(1. 中南大學(xué) 信息科學(xué)與工程學(xué)院，長(zhǎng)沙 410083；
2. 湖南人文科技學(xué)院 通信與控制工程系，婁底 417000)
摘 要: 針對(duì)鋅凈化除鈷過程生產(chǎn)數(shù)據(jù)存在噪聲和系統(tǒng)參數(shù)緩慢變化的問題, 提出一種基于灰色模糊LSSVM的鈷離子濃度預(yù)測(cè)模型。對(duì)樣本數(shù)據(jù)進(jìn)行灰色累加，削弱原始數(shù)據(jù)序列中的噪聲，使數(shù)據(jù)規(guī)律性增強(qiáng)，灰色累加后數(shù)據(jù)作為L(zhǎng)SSVM輸入，提高模型抗干擾能力和預(yù)測(cè)能力；由于鋅凈化除鈷工序的系統(tǒng)參數(shù)隨時(shí)間發(fā)生變化，提出對(duì)不同時(shí)期的樣本賦予不同的模糊加權(quán)值；利用改進(jìn)PSO的全局優(yōu)化能力和快速收斂性, 優(yōu)化LSSVM模型的懲罰因子和核函數(shù)參數(shù)，避免人為選擇參數(shù)的盲目性。對(duì)硫酸鋅溶液凈化除鈷過程生產(chǎn)數(shù)據(jù)的仿真結(jié)果表明，灰色模糊LSSVM預(yù)測(cè)值能很好地跟蹤實(shí)際值的變化趨勢(shì)，滿足鈷離子濃度預(yù)測(cè)要求。
關(guān)鍵字: 最小二乘支持向量機(jī)；微粒群算法；模糊加權(quán)；灰色累加
（1. School of Information Science and Engineering, Central South University, Changsha 410083, China;
2. Department of Communications & Control Engineering, Hunan Institute of Humanities Science and Technology,
Loudi 417000, China）
Abstract:To solve the problems that noises exist in the data of cobalt removal from zinc electrolyte and the system parameters change slowly, a cobalt ion forecasting model was proposed based on the grey Fuzzy-LSSVM. Grey accumulation is carried out, which weakens the influences of the random disturbance factors in the primary data sequence and strengthens the regularity of the data. Therefore, the anti-interference ability and the predictive ability of the LSSVM model are strengthened when using the grey-accumulated data as the inputs. The system parameters of the purification process have the characteristic of time-varying. So, different fuzzy weighted values are assigned to different samples collected at different times. The two parameters of LSSVM model are optimized by PSO which has the abilities of fast convergence and global optimization, so that the blindness of artificial choice of model parameters can be avoided. The model is applied to the industrial purification process. The experiment on process data in cobalt removal from zinc electrolyte shows that, the grey fuzzy LSSVM algorithm can commendably predict the cobalt ion concentration, which meets the requirement of cobalt ion concentration prediction.
Key words: LSSVM; PSO; fuzzy weighted; grey accumulation
