引入免疫粒子群優化算法的木材干燥模糊神經網絡控制系統設計1)

張少如 孫麗萍

(東北林業大學，哈爾濱，150040)

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引入免疫粒子群優化算法的木材干燥模糊神經網絡控制系統設計1)

張少如 孫麗萍

(東北林業大學，哈爾濱，150040)

針對木材干燥窯溫濕度控制采用的模糊神經網絡比較依賴于網絡初始權值，且網絡的訓練時間較長、容易陷入非要求的局部極值，采用粒子群優化算法(PSO)的全局尋優性能，設計一種引入免疫PSO算法的木材干燥模糊神經網絡控制系統。為避免PSO算法的早熟和進一步導入待求解問題的先驗知識與經驗，加快算法的全局收斂能力，引入免疫算法的接種疫苗、免疫選擇、良種遷移3種免疫算子。仿真結果表明：溫度和濕度，能更加快速、平滑地到達設定值(溫度需要70 s左右，濕度需要75 s左右)。實例驗證結果表明：溫度曲線均方誤差僅為0.020 7，擬合優度高達0.979 7；濕度曲線均方誤差均在0.3以下，擬合優度均在0.96以上。說明免疫PSO算法具有較高的收斂速度和識別率，對不確定非線性系統具有良好的控制效果。

木材干燥；溫濕度控制；免疫粒子群優化算法；免疫算法；模糊神經網絡

Journal of Northeast Forestry University，2016,44(12)：83-90.

To improve the temperature and humidity control precision of wood drying process, fuzzy neural network control system for wood drying was designed by immune PSO algorithm. According to the overused network with initial weights, the long-time network training and the non-required local extremum in the previous fuzzy neural network of controlling temperature and humidity on lumber kiln, the global particle swarm optimization (PSO) algorithm was adopted. However, in order to avoid earliness of PSO and lead in prior knowledge and experience of unsolved problems, as well as accelerating global convergence of algorithm, three improved immune operator were added, including vaccination, immune selection and fine breed migration. Simulation results show that the temperature and humidity can be more quickly and smoothly reaches the set value (the temperature takes 70 s, and the humidity takes about 75 s). The temperature curve mean square error is 0.020 7, the goodness of fit is 0.979 7, humidity curve mean square errors are below 0.3, and the goodness of fit are above 0.96. This method has higher convergence rate and recognition rate with better control effect on uncertain nonlinear systems.

木材干燥控制是一種大滯后、強耦合的過程，由于控制模型的限制，傳統的建模方法難以實現木材干燥過程的精確數學模型的建立。模糊神經網絡控制是一種不依賴定量模型的控制方法，可用于不確定性和高度非線性的控制對象，可實現木材干燥窯溫濕度的解耦，為干燥過程的精確控制奠定了基礎[1]。目前，國內最為先進的木材干燥控制系統T509A由廣州科凌電氣有限公司研發。……