error n. 1.錯(cuò)誤;失錯(cuò)。 2.謬見,誤想;誤信;誤解。 3.罪過。 4.【數(shù)學(xué)】誤差;【法律】誤審,違法;(棒球中的)錯(cuò)打。 commit [make] an error 犯[出]錯(cuò)。 correct errors 改正錯(cuò)誤。 a clerk's [clerical] error 筆誤。 mean errors 標(biāo)準(zhǔn)誤差。 a writ of error 【法律】(推翻錯(cuò)誤原判的)再審命令。 nature's error 天生畸形。 in error 弄錯(cuò)了的;錯(cuò)誤地。 errors of commission [omission] 違犯[疏忽]罪。 fall into error 誤入歧途。 nature's error 天生畸形。 adj. -less 無錯(cuò)誤的,正確的。
Firstly , based on backstepping and the supervisory control strategy , a robust adaptive fuzzy controller is designed for a class of nonlinear systems . the first type fuzzy logic system is used to approximate the unknown part of the process . the adaptive compensation term of the optimal approximation error is adopted 本文首先針對(duì)一類不確定非線性系統(tǒng),基于backstepping方法,利用監(jiān)督控制,引入最優(yōu)逼近誤差的自適應(yīng)補(bǔ)償項(xiàng),并利用型模糊邏輯系統(tǒng)逼近系統(tǒng)的未知部分,提出了一種魯棒自適應(yīng)模糊控制器設(shè)計(jì)方案,運(yùn)用李亞普諾夫第二方法,先證明了閉環(huán)模糊控制系統(tǒng)全狀態(tài)有界,再證明了跟蹤誤差收斂到零。
Secondly , based on a modified supervisory control strategy and the approximation capability of generalized multilinear fuzzy logic systems ( gmfls ) , a new scheme called model reference adaptive fuzzy control ( mrafc ) for a class of siso nonlinear systems is proposed , the external disturbances and the approximation error are considered 其次針對(duì)一類具有siso的不確定非線性系統(tǒng),同時(shí)考慮外界干擾和建模誤差,基于一種修改的監(jiān)督控制方案并利用廣義多線性模糊邏輯系統(tǒng)的逼近能力,提出一種模型參考自適應(yīng)模糊控制器設(shè)計(jì)的新方案。
Abstract : in this paper a new identification model constructed by neural networks with modified inputs and stable filters is presented for continuous time nonlinear systems in order to reduce the inherent network approximation errors . an adaptive law with projection algorithm is employed to adjust the parameters of networks . under certain conditions , convergence of the identification error is proved 文摘:在用神經(jīng)網(wǎng)絡(luò)進(jìn)行系統(tǒng)建模時(shí),建模誤差的存在是難免的.為了減小這種誤差,本文對(duì)連續(xù)時(shí)間非線性系統(tǒng)提出了一種新的神經(jīng)網(wǎng)絡(luò)辨識(shí)模型,它是由帶有輸入修正的神經(jīng)網(wǎng)絡(luò)和穩(wěn)定濾波器組合而成.文中給出了權(quán)值的學(xué)習(xí)算法,即權(quán)值是根據(jù)辨識(shí)誤差的投影算法來改變,證明了在一定條件下辨識(shí)誤差的收斂性
Because the extension of dynamic change in weak nonlinear system is not large , the robust reliable controller designed by ldi can make the whole controlled system stable when time lag and faults exit in the system , at the same time , satisfying robust performance index of the system . next , considered that the approximation error produced by ldi , the unmodeling error produced by system , the parameter uncertainties and the external disturbances can not be ignored , a dynamic neural network controller is designed to compensate their effect on line . adjusted by the state output error between the ideal model and the controlled system , the cooperation of on - line network compensator and linear h _ ( ) controller of ideal model makes the whole close - loop system guar antee robust stability and track the specified signal well 本文在基于線性微分包含( ldi )的技術(shù)基礎(chǔ)上,提出了兩種非線性系統(tǒng)的魯棒控制方法,首先討論了一類弱非線性時(shí)滯控制系統(tǒng)中的魯棒可靠控制器設(shè)計(jì)問題,由于弱非線性系統(tǒng)本身的動(dòng)態(tài)變化范圍不大,在確保整個(gè)系統(tǒng)魯棒性能指標(biāo)的前提下,當(dāng)系統(tǒng)存在時(shí)滯和故障時(shí),通過ldi設(shè)計(jì)出的魯棒可靠控制器可以鎮(zhèn)定整個(gè)被控系統(tǒng);其次,在考慮運(yùn)用ldi技術(shù)產(chǎn)生的逼近誤差、系統(tǒng)本身的未建模誤差及參數(shù)不確定性以及外部擾動(dòng)的影響不能被忽略的情況下,設(shè)計(jì)了在線補(bǔ)償這部分影響的動(dòng)態(tài)神經(jīng)網(wǎng)絡(luò)控制器,在理想模型和被控系統(tǒng)狀念輸出誤差的調(diào)節(jié)作用下,在線神經(jīng)網(wǎng)絡(luò)補(bǔ)償器與理想模型的線性h _控制器相互配合,使得整個(gè)閉環(huán)系統(tǒng)既可以保證魯棒穩(wěn)定性又能夠跟蹤給定的指令信號(hào)。
Adaptive bounding technique is used to deal with unknown boundedness of approximation errors . the arbitrary output tracking accuracy is achieved by tuning the design parameters . thirdly , based on the results in chapter 3 , two design approaches of adaptive iterative learning control ( ailc ) are proposed for two classes of parametric nonlinear time - delay systems 神經(jīng)網(wǎng)絡(luò)用于逼近未知的非線性時(shí)滯函數(shù),當(dāng)狀態(tài)不可測(cè)時(shí),采用時(shí)滯濾波器估計(jì)系統(tǒng)狀態(tài),利用backstepping技術(shù)設(shè)計(jì)權(quán)值自適應(yīng)律和控制律,占優(yōu)化方法處理時(shí)滯基函數(shù),自適應(yīng)界化技術(shù)處理逼近誤差的未知上界,通過調(diào)節(jié)設(shè)計(jì)參數(shù)可以實(shí)現(xiàn)對(duì)目標(biāo)軌線任意精度的跟蹤。
In this approach , the neural network is used to learning the nonlinear function of the system . the network weights are derived using lyapunov - based design and are adapted on - line . due to the existence of neural network approximation error and external disturbance , the sliding mode control which is insensitive to disturbance and parameter pertabation is used to achieve robust tracking for the system 該方法利用神經(jīng)網(wǎng)絡(luò)學(xué)習(xí)系統(tǒng)中的非線性函數(shù),神經(jīng)網(wǎng)絡(luò)的權(quán)值由lyapunov穩(wěn)定性理論導(dǎo)出,并且在線調(diào)整;考慮到網(wǎng)絡(luò)逼近誤差和外部干擾的存在,文中利用滑動(dòng)模態(tài)對(duì)參數(shù)和擾動(dòng)不敏感的特點(diǎn),實(shí)現(xiàn)了系統(tǒng)的魯棒輸出跟蹤。
This paper researches the numeric approximation characteristic of series - parallel fuzzy system and points out that the number of fuzzy rules should not exceed the number of the samples . in addition , the influence of approximation error and system initial error on the performance of the series - parallel fuzzy system is also investigated 本文研究了串并聯(lián)方式模糊系統(tǒng)的數(shù)字逼近特性,得出結(jié)論:當(dāng)模糊規(guī)則數(shù)等于樣本數(shù)時(shí),已經(jīng)可以實(shí)現(xiàn)精確插值,因此模糊規(guī)則條數(shù)不能超過樣本數(shù)目,否則將冗余,并可能引起振蕩,削弱模糊系統(tǒng)的泛化能力。