function approximation 中文意思是什麼

function approximation 解釋
函數逼近
  • 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】...
  • approximation : n. 1. 接近;近似。2. 【數學】近似值。3. 概算,略計。
  1. In rsdm, binary patterns are replaced by real - valued patterns, accordingly avoiding the coding process ; the outer learning rule is replaced by regression rule, therefore the model has not only the ability of pattern recognition but the ability of function approximation. the prearrangement of the address array bases on the distribution of patterns. if the distribution of patterns is uniform. then the address array is prearranged randomly, otherwise predisposed with the theory of genetic algorithm and the pruneing measure so as to indicate the distribution of patterns and improve the network performance. non - linear function approximation, time - series prediction and handwritten numeral recognition show that the modified model is effective and feasible

    在rsdm中,以實值模式代替二值模式,避免了實值到二值的編碼過程:以回歸學習規則代替外積法,使該模型在具有識別能力的同時具有了對函數的逼近能力;地址矩陣的預置根據樣本的分佈採取不同方法,若樣本均勻分佈,則隨機預置,否則利用遺傳演算法的原理和消減措施來預置地址矩陣,使之反映樣本的分佈,改善網路的性能。
  2. So the network has the better capability of function approximation and pattern recognition

    因此改進的網路具有較好的函數逼近能力和模式識別能力。
  3. The kanerva ' s sparse distributed memory ( sdm ) tackles the problem of training large data patterns and extendes the storage mode of existing computer. but it ' s address array produced randomly ca n ' t reveal the distribution of patterns and it has ' t the ability of function approximation for its learning rule

    Kanerva的稀疏分佈存儲( sdm )模型解決了大維數樣本的訓練問題,推廣了現有計算機的存儲方式。但其地址矩陣的隨機預置方式不能反映樣本的分佈,並且sdm的學習方式使之不能用於函數逼近及時間序列預測問題。
  4. In addition, the paper makes relatively in - depth analyses on the function approximation theory of radial basis function neural networks and the stability of the adaptive controller based on radial basis function neural networks

    此外,本文對徑向基函數神經網路的函數逼近理論以及基於徑向基函數神經網路的自適應控制器的穩定性作了較深入的分析。
  5. Cfd is also used to get the data of performance parameters of the nozzle at different nozzle pressure ratio, different nozzle area ratio and different geometric defected angle. the theory of function approximation is used to establish the transient model of thrust vectoring with the data calculated

    本文通過cfd計算獲得矢量噴管的性能參數,如流量系數、推力系數以及有效推力矢量角等與矢量噴管壓比、面積比以及幾何偏轉角之間的關系數據,並利用函數逼近理論建立了矢量噴管的動態數學模型。
  6. Conditions that a class of sequence has convergent subsequence arc discussed in the paper, this sequence is important in function approximation. the gained conclusions are useful in some relative areas

    摘要文章討論了在函數逼近論中有重要作用的一類序列存在收斂子列的條件,文中所得結論在相關問題的研究中有較重要的作用。
  7. We look at the problem of learning from examples as the problem of multivariate function approximation from sparse chosen data, and then consider the case in which the data are drawn, instead of chosen, according to a probability measure

    並檢視稀疏精選值中多變量函數近似法等這些從實例學習法所發現的問題,然後根據機率衡量,審思隨機獲得資料而非選定資料的案例。
  8. Function approximation study of general fuzzy system

    模糊系統的函數逼近特性研究
  9. The applications in fault signal classification and data compression of power system are studied and simulated. through the introduction of multiwavelet network, other two kinds of multiwavelet networks are proposed. the theorems for function approximation abilities of some wavelet networks and multiwavelet networks are proposed and proved

    從理論上分析了小波變換和神經網路的函數逼近能力以及它們之間的內在聯系,並且利用不同的激勵函數構造相應的小波函數;對小波網路在電力系統故障信號分類和故障數據壓縮方面的應用進行了研究和數字模擬;提出了另外兩種不同的多小波網路,對一些小波網路和多小波網路的函數逼近能力,提出了相應的定理並進行了證明。
  10. Firstly, the basic theories of artificial neural network are introduced, including neural cell model, the basic structure of artificial neural network and the study methods. the essential of bp network with function approximation and rbf network and the performance of technology are analyzed. the algorithm of bp and rbf networks are also studied

    論文首先闡述了神經網路的基本理論,包括神經元模型,神經網路的基本結構和神經網路的學習方法;分析了具有函數逼近能力的bp網路和徑向基函數( rbf )網路實質、技術實現問題,並研究了bp和rbf網路學習演算法。
  11. In addition, we formulated measures of ga - hardness for gas with real - valued encoding, and advanced the method of the first order function approximation

    對實數編碼遺傳演算法困難度的測試方法進行了分析,提出了一階函數逼近測試法。
  12. Abstract : based on the strong learning ability and nonlinear function approximation capacity of multi - layer perceptrons ( mlps ), a generating chaotic sequence model is proposed in this paper

    文摘:應用具有全局最優的進化規劃演算法建立產生混沌序列的優化神經網路模型。
  13. Research and simulation of bus protection with function approximation ability

    基於函數逼近能力的母線保護的研究及模擬
  14. After trained, the fuzzy neural networks have good function approximation ability and generalization ability

    訓練后的模糊神經系統具有良好的函數逼近能力和泛化能力。
  15. Secondly, a solution based on the theory of function approximation is proposed to select the key parameter of neural network

    其次,在神經網路關鍵參數選擇問題上,提出了一種基於函數逼近思想選擇網路參數的方法。
  16. , 2002 ). the varying coefficients model, which is the function approximation method for high dimension, is discu ssed in this paper

    本論文主要討論的是變系數模型( thevaryingcoefficientmodel ) ,屬于函數近似這一類。
  17. So, it could be seen that the structures research, function approximation properties and learning algorithms of procedure neural network models is quite significant

    研究過程神經元網路模型的拓撲結構,函數逼近性質,學習演算法等具有十分普遍的意義。
  18. Kosko applied it to several engineering applications and summarized that the key of fuzzy engineering was function approximation in the book of fuzzy engineering

    在模糊工程一書中, kosko把這個模型應用到許多工程領域並概括出模糊工程的核心問題是函數逼近問題。
  19. While using support vector regression to do function approximation, we can control the number of support vectors and the approximative performance all by two parameters

    在用支持向量回歸進行函數逼近時,我們完全可以用兩個參數來控制支持向量的個數和逼近的效果。
  20. Its function approximation property is analyzed. in the view of generalization ability of wavelet network, a constructive method of designing wavelet network and a new learning algorithm are proposed to obtain improved network ' s performance. the latter is a multiresolution wavelet model based on multiresolution analysis theory

    文中分析了此種小波網路的函數逼近能力,從網路泛化能力的角度,給出了網路結構具體的設計方法,提出了一種學習速度更快的新的參數混合學習演算法。
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