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。
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.
出处
《计算机应用》
CSCD
北大核心
2006年第5期1122-1124,共3页
journal of Computer Applications
基金
国家科技攻关计划资助项目(2001BA204B0102)
关键词
粒子群
神经网络
油品质量预测
优化
particle swarm
neural network
oil quality prediction
optimization
