Application of neural network based on particle swarm algorithm for the prediction of oil quality

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Application of neural network based on particle swarm algorithm for the prediction of oil quality

南京工业大学

Received:2005-11-30 Revised:1900-01-01 Online:2006-05-01 Published:2006-05-01

基于粒子群优化算法的神经网络在油品质量预测中的应用

李方方,赵英凯,贾玉莹

南京工业大学

通讯作者: 李方方

Abstract

Abstract: PSO(particle swarm optimization) algorithm is a kind of stochastic global optimization based on swarm intelligence. Through the interaction of particles, PSO searches the solution space intelligently and finds out the best solution. The advantage of PSO is that it is easy to operate and to achieve. A model integrating PSO and NN (neural network) was established in this paper, which takes full use of the global optimization of PSO and local accurate searching of BP. The example of oil quality prediction shows that PSONN is more efficient and has good generalization.

Key words: particle swarm, neural network, oil quality prediction, optimization

摘要： 粒子群优化算法是基于群体智能的全局优化技术，它通过了粒子间的相互作用，对解空间进行智能搜索，从而发现最优解。其优势在于操作简单，容易实现。文中将粒子群算法和神经网络进行融合，优化神经网络的权值和域值，充分发挥了粒子群算法的全局寻优能力和BP算法的局部搜索优势，并与改进的BP算法进行了比较。油品质量预测的实例表明，将粒子群算法用于神经网络的优化，收敛速度更快,预测精度更高,而且算法简单。

关键词: 粒子群, 神经网络, 油品预测, 优化

;;. Application of neural network based on particle swarm algorithm for the prediction of oil quality[J]. Journal of Computer Applications.

李方方;赵英凯;贾玉莹. 基于粒子群优化算法的神经网络在油品质量预测中的应用[J]. 计算机应用.

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