Fuzzy control fit the control of nonlinear , time lag system . it especially fits the control of neutralization process . so fuzzy control is employed in the control system as a feed forward control 模糊控制完全是在操作人員控制經(jīng)驗(yàn)的基礎(chǔ)上實(shí)現(xiàn)對(duì)系統(tǒng)的控制,無(wú)需建立數(shù)學(xué)模型,具有較強(qiáng)的魯棒性,適用于非線性、時(shí)變、時(shí)滯系統(tǒng)的控制,對(duì)中和過(guò)程的控制尤其適合。
For the first time this dissertation discusses the feasibility of data mining for the fields data of cfbb , analyses the preprocessing technology of sample data , introduces the application of clustering analysis , and finally completes the rule extraction by applying knowledge - based artificial neural network . considering distributed parameters , nonlinear , and long time lag system of cfbb , the theory of self - organizing neural fuzzy inference system is applied to the control system of cfbb , and enables control rules extraction on - line . the models of data mining and self - organizing ( fbnc - pnn controller ) are programmed , embedded to the control system of a 35 t / h cfbb in tsingtao , and finally improve the performance of cfbb effectively 本文還針對(duì)流化床鍋爐運(yùn)行的非線性嚴(yán)重、大滯后、不確定性大等特點(diǎn),研究了基于數(shù)據(jù)挖掘優(yōu)化的模糊神經(jīng)自組織控制策略在循環(huán)流化床控制系統(tǒng)中的應(yīng)用,使規(guī)則獲取可以在線進(jìn)行;并編制了數(shù)據(jù)挖掘模塊優(yōu)化自組織控制模塊,應(yīng)用到青島35t / h循環(huán)流化床鍋爐的控制系統(tǒng)中,提高了鍋爐的運(yùn)行水平和效率,取得了良好的效果。