approximating function 中文意思是什麼

approximating function 解釋
逼近函數
  • function : n 1 功能，官能，機能，作用。2 〈常 pl 〉職務，職責。3 慶祝儀式；（盛大的）集會，宴會。4 【數學】...
  1. Considering the characters of bp neural network, such as the simple structure, the advisable malleability, self - fitness, self - studying, nonlinear function approximating, the considerable abilities of parallel computing, fault - tolerant and so on, the bp algorithm have been extensively applied to the areas of system modeling, pattern recognition and seismic exploration since 1986. compared with other algorithms, as the above reasons, the bp algorithm has become the most usual and efficient solutions to the artificial neural networks

    由於人工神經網路中的bp神經網路結構簡單，可塑性強，具有良好的自適應、自學習、極強的非線性逼近、大規模并行處理和容錯能力等特點，自1986年rumelhart等人提出以來，被廣泛應用於系統建模、模式識別、地震勘探等重要領域。而bp演算法數學意義明確，步驟分明，是神經網路中最為常用、最有效、最活躍的一種方法。
  2. Secondly, a new adaptive osculatory rational interpolation kernel function is constructed from the point of approximating the ideal interpolating function, the function ' s characteristics, i. e., the space properties, the spectral properties, and the efficiency are analyzed, and the comparision it with other interpolation methods is made

    然後，在圖像采樣和圖像重建理論的基礎上，基於逼近理想插值核函數的思想，構造了一種自適應切觸有理插值函數，對其空域和頻域的性能進行了分析，並與傳統的圖像插值核函數進行了比較。
  3. Evading risk in financial trading market cries for pricing options to a nicety. asian option, as the most flourish options in the finace market, the pricing has been focused on always. the exact pricing formula for the geometric average asian option had existed, but as to the european - style arithmetic average asian option, due to the dependence structure between the prices of the underlying asset, no analytical formula exists. on the hypothesis that the market is frictionless and without transaction costs 、 on the base of b - s ’ s and in the binomial tree model, we provide several algorithms for computing an accurate value of the european - style arithmetic average asian option. following rogers and shi and by jensen ’ s inequality, many different upper and lower bounds are provided ; meanwhile a formula have got by the comonotonicity and approximating the distribution function. all of the algorithms are easy for programming. with the development of computer, more accurater price can be computed quickly. and numerical example proved that these algorithms are very accurate

    對于幾何平均亞式期權它的定價相對簡單，已經給出了定價公式。對于算術平均亞式期權，它的未定權益具有軌道依賴特性，一直沒有得到它的定價方程的解析解形式。本文基於對市場是無摩擦且在沒有交易費用的情況下，在b - s模型下，利用二叉樹模型給出了算術平均亞式期權定價方法;並總結了利用jensen 』 s不等式給出的各種不同情況下的上下界;同時應用共單調性和近似分佈函數的方法也給出了算術平均亞式期權價格的近似公式。
  4. We introduce and motivate the main theme of the course, the setting of the problem of learning from examples as the problem of approximating a multivariate function from sparse data - the examples

    我們介紹且激發課程的主題將朝向于實例學習法的問題設定，例如稀疏值中多變量函數近似的問題。
  5. Finally, the paper has designed the program of bp neural networks, neural networks based genetic algorithms and hybrid intelligence learning algorithms in vc + +, and applied those algorithms to the xor problem, the function approximating problem and the explaining high difference seismic data problem. the experiment results have showed that hybrid intelligence learning algorithm for training neural networks is better, faster and more accurate than bp algorithm and genetic algorithm

    最後，用vc + +語言設計了bp神經網路、基於遺傳演算法的神經網路和混合智能學習法神經網路實現和進行計算機模擬運行程序，並分別將它們應用於解決異或、函數擬合和高解析度地震資料解釋等問題，從實踐中證明混合智能學習法神經網路與bp神經網路和基於遺傳演算法的神經網路相比有更好的運算性能、更快的收斂速度和更高的精度。
  6. ( 5 ) the constructively sufficient conditions of the approximating c1 functions and the explicit formula dealing with the approximation precision and the number of fuzzy rules are given for one kind of general fuzzy systems, using the methods different from the former ones which are based on the stone - weierstrass theory, therefore, the constructively sufficient conditions are also generalized from polynomial function to c1 function

