TY - GEN
T1 - Particle swarm learning algorithm based on adjustment of parameter and its applications assessment of agricultural projects
AU - Yang, Shanlin
AU - Zhu, Weidong
AU - Chen, Li
PY - 2009
Y1 - 2009
N2 - The particle swarm, which optimizes neural networks, has overcome its disadvantage of slow convergent speed and shortcoming of local optimum. The parameter that the particle swarm optimization relates to is not much. But it has strongly sensitivity to the parameter. In this paper, we applied PSO-BP to evaluate the environmental effect of an agricultural project, and researched application and Particle Swarm learning algorithm based on adjustment of parameter. This paper, we use MATLAB language .The particle number is 5, 30, 50, 90, and the inertia weight is 0.4, 0.6, and 0.8 separately. Calculate 10 times under each same parameter, and analyze the influence under the same parameter. Result is indicated that the number of particles is in 25 ∼ 30 and the inertia weight is in 0.6 ∼ 0.7, and the result of optimization is satisfied.
AB - The particle swarm, which optimizes neural networks, has overcome its disadvantage of slow convergent speed and shortcoming of local optimum. The parameter that the particle swarm optimization relates to is not much. But it has strongly sensitivity to the parameter. In this paper, we applied PSO-BP to evaluate the environmental effect of an agricultural project, and researched application and Particle Swarm learning algorithm based on adjustment of parameter. This paper, we use MATLAB language .The particle number is 5, 30, 50, 90, and the inertia weight is 0.4, 0.6, and 0.8 separately. Calculate 10 times under each same parameter, and analyze the influence under the same parameter. Result is indicated that the number of particles is in 25 ∼ 30 and the inertia weight is in 0.6 ∼ 0.7, and the result of optimization is satisfied.
KW - agricultural projects measurement
KW - parameter
KW - the particle swarm optimization
UR - https://www.scopus.com/pages/publications/85099734224
U2 - 10.1007/978-1-4419-0211-5_66
DO - 10.1007/978-1-4419-0211-5_66
M3 - 会议稿件
AN - SCOPUS:85099734224
SN - 9781441902108
T3 - IFIP International Federation for Information Processing
SP - 1379
EP - 1388
BT - Computer and Computing Technologies In Agriculture II, Volume 2
PB - Springer Science and Business Media, LLC
ER -