A method to get the initial rule sets by the way of finite - state - machine - rough - sets is brought up to solve the special key problem in navigation knowledge acquiring 該方法提取有限狀態(tài)機(jī)中的格局轉(zhuǎn)換關(guān)系,形成粗糙集決策信息系統(tǒng)中的規(guī)則樣例,采用屬性約簡(jiǎn)與規(guī)則提取算法獲得導(dǎo)航知識(shí)。
The importance of rule extraction from trained ann is that of using the ann for the " learning " of impliedly rules within swatch knowledge , and then expressing with rules . the goal in rule refinement is to use combination of ann learning and rule extraction techniques to produce a " better " set of symbolic - rules which can then be applied back in the original problem domain . in the rule refinement process , the initial rule base is inserted into an ann by programming some of the 這樣既克服了單一專家系統(tǒng)知識(shí)獲取的“瓶頸” ,又避免了神經(jīng)網(wǎng)絡(luò)的“黑箱問題” ;對(duì)于知識(shí)求精,先把已經(jīng)得到的規(guī)則轉(zhuǎn)換為網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu),利用神經(jīng)網(wǎng)絡(luò)對(duì)知識(shí)進(jìn)行求精,這樣可解決初始知識(shí)庫存在的知識(shí)不完全、知識(shí)之間不一致、有的知識(shí)不正確等問題,改善管理決策的智能水平和增強(qiáng)了系統(tǒng)的運(yùn)行效能。