誤差棒 的英文怎麼說
中文拼音 [wùchābàng]
誤差棒
英文
error bar-
Compared with the classical bp algorithm, robust adaptive bp algorithm possesses some advantages as following : ( 1 ) increasing the accuracy of the network training by means of using both the relative and absolute residual to adjust the weight values ; ( 2 ) improve the robustness and the network convergence rate through combining with the robust statistic technique by way of judging the values of the samples " relative residual to establish the energy function so that can suppress the effect on network training because of the samples with high noise disturbances ; ( 3 ) prevent entrapping into the local minima area and obtain the global optimal result owing to setting the learning rate to be the function of the errors and the error gradients when network is trained. the learning rate of the weights update change with the error values of the network adaptively so that can easily get rid of the disadvantage of the classical bp algorithm that is liable to entrap into the local minima areas
與基本bp演算法相比，本文提出的魯棒自適應bp演算法具有以下優點： （ 1 ）與魯棒統計技術相結合，通過訓練樣本相對偏差的大小，確定不同訓練樣本對能量函數的貢獻，來抑制含高噪聲干擾樣本對網路訓練的不良影響，從而增強訓練的魯棒性，提高網路訓練的收斂速度； （ 2 ）採用相對偏差和絕對偏差兩種偏差形式對權值進行調整，提高了網路的訓練精度； （ 3 ）在採用梯度下降演算法對權值進行調整的基礎上，通過將學習速率設為訓練誤差及誤差梯度的特殊函數，使學習速率依賴于網路訓練時誤差瞬時的變化而自適應的改變，從而可以克服基本bp演算法容易陷入局部極小區域的弊端，使訓練過程能夠很快的「跳出」局部極小區域而達到全局最優。Firstly, studied feed - forward neural network and put forward a new algorithm on bp network, called bp algorithm based on robust error function ( bparef ), and the algorithm is proved to be effective for approaching nonlinear system
首先研究了前饋神經網路，提出了基於魯棒誤差函數的bp神經網路的演算法，並且驗證了其對非線性系統逼近的有效性。On the basis of analysing multilayer forward artificial neural networks which based on back propagation algorithms and basic principles of the adaptive noise cancellation system, this paper sets up an adaptive noise cancellation controller based on artificial neural network, which is proved to be more efficient in the noise cancellation and has robust performance based on simulink of matlab at the end, this paper proposes some advices of model and algorithms
在對基於誤差反向傳播學習演算法的多層前向人工神經網路進行分析基礎上，結合傳統自適應噪聲抵消系統基本原理，建立了基於人工神經網路的自適應噪聲抵消器，經基於matlab的simulink模擬實例證明，具有很強的噪聲濾除能力和魯棒性。最後並提出了網路及演算法進一步改進的方法。The feedback control is introduced to compensate effects of both errors of identification and iterative learning, so the control system can get better robustness and control precision
在此引入反饋控制是為了補償辨識和迭代學習的誤差，以增強被控系統的魯棒性，提高控制精度。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
本文首先針對一類不確定非線性系統，基於backstepping方法，利用監督控制，引入最優逼近誤差的自適應補償項，並利用型模糊邏輯系統逼近系統的未知部分，提出了一種魯棒自適應模糊控制器設計方案，運用李亞普諾夫第二方法，先證明了閉環模糊控制系統全狀態有界，再證明了跟蹤誤差收斂到零。The self - tuning algorithm of the parameter adopts eigenvalue of the systemic error and derivative error and so on. thus this method can overcome the disadvantage of the previous method which make the calibrating date not match up to the moveable track of the system, and improve the adaptability, rapidity and robustness of the human simulating intelligent control ' s algorithm
