
solution and its group’s previous best solution. The dimension of the search space can be any
positive integer. Platform: |
Size: 7168 |
Author:ezuezaimie |
Hits:
Description: Particle swarm optimization has been used to solve many optimization problems since it was
proposed by Kennedy and Eberhart in 1995 [4]. After that, they published one book [9] and
several papers on this topic [5][7][13][15], one of which did a study on its performance using
four nonlinear functions adopted as a benchmark by many researchers in this area [14]. In PSO,
each particle moves in the search space with a velocity according to its own previous best
solution and its group’s previous best solution. The dimension of the search space can be any
positive integer. Platform: |
Size: 4096 |
Author:ezuezaimie |
Hits:
Description: Particle swarm optimization has been used to solve many optimization problems since it was
proposed by Kennedy and Eberhart in 1995 [4]. After that, they published one book [9] and
several papers on this topic [5][7][13][15], one of which did a study on its performance using
four nonlinear functions adopted as a benchmark by many researchers in this area [14]. In PSO,
each particle moves in the search space with a velocity according to its own previous best
solution and its group’s previous best solution. The dimension of the search space can be any
positive integer. Platform: |
Size: 6144 |
Author:ezuezaimie |
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Description: Abstract A particle swarm optimization method with nonlinear
time-varying evolution (PSO-NTVE) is employed in
designing an optimal PID controller for asymptotic stabilization
of a pendubot system. In the PSO-NTVE method,
parameters are determined by using matrix experiments
with an orthogonal array, in which a minimal number of
experiments would have an effect that approximates the full
factorial experiments. Platform: |
Size: 274432 |
Author:med |
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Description: 基于粒子群算法求解低速车辆模型的非线性模型预测控制问题(The nonlinear model predictive control problem of low-speed vehicle is solved based on particle swarm optimization (pso)) Platform: |
Size: 2048 |
Author:L_F |
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Search - nonlinear particle swarm