    （ 5 ）針對一類一般的模糊系統，採用與通常的基於stone - weierstrass定理證明模糊逼近器不同的方法，首次給出了它們逼近c ~ 1函數的構造性充分條件，並給出了逼近精度與模糊規則條數之間關系的顯式表達式
  7. The characteristic approximation is used to handle the convection part along the direc - tion of fluid namely characteristic direction to ensure the high stability of the method in approximating the sharp fronts and reduce the numerical diffusion ; the mixed finite element spatial approximation is employed to deal with diffusion part and approximate the scalar unknown and the adjoint vector function optimally and simultaneously ; in order to preserve the integral conservation of the method, we introduce the modified characteristic method

    該方法對方程的對流部分沿流體流動的方向即特徵方向離散以保證格式在流動的鋒線前沿逼近的高穩定性，消除數值彌散現象；對方程的擴散部分採用最低次混合有限元方法離散、同時以高精度逼近未知函數及未知函數的梯度；為保證方法的整體守恆性，在格式中引入修正項
  8. The new method is a combination of characteristic approximation to handle the convection part, to ensure the high stability of the method in approximating the sharp fronts and reduce the numerical diffusion, a smaller time truncation is gained at the same time, and a mixed finite element spatial approximation to deal with the diffusion part, the sealer unknown and the adjoint vector function are approximated optimally and simultaneously

    此方法即為對方程的對流項沿流體流動的方向即特徵方向進行離散，從而保證格式在流動鋒線前沿逼近的高穩定性，消除了數值彌散現象，並得到了較小的時間截斷誤差；另一方面，對方程的擴散項採用混合元離散，可同時高精度逼近未知函數及其伴隨向量函數，理論分析表明，此方法是穩定的，具有最優的l ~ 2逼近精度。
  9. As in nature, the network function is determined largely by connections ( weights ) between elements, so that a particular input leads to a specific target output. the cores of backpropagation neural network are the capacity of parallel computing, distribute saving, self - studying, fault - tolerant and nonlinear function approximating. input vectors and the corresponding target vectors are used to train a network until it can approximate a function, associate input vectors with specific output vectors, or classify input vectors in an appropriate way as defined by you

    人工神經網路是一類模擬人類神經系統的結構，他揭示數據樣本中蘊含的非線性關系，大量處理單元組成非線性自適應動態系統，具有良好的自適應性、自組織及很強的學習、聯想、容錯和抗干擾能力，在不同程度和層次上可模仿大腦的信息處理機理，可靈活方便的對多成因的復雜未知系數進行建模。
  10. At first, the fundamental principles on wavelet transform ( wt ) and its reconstruction, specially on one - dimension cwt, binary discrete a, # cwt and their reconstruction, are provided. next, the principles on vlsi realization of one - dimension cwt are expounded, and the relevant methods of the implementation are classified and compared with each other. a systematic algorithm for approximating the wavelet function and a example of calculation are gived. it is demonstrated by the example that the algorithm is simple, effective, low erroneous and can be applied to approximating the wavelet function with analytic expression or equal interval samples in time - domain

    論文介紹了小波變換特別是一維連續小波變換和二進離散，柵格下的連續小波變換與重構的基本原理；闡述了小波變換vlsi實現的原理，並對相關的實現方法進行了分類和比較；提出了一種系統地逼近小波函數的演算法，並給出了計算實例；計算結果表明，該演算法簡單、有效、誤差小且適合於逼近具有時域解析表達式或給定了等時間間隔時域樣點值的小波函數。
  11. Based on the analysis of the methods for optimizing the fuzzy neural networks before, this paper has finished following works : 1 ) we proposed a learning algorithm based on tabu search for fuzzy neural networks based on the model of anfis proposed by jyh - shing roger jang. then used the system for one variable function ' s approximation. 2 ) based on the first research, we improved the tabu search algorithm for the purpose of approximating complex functions. 3 ) analysis the capabilities of tabu search, and discuss the approximation ability and generalization ability of the fuzzy neural networks system according to the compute results

    本文在對以前的模糊神經網路參數學習演算法進行分析的基礎上，做了以下幾個方面的工作： 1 ）根據禁忌搜索演算法的特點，在jyh - shingrogerjang提出的anfis模型的基礎上，將禁忌搜索演算法應用於模糊神經網路線性和非線性參數的學習上，並將該模型用於單變量函數的逼近； 2 ）在第一階段的基礎上，對演算法進行了改進，使改進后的演算法能夠適用於復雜的ii函數逼近問題; 3 ）根據計算機模擬的結果，對禁忌搜索演算法的性能進行了分析，並對該模糊神經系統的函數逼近能力和泛化能力進行了討論。
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