sugeno 中文意思是什麼

sugeno 解釋
菅乃
  1. To the level control problem of a spherical tank, two model - free control methods are proposed. in the former method, the takagi - sugeno fuzzy model is used to tune the neuron controller gain. in the latter method, the model - free control method using the neural network model proposed for nonlinear plants is presented

    針對具有非線性特性的球形容器液位受控對象，從增益自調整和非線性補償兩個角度，分別提出了兩種非模型控制方法，前者採用t - s模糊模型對神經元控制器的增益進行在線整定，後者使用本文建立的非線性神經元網路對球形容器進行非模型控制。
  2. The algorithm can not only eliminate the influence of the cumulative errors of the photoelectric code recorder, but also it can satisfy the requirement of the real - time control. a direct inverse model controller of fuzzy neural network with changeable structure based on takagi - sugeno inference is presented and it is used to the motion control of mobile robot. in order to avoid the obstacles successfully, detection results from ccd and ultrasonic sensors are fused by a fuzzy neural network, which acts as an avoidance controller

    包括移動機器人的融合自定位問題：移動機器人利用光電編碼器進行自定位，同時用擴展卡爾曼濾波器融合多個超聲波傳感器的測量值，採用回朔演算法將融合值用於復位光電編碼器，消除了光電編碼器累積誤差的影響，並能滿足實時控制的要求：並提出一種基於takagi - sugeno模型的變結構模糊神經網路直接逆模型控制器，並應用於移動機器人的運動控制；利用模糊神經網路避障控制器融合ccd攝象機與超聲波傳感器探測到的環境信息，以實現機器人的安全避障。
  3. Anfis based on takagi and sugeno ' s fuzzy model has the advantage of being linear - in - parameter ; thus the conventional adaptive methods can be efficiently utilized to estimate its parameters

    由於節點參數是線性的，用梯度下降和最小二乘的混合學習演算法來調節參數，減少了運算量，加快了收斂速度。
  4. When the parameters of a tagaki - sugeno ( t - s ) fuzzy system are perturbed with random noise, the system turns to be a stochastic t - s system. essentially, it is a nonlinear stochastic differential system. the second part of the dissertation focuses on the stability analysis and control of the stochastic t - s systems

    設控制對象是用tag山一sugeno （ t . s ）模糊模型表示的非畢蜂琴攀{當模型參姆到統計特徵已知的瞰嗓桿擾吟一就成為一個瞬娜咚模糊模型，本質上它是一個非線性隨機微分系統。
  5. According to the case that the macrocosmic system is nonlinear and lack of testing data, the improved forecasting methods are proposed such as fuzzy exponential smoothing forecasting, center approaching gray prediction and the local multiple regression fuzzy ( lmrf ) model based on takagi - sugeno fuzzy logical system. these improved methods are applied into the forecasting instances. the prediction accuracy of the stimulation result is testified and the improved forecasting methods are proved much better than conventional forecasting methods

    本文從宏觀角度和基於區域交通流小樣本數據的實際情況，提出了改進的模糊指數平滑預測和中心逼近式灰色預測方法，建立了基於takagi - sugeno模糊邏輯推理的局部多元回歸模型（ lmrf模型） ，並進行了實例預測模擬，實例模擬結果表明改進的預測方法比傳統的預測方法精度提高了好多倍。
  6. Takagi t, sugeno m. fuzzy identification of systems and applications to modeling and control [ j ]. ieee trans. on smc, 1985, 15 ( 1 ) : 116 132

    修智宏，張運傑，任光.輸入採用標準模糊分劃的模糊控制系統性質及穩定性分析[ j ] .模糊系統與數學.已錄用
  7. Generalized takagi - sugeno fuzzy logical syste

    模糊邏輯系統最優參數辨識
  8. In this paper the predictive system of field current is studied using takagi - sugeno fuzzy system. the input parameters are drawn from the turbo - generator ' s stator

    因此，本文利用基於神經網路集成的高木一關野模糊系統來建立發電機正常運行時轉子電流的預測模型。
  9. With these algorithms above, a simulation test has been taken to the main steam temperature control of boiler by means of generalized predictive control using takagi - sugeno fuzzy models. the test shows that t - s model of the nonlinear system can be successfully identified on line and the nonlinear main stream temperature can be controlled successfully using predictive algorithm

    並把這種基於t - s模型的預測控制演算法應用在某電廠的主汽溫的升負荷過程的控制中，模擬表明本演算法能夠很好地通過在線非線性t - s模型的辨識從而實現對于具有非線性特性的主汽溫對象的預測控制。
  10. Based on the introduction of the principles of takagi - sugeno ( t - s ) fuzzy model and generalized predictive control ( gpc ) algorithm, the fuzzy predictive control method combining gpc and t - s model is classified as three kinds of algorithms. the design method of these algorithms is presented in detail. a comparison of these fgpc strategies in control performance and complexity of computation is given by simulation

    在介紹了t - s模糊模型和gpc基本原理的基礎上，將基於t - s模型的gpc歸納為三種演算法，從理論上對這三種演算法進行了詳細地推導，並通過模擬研究比較了三種演算法的控制性能和計算負擔上的差異；從而為這一類模糊預測控制的實際應用提供了選擇的依據，也為進一步的性能分析奠定了基礎。
  11. Simplified identification method of takagi - sugeno fuzzy model

    模糊模型的一種簡單辨識演算法
  12. Identification of nonlinear systems by takagi - sugeno fuzzy model

    模糊模型的非線性系統辨識
  13. In this dissertation, we presented a new kind of image measurement and showed the algorithm of how to calculate the value of this measurement fastly. the new image measurement is named fuzzy image measurement and based on the generalized integral extended from the sugeno integral

    本文通過對sugeno模糊積分進行推廣后得到的基於三角模的廣義模糊積分，建立了一種新的模糊圖像度量，並給出了在實際應用中此度量的快速計算方法。
  14. 4 sugeno m, kang g. structure identification of fuzzy model. fuzzy sets and systems, 1988, 28 : 15 - 33. 5 sugeno m, yasukawa t. a fuzzy - logic - based approach to qualitative modeling

    首先我們利用模糊數據聚集的手法，將變量的宇集分成若干小區接著在每一小區，採取模糊回歸分析的技術，推演出最佳的模糊限制式。
  15. Competitive takagi - sugeno fuzzy reinforcement learning

    模糊再勵學習
  16. Fuzzy neural networks control based on takagi - sugeno and fuzzy reinforce

    的再勵學習模糊神經網路控制
  17. Stabilizing strategy of system based on the simplified takagi - sugeno fuzzy model

    模糊模型的系統鎮定策略
  18. Equivalence of generalized takagi - sugeno fuzzy system and its hierarchical systems

    模糊系統與其分層系統的等價性
  19. Based on the theory of fuzzy constraint processing, the fuzzy model can be viewed as a generalized takagi - sugeno fuzzy model with fuzzy functional consequences

    本論文系藉由模糊限制處理為基礎，提出一種新的模糊塑模架構，我們稱之為限制式模糊塑模。
  20. Algorithms to combine the neural networks classifiers based on dempster - shafer theory and two kinds of fuzzy integral ( sugeno and choquet integral ) respectively are proposed. the influences of the fact that every classifier has different classification ability for different class are all considered in these two kinds of algorithms

    提出了分別基於dempster - shafer組合公式和兩類模糊積分（ sugeno積分和choquet積分）進行多個神經網路分類器組合的演算法，這兩種演算法都考慮了每個分類器對不同類的識別能力的不同這一經驗知識。
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