|本期目录/Table of Contents|

[1]崔丽洁,程换新,宋生建.基于改进PSO算法的模糊神经网络的研究与应用[J].工业仪表与自动化装置,2018,(05):24-28.[doi:1000-0682（2018）05-0000-00]
　CUI Lijie,CHENG Huanxin,SONG Shengjian.Research and application of fuzzy neural network based on improved PSO algorithm[J].Industrial Instrumentation & Automation,2018,(05):24-28.[doi:1000-0682（2018）05-0000-00]
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基于改进PSO算法的模糊神经网络的研究与应用

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2018年05期
页码:
24-28
栏目:
出版日期:
2018-10-15

文章信息/Info

Title:
Research and application of fuzzy neural network based on improved PSO algorithm
作者:
崔丽洁; 程换新; 宋生建
青岛科技大学 自动化与电子工程学院，山东 青岛 266042
Author(s):
CUI Lijie;  CHENG Huanxin; SONG Shengjian
College of Automation and Electrical Engineering, Qingdao University of Science and Technology, Shandong Qingdao 266042, China
关键词:
模糊神经网络; 粒子群算法; 惯性权重; 水质评价
Keywords:
fuzzy neural network;  partical swarm optimization;  inertia weight;  water quality evaluation
分类号:
TP391.41
DOI:
1000-0682（2018）05-0000-00
文献标志码:
A
摘要:
典型的模糊神经网络在处理复杂的多优化问题时存在收敛速度慢、易陷入局部极值等缺点。粒子群算法是一种全局优化的群体迭代算法，能有效克服传统模糊神经网络算法的不足。该文对粒子群算法的惯性权重及学习因子进行了改进，用其优化模糊神经网络模型参数，并将该模型应用到水质评价中以测试算法性能。通过与传统模糊神经网络的仿真结果比较，该模型精度更高，在水质评价过程中的应用效果更好。
Abstract:
For the complex multi optimization problems, the traditional fuzzy neural network has the disadvantages of slow convergence speed and easy to fall into the local extremum.Particle swarm optimization is a group iterative algorithm,which can effectively overcome the shortcomings of fuzzy neural network.In this paper,the inertia weight and learning factor of particle swarm optimization are improved.The improved particle swarm optimization algorithm is used to optimize the parameters of the fuzzy neural network model,and the model is applied to the water quality evaluation to test algorithm performance.The simulation results show that the model is better than the traditional fuzzy neural network and has higher efficiency in the process of water quality evaluation.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期：2018-03-13
作者简介：崔丽洁(1994)，女，山东临沂人，硕士研究生，研究方向为智能控制理论及应用。
更新日期/Last Update: 2018-10-15