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  径向基
    Application on Foreign Exchange Reserves' Forecast by RBF Network in China
    基于径向基神经网络的我国外汇储备规模预测
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    This paper, the writers assign different weight to different data according to it’s importence and put forward the weighted support vector machines to predict the stock index, it performs well comparing with RBF network.
    而神经网络则采用经验风险最小化原则,会出现过学习现象. 根据证券指数等时间序列数据的特点即近期数据要比远期数据重要,重要数据点要求比较小的误差而提出了加权支持向量机算法,与径向基神经网络相比较,加权支持向量机在证券指数预测方面表现出了良好的泛化性能.
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  rbf
At the same time, linear regression, nonlinear regression and radial basis function (RBF) neural network models are set up to evaluate weld quality between the