該方法將誤差、誤差導數等特徵量引入參數在線校正公式，克服了以往採用盲目搜索法時校正量與系統實際運行軌跡不匹配的缺點，提高了仿人智能演算法的適應性，快速性和魯棒性。Among the adaptive beam - forming algorithms, the least mean square algorithm is widely used because it has a simple configuration and it is apt to come true and have nice convergence. on the other hand, it has a disadvantage that it converges slowly and there is a conflict between the fixed step and the convergence pace or the error in stabilization. so people have developed many improved least mean square algorithms which generally start from convergence, stabilization, misadjustment, and robustness and come to a formula about variational step in the end
在自適應波束形成演算法中，最小均方（ lms ）演算法因結構簡單，易於實現，能穩定收斂而得到廣泛應用，但它也存在收斂速度受限的缺點：固定步長因子無法解決收斂速度和穩態誤差之間的矛盾。因此，人們提出了各種改進的最小均法演算法來解決這一問題。改進的最小均方演算法通常從如何改進收斂速度、穩態誤差、失調量和魯棒性等指標上出發，最後在新演算法最終表達式中的步長公式上變化。One of the important prerequisites for nn to approximate the nonlinear function f ( x ) is the states x of the system should be in a compact set. therefore, the unkown unlinear systems can be expressed by nn and a model error e ( x ), and there exists a positive constant 6 such that e ( x ) < 8. so the problem can be incorporated into robust adaptive control
神經網路可以逼近非線性函數f （ x ）的一個重要前提是x必須落入某一緊集s中，因此在一緊集u上，未知的非線性系統就可以用神經網路及一建模誤差項（ x ）表示出來，且存在一正的常數，使得（ x ） ，從而這一問題可以歸結到魯棒自適應控制中。This paper first analyzes performance criteria of a control system, and proposes a controller design method that incorporates disturbance attenuation and robust performance. the robust pid controller design amounts to an optimization problem with a non - convex constraint. genetic algorithm is used to solve the problem due to its global search ability, simulation examples show that the method is effective and have general sense
本文從分析控制系統的性能指標入手，從抗干擾性能和魯棒性能兩方面綜合考慮控制器的設計，得到一種魯棒pid控制器的設計思路，把pid控制器的設計問題轉化為求解一個帶魯棒性能約束的絕對誤差積分指標（ iae ）優化問題。A kinematics model for 3d guidance problems is derived, a new kind of nonlinear variable structure adaptive guidance law is brought up by researching the variable structural strength item, and another fuzzy variable structure guidance law with self - adapt region is realized to enhance robustness of target acceleration estimate error of guidance law
本文建立了三維空間追逃問題的數學模型;通過對變結構控制中其變結構強度項的研究，提出一種新型變結構自適應制導律;並且考慮到對變結構強度項進行模糊化，實現一種論域自調整的模糊變結構制導律，以增強制導律對目標加速度估計誤差的魯棒性。Magic marker tool with a tolerance at 15 - 20 ( i alternated the setting as i did it ) to select the skin
魔術棒工具的誤差值設置15 - 20 （我邊嘗試邊設定誤差值）選擇外皮Abstract : a new variable structure model reference adaptive control ( vs - mrac ) scheme is proposed in this paper. by transforming a relative degree one system into a first - order - like differential equation, it is shown that the spr ( strictly positive real ) assumption for reference model or its modified form ( if plant relative degree n * > 1 ) can be removed and the tracking error can converge to zero in finite time or a small residual set
文摘:提出了一種可具有非嚴格正實參考模型的變結構魯棒自適應控制方案，可去除變結構模型參考自適應控制中普遍要求參考模型為嚴格正實或當相對階大於1時參考模型經補償后為嚴格正實的假設.在此基礎上提出的變結構控制律可保證跟蹤誤差在相對階為1時于有限時間內收斂到零，而當相對階大於1時則可使跟蹤誤差收斂到一個殘差集內The paper mainly did the dissemination of error neural network ( bp ) research and gave recommendations on ways to improve bp algorithm. by using better diagnostic techniques with the neural networks legend, memory and reasoning functions and tolerant nature, robustness and good nonlinear, it can better realized the fault
主要對誤差後向傳播神經網路（ bp ）進行研究，提出了改進的bp演算法，利用神經網路的聯想、記憶和推理功能以及容錯性、魯棒性和很好的非線性映射能力等特點，更好地實現故障診斷。Robust optimal tracking error portfolio models based on var
