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function approximation中文是什么意思
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- 中文翻译
- 造句
- 函数逼近
- function n. 1.功能，官能，机能，作用。 2.〔常 pl.〕职 ...
- approximation n. 1.接近；近似。 2.【数学】近似值。 3.概算， ...
- approximation function 逼近函数; 近似方程式
- approximation of function 函数逼近; 函数的逼近; 函数近似
- approximation of lagrange function 拉格朗日函数近似
- exponential function approximation 指数函数近似
- rational function approximation 有理函数逼近; 有理函数近似
- rational-function approximation 有理函数逼近
- approximation n. 1.接近；近似。 2.【数学】近似值。 3.概算，略计。 successive approximations 逐次近似计算法。 be a very close approximation to 很接近于…。
- approximation approximation 近似，逼近
- acoustic approximation 声学近似
- adiabatic approximation 绝热近似法
- allowable approximation 容许近似值
- alternative approximation 交互近似法
- analog approximation 模拟近似
- analytical approximation 解析近似
- approx approximation 近似.略计
- approximation algorithm 近似算法
- approximation algorithms 第三十五章 近似算法
- approximation and correction 近似与校正
- approximation andcorrectionon 近似与校正
- approximation by excess 过剩逼近
- approximation by polynomial 多项式逼近
- approximation calculus 近似计算
- approximation coefficient 近似系数
- The value function approximation of reinforcement learning with neural network is studied . based on this method , the problems of the " curse of dimensionality " in the reinforcement - learning algorithm and " structure credit - assignment " in learning are solved
本文针对传统增强学习中出现的“维数灾难”问题以及学习问题中的“ structurecredit - assignment ”问题采用bp神经网络近似q -学习的评价函数的解决方法。 - Then the principle of surplus torque was analyzed in detail , and some factors that influenced the surplus torque were given . based on the learning characteristic of neural network and the function approximation ability of the neural networks , a hybrid control based on neural networks was proposed
针对多余力矩严重影响施力系统动态加载性能的特点，依据神经网络的非线性逼近和自学习特性，提出一种基于神经网络的复合控制方法宋提高系统性能。 - 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，以实值模式代替二值模式，避免了实值到二值的编码过程：以回归学习规则代替外积法，使该模型在具有识别能力的同时具有了对函数的逼近能力；地址矩阵的预置根据样本的分布采取不同方法，若样本均匀分布，则随机预置，否则利用遗传算法的原理和消减措施来预置地址矩阵，使之反映样本的分布，改善网络的性能。 - In this dissertation , we provide how to apply support vector regression to function approximation by means of experiments and obtain the relevant conclusion . we also put forward the concept , algorithm and examples of multistage support vector machine which based on the idea of classification after clustering
本文以实验的方式给出了支持向量回归在函数逼近上的应用并得出了相关的结论；另外本文根据先聚类后分类的思想提出了多级支持向量机的概念、算法，并给出了具体的例子。 - Since time sectional scan method ( tssm ) acts as the dominant partner in the panel display , attention is drawn to the aspect in the drive methods . according to functions approximation theory , the method of complex merging approximation tssm which utilize multiple dissimilarity @ subclasses to achieve precision visual perception scale control is proposed . the comparison examples are given thereinto
首先，针对这种在平板显示器显示技术方面占主导地位的驱动方法进行细致的研究，在此基础上根据函数逼近理论提出了平板显示器时间片扫描的混合拟合逼近驱动方法，该方法采用多组相异子集_ i ~ *完成高精度的符合视觉感觉的灰度级控制。 - It ' s easy to translate the node of regression tree into radial basis function . after the nodes of regression tree are visited , we can generate a set of radial basis functions from which the final network can be selected . this algorithm fits to the application of function approximation , image procession and so on
回归树的结点可以很容易的转换为径向基函数，通过对回归树结点的访问，可以选择出使网络达到最优的基函数集，形成最终的网络，此算法可以很好的应用到函数逼近、图像处理等方面。 - 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
论文首先阐述了神经网络的基本理论,包括神经元模型,神经网络的基本结构和神经网络的学习方法;分析了具有函数逼近能力的bp网络和径向基函数( rbf )网络实质、技术实现问题,并研究了bp和rbf网络学习算法。 - 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
从理论上分析了小波变换和神经网络的函数逼近能力以及它们之间的内在联系，并且利用不同的激励函数构造相应的小波函数；对小波网络在电力系统故障信号分类和故障数据压缩方面的应用进行了研究和数字仿真；提出了另外两种不同的多小波网络，对一些小波网络和多小波网络的函数逼近能力，提出了相应的定理并进行了证明。 - 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
文中分析了此种小波网络的函数逼近能力，从网络泛化能力的角度，给出了网络结构具体的设计方法，提出了一种学习速度更快的新的参数混合学习算法。 - A drive method of unequalized clock counter in panel display which uses no dissimilarity @ subclass to achieve precision unequalized clock counter correction based on functions approximation theory is proposed . the new method is acquired based on the particular analysis results of the display drive design projects which adopted counter drive method in which the balance between the display image quality and the cost of drive circuit is given . finally , synthesis comparison examples are given
针对目前以该方法为基础普遍采用的不同技术方案进行详尽的分析，根据分析的结果阐明了其在图像显示质量和驱动代价方面的优缺点，在此基础上基于函数逼近理论提出了一种平板显示器计数器非均匀时钟驱动方法，该方法在计数器时钟上采用非相异子集完成高精度的非均匀时钟计数器校正。
The need for function approximations arises in many branches of applied mathematics, and computer science in particular. In general, a function approximation problem asks us to select a function among a well-defined class that closely matches ("approximates") a target function in a task-specific way.
详细百科解释
- function approximationとは意味：関数近似｛かんすう きんじ｝
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Last modified time:Mon, 11 Aug 2025 00:29:56 GMT
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