摘要
对灰色预测算法进行了研究。在GM(1,1)模型中,发展系数a和灰色作用量u是两个关键的参数,对系统的性能有较大的影响。传统的方法使用最小二乘法来求解,不仅计算复杂,而且预测结果的误差也较大。论文对此进行了研究,并提出了一种改进的灰色预测算法PSOGP。PSOGP的主体仍使用GM(1,1)模型,但在求解相关参数时,PSOGP使用了粒子群优化算法。仿真试验表明,与经典的GM(1,1)模型相比,PSOGP算法的预测精度得到了较大的提高。
Grey prediction algorithms are studied in this paper. In GM （1,1） model, development coefficient a and grey action quantity u are two key parameters which have a great impact on prediction system. In traditional methods, these two parameters are obtained by a least squares method with high computation overhead and large prediction error. This problem is discussed in this paper and a grey pre- diction method called PSOGP is proposed. Based on the GM （1, 1） model, PSOGP uses a particle swarm optimization algorithm to solve the two parameters. Simulation results show that, comparing with the classic GM （1,1） model, the accuracy of PSOGP is greatly improved.
出处
《电脑与电信》
2011年第12期43-45,共3页
Computer & Telecommunication
关键词
灰色预测
GM(1
1)模型
粒子群算法
最小二乘法
grey prediction
GM （1, 1） model
particle swarm optimization
least squares method
