traditional approximation 中文意思是什麼
traditional approximation
解釋
慣用近似- traditional : adj. 口頭傳說的；傳統的，慣例的，因襲的。 traditional Chinese medicine 中藥。
- approximation : n. 1. 接近；近似。2. 【數學】近似值。3. 概算，略計。
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A new learning algorithm of the continue wavelet networks parameters is proposed, that is, hybrid parameters learning algorithm. the parameters of wavelet networks are divided into two parts, and different methods are used to training them. compare to the traditional parameters learning method, the new method proposed in this paper has the advantages of rapid convergence speed and high approximation capability
提出了連續小波網路的混和參數訓練方法，將小波網路參數分為小波系數和網路權值兩部分，對這兩部分參數分別採用不同的方法進行訓練，這一新的訓練方法相對于傳統的小波網路參數訓練方法來說具有收斂速度快，逼近精度高等優點。 -
On the basis of analyzing the several traditional algorithms, the efficient design method, the self - initiated weighted least squares ( swls ) combined with adaptive simulated annealing ( asa ), are proposed explicitly for the design of pif. this chebyshev criterion based optimal approximation method has not only very fast computing speed but also high accuracy and good controllability
在對這些演算法特性分析比較的基礎上，系統完整地提出適用於lcos投影分合色偏振干涉濾光片設計的最高效方法? ?自啟動權值最小二乘演算法（ swls ）結合自適應模擬退火演算法（ asa ） 。 -
2 ) systematically introduced parameter estimation of distributed sources on the base of models, including the maximum likelihood estimate, least squares estimator, dspe, dispare, etc. 3 ) studied four low complexity algorithms : one order approximation, two point sources approximation, traditional beamforming and relax of parameter estimation
2 ）在模型基礎上系統地介紹了已有分散式目標參數估計方法，包括最大似然與最小二乘演算法， dspe和dispare演算法等。 3 ）研究了四種低復雜度演算法：一階近似演算法、兩點近似的演算法、常規波束形成演算法和relax演算法，這些演算法都是次優演算法，但計算量小，具有實用價值。 -
Using calculation result from prophase sample, sampling size needed in next step can been determined and it can be expected that ultimate sample size would be a best approximation for that desired and meet the precision demanded at less cost than traditional methods
利用前期抽取樣本的計算結果，可以規定進一步需抽取的樣本量，最終樣本量是對真正期望樣本量的一個最佳近似，它比傳統方法更能保證以最少的費用滿足預先設定的精度要求。 -
Comparing with traditional successive approximation process algorithm, our method can improve quality of reconstruction result while speeding up convergence. further, proper threshold is adopted to enhance the robustness of the histogram constraint.,
和傳統的連續近似迭代法相比，該方法在加快收斂速度同時能夠有效地改善重建質量，同時我們採用閾值對先驗直方圖的影響進行動態地控制，有效地增強了直方圖約束的抗干擾性。 -
Traditional method can be classified two class : linear optimization technique and nonlinear optimization technique, linear optimization technique base on born approximation or rytov approximation is usually used to solve weak scattering problem
線性優化方法採用線性近似忽略了散射體內部的多次散射，可以有效的反演低對比度的問題，但對于高對比度問題的求解則有可能不收斂。 -
Whereas model 2 is geared towards a stateless protocol http, jsf supports a richer mvc model, a much closer approximation of a traditional gui application
盡管model 2連接到一個無狀態協議http ，但是jsf支持更加豐富的mvc模型這是傳統gui應用程序更加緊密的近似。 -
Lbie, based on the local boundary equation, adopts the traditional moving least squares ( mls ) approximation which depends on only the values of the nodes in the domain of the problem or along its boundary. the whole process of integration is carried on over a local domain or its local boundary centered at the node in question. the local boundary equation can be rewritten to represent the values of the unknown function at the point of interest, and the essential boundary conditions can be directly and easily enforced by using the green formula and the characters of the dirac function
