本文提出了用于SCARA 機(jī)器人運(yùn)動(dòng)控制的自組織模糊聚類神經(jīng)網(wǎng)絡(luò)控制器。該控制器基于模糊聚類方法在學(xué)習(xí)模糊規(guī)則之前先優(yōu)化訓(xùn)練數(shù)據(jù)，去除冗余數(shù)據(jù)并解決數(shù)據(jù)沖突問題，不但減少了神經(jīng)網(wǎng)絡(luò)的計(jì)算負(fù)擔(dān)，而且生成的規(guī)則更加適合機(jī)器人運(yùn)動(dòng)控制。控制器主要特點(diǎn)是能夠動(dòng)態(tài)地自組織結(jié)構(gòu)，學(xué)習(xí)速度快，魯棒性強(qiáng)。仿真結(jié)果表明控制效果很好。
關(guān)鍵詞：自組織模糊控制; 聚類分析; 模糊系統(tǒng); 神經(jīng)網(wǎng)絡(luò)
Abstract：This paper presents a self-organizing fuzzy clustering neural network (SOFCNN)
controller suitable for motion control of SCARA robotic manipulators. The SOFCNN is based on the fuzzy clustering method optimizing training data before learning fuzzy rules, in order to
remove redundant data and resolve conflicts in data. The method reduced computation of neural network and made the control rules more reasonable for the robotic manipulators. The feature of the SOFCNN controller has dynamic self-organizing structure, fast learning speed and flexibility in learning. The simulation results show that is very fine.
Key Word: Self-Organizing Fuzzy Control; Clustering Analysis; Fuzzy System; Neural
Networks