系统工程与电子技术
繫統工程與電子技術
계통공정여전자기술
SYSTEMS ENGINEERING AND ELECTRONICS
2010年
2期
321-325
,共5页
灰色预测%GM(1,1)模型%粒子群算法%参数辨识
灰色預測%GM(1,1)模型%粒子群算法%參數辨識
회색예측%GM(1,1)모형%입자군산법%삼수변식
grey forecasting%GM (1,1) model%particle swarm optimization (PSO)%parameter identification
针对普通GM(1,1)模型应用于非平缓变化序列预测时误差较大甚至失效的缺陷,提出了一种内涵式参数辨识的GM(1,1)新模型.推导了模型边值、背景值权重系数、发展系数以及灰作用量与预测值之间的非线性内涵表达式,并采用粒子群算法(particle swarm optimization, PSO)对内涵式参数进行辨识,建立了PSOGM(1,1)预测新模型.典型算例表明,PSOGM(1,1)模型收敛速度快,较普通GM(1,1)模型具有更高的预测精度,可应用于平缓变化及非平缓变化序列预测.
針對普通GM(1,1)模型應用于非平緩變化序列預測時誤差較大甚至失效的缺陷,提齣瞭一種內涵式參數辨識的GM(1,1)新模型.推導瞭模型邊值、揹景值權重繫數、髮展繫數以及灰作用量與預測值之間的非線性內涵錶達式,併採用粒子群算法(particle swarm optimization, PSO)對內涵式參數進行辨識,建立瞭PSOGM(1,1)預測新模型.典型算例錶明,PSOGM(1,1)模型收斂速度快,較普通GM(1,1)模型具有更高的預測精度,可應用于平緩變化及非平緩變化序列預測.
침대보통GM(1,1)모형응용우비평완변화서렬예측시오차교대심지실효적결함,제출료일충내함식삼수변식적GM(1,1)신모형.추도료모형변치、배경치권중계수、발전계수이급회작용량여예측치지간적비선성내함표체식,병채용입자군산법(particle swarm optimization, PSO)대내함식삼수진행변식,건립료PSOGM(1,1)예측신모형.전형산례표명,PSOGM(1,1)모형수렴속도쾌,교보통GM(1,1)모형구유경고적예측정도,가응용우평완변화급비평완변화서렬예측.
Aiming to overcome the disadvantage for the common GM (1,1) model of badly forecasting results to those non-smooth variation sequences, a new GM (1,1) model with parameters identification method for the intension expression is proposed. The intension expression, describing the nonlinear relations between developing coefficient, the grey input, the background weight parameter, and the boundary-value and forecasting value, are deduced. Then the particle swarm optimization (PSO) algorithm is adopted to identify internal parameters of the intension expression, thus the PSOGM (1,1) model is founded. The typical numerical examples demonstrate that the PSOGM (1,1) model can provide fast convergence rate, and has better-predicted precision than common GM (1,1) model. Moreover, the proposed model is comfortable not only for smooth variation sequences, but also for non-smooth variation sequences.