它以局部邊界積分方程為基礎，採用移動最小二乘近似函數，從而只需要分佈在問題域內及其邊界上的節點的信息值，無需劃分單元；整個積分是在以節點為中心的局部域及其邊界上實現，所以不需要背景積分網格；藉助于格林公式及dirac函數的性質，將局部邊界積分方程轉化為所考慮點的未知函數的邊界積分表達式，便於直接施加本質邊界條件。 -
After reducing the noise by the technique of signal enhancement, the more extract approximation results than traditional approach can be achieved with the tls method
在採用信號增強技術降低觀測噪聲后，利用總體最小二乘方法估計被測信號參數可以得到比傳統方法更好的分析結果。 -
To reduce huge computation of the traditional stochastic optimization methods for engineering optimization, approximation model methods with acceptable accuracy for engineering design are developed based on the statistical theory
摘要針對在工程中完全採用隨機類優化方法尋優時計算量過大的問題，應用統計學的方法發展了計算量小、在一定程度上可以保證設計準確性的近似模型方法。 -
The new ideas of this dissertation mainly lie in two points. based on the research on the traditional edge tracing techniques, this dissertation presents an improved method according to lml rule. at the same time, studying the polygonal approximation methods, this dissertation also presents an improved and based on mergence method to image contour, and implements the transform of the contour from image to graphics preferably
本文的主要創新點在於：在研究傳統輪廓跟蹤技術? ? 「爬蟲」法的基礎上，提出了基於lml規則的改進的輪廓跟蹤方法；同時，研究了多邊形擬合法，提出了基於聚合的改進的多邊形擬合物體圖象輪廓線方法，較好地實現了對邊緣輪廓線的從圖象到圖形的轉換。 -
Control systems in modern automatic engineering are nonlinear, time - changed and indefinite. lt is difficult to model by traditional method, even sometime impossible. under these circumstances we should apply model identification to gain the approximate model of object for effective control, there are many models to be chosen, fuzzy model is one of them, it is put forward with the development of fuzzy control. fuzzy model has characteristics of general approximation and strong nonlinear, it is fit for describing complex, nonlinear systems. to avoid rules expansion when the number of input values are very big. in this paper we apply hierarchical fuzzy model to resolve this problem, we also illustrate it has general approximation to any nonlinear systems. genetic algorithm is a algorithm to help find the best parameters of process. lt has abilities of global optimizing and implicit parallel, it can be generally used for all applications. in our paper we use fuzzy model as predictive model and apply ga to identify fuzzy model ( including hierarchical fuzzy model ), we made experiments to nonlinear predictive systems and got very good results. the paper contains chapters as below : chapter 1 preface
現代控制工程中的系統多表現為非線性、時變和不確定性，採用傳統的建模方法比較困難，或者根本無法實現，在這種情況下，要實現有效的控制，必須採用模型辨識的方法來獲取對象的近似模型，並加以控制，目前用於系統辨識的模型種類很多，模糊模型是其中的一種，它隨著模糊控制的發展而被人提出，模糊模型具有萬能逼近和強非線性的特點，比較適合於描述復雜非線性系統，為了解決模糊模型在輸入變量較多時規則數膨脹的問題，文中引入遞階型模糊模型，並引證這種結構的通用逼近特性。遺傳演算法是模擬自然界生物進化「優勝劣汰」原理的一種參數尋優演算法，它具有隱含并行性和全局最優化的能力，並且對尋優對象的要求比較低，在工程應用和科學研究中，得到了廣泛的應用，本文將遺傳演算法引入模糊模型的辨識，取得了很好的效果。 -
Simulation has proved the validity of chaos genetic algorithm. in succession, this paper deeply discusses neural network theory and traditional radial basis function algorithm. based on this studying, for resolving rbfnn approximation and generalization, combination of rbfnn and chaos genetic algorithm is put forward, which make the most of rbfnn approximation and chaos genetic algorithm entire optimization
接著，本文深入探討了神經網路基本理論以及傳統徑向基神經網路的學習演算法，在此基礎上，針對徑向基神經網路逼近能力和泛化能力不理想的問題，將徑向基神經網路和混沌遺傳演算法相結合，充分利用徑向基神經網路的逼近能力和混沌遺傳演算法的全局搜索能力。
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