約束的跟蹤誤差投資組合魯棒優化模型This dissertation presented two new methods of robust adaptive track control for a class of mimo strong nonlinear system with external disturbance. one method makes use of taylor approximation principle to linearize the mimo strong nonlinear system at the ideal equilibrium point, meanwhile external disturbance is considered, and then designs two on - line neural network controller respectively, which can dynamically compensate the high order items of taylor series and the control signals at ideal equilibrium point under the drive of state error between linear and nonlinear system. a linear feedback controller obtained by pole assignment and two on - line neural network act on the practical mimo high nonlinear system together, guaranteeing the whole system robust stable and tracking the specified signal ; the other method designs three on - line neural networks for this class of system
本文對於一類含有外部擾動的多輸入多輸出（ mimo ）強非線性系統，提出了兩種新的魯棒自適應跟蹤控制方法，第一種利用了taylor近似的原理，在考慮了外部擾動的情況下，將mimo強非線性系統在理想平衡點處線性化，分別設計了兩個在線神經網路控制器，在線性和非線性系統之間的狀態誤差驅動下動態補償系統的taylor近似高階項及理想平衡點處的控制信號，滿足極點配置方法的線性反饋控制器和兩個在線神經網路聯合作用於實際的被控mimo強非線性系統，在保證整個系統魯棒穩定性的情況下，能夠跟蹤給定的指令信號；另一種方法是針對這類系統設計了3個在線神經網路，分別實時抵消這類非線性系統中的非線性部分、與控制量耦合的非線性項以及外部擾動，使得受控系統的輸出可以完全跟蹤給定輸入參考信號。In the new method the uncertainty of the system to be controlled is normalized firstly, and then the partition of unity that is subordinated to a open covering of state variables compact set is constructed, and the approximation is realized by using its property that can approximate nonlinear continuous function with arbitrary precision, then the decentralized adaptive robust controller of complex systems and adaptive laws of approximate parameter estimation are designed
該方法首先對被控系統中的不確定性進行歸一化處理，進而構造出從屬于狀態變量的緊致域的一組開覆蓋的單位分解，利用其具有以任意精度逼近非線性連續函數的性質，實現了對被控系統不確定性的近似逼近，然後設計了復雜系統的分散魯棒控制器和參數估計的自適應律，並證明了這種控制器使被控系統的狀態及參數估計誤差一致終極有界。2. the time variant nonlinear model of the laser gyro inertial surveying and attitude reference system is changed and simplified to time invariant linear model of the error states using robust analysis method. these results make frequency analysis based on transfer function become possible which is important for dynamic system analysis
2 、採用魯棒分析方法，將激光陀螺慣性測量與姿態參考系統的時變非線性模型轉化為誤差狀態的近似線性定常模型，從而使基於傳遞函數的頻域分析成為可能，這對動態系統分析是至關重要的。The dynamic matrix control algorithms based on finite impulse response are studied. the model errors are defined in the form of upper and lower bound and the error square sum of impulse response coefficients of single - input / single - output systems. the robust stability conditions are proposed for closed - loop systems using dmc in the form of lmi, which can assure the closed - loop system using dmc algorithm to be asymptotically stable, when the coefficients of characteristic polynomial do n ' t satisfy jury ' s dominant coefficient lemma
浙江大學博士學位論文4 .研究了基於脈沖響應模型的動態矩陣預測控制田mc ）演算法，針對單輸入、單輸出系統可能出現的預測模型誤差，分別以脈沖響應系數上下界和脈沖響應系數誤差平方和的形式對預測模型的模型誤差進行定義，根據該定義以線性矩陣不等式的形式分別提出了閉環系統魯棒穩定判據，當閉環多項式系數不能滿足j切嘆主系數定理的情況下仍能保證系統閉環穩定。The image - based approach may reduce computational delay, eliminate the necessity for image interpretation and eliminate errors due to sensor modeling and camera calibration
該方法可以減少計算延時，並且對攝像機和機械臂的校準誤差和目標模型誤差具有較強的魯棒性。Robustness of obe algorithms to underestimation of error bounds
演算法對誤差界低估的魯棒性分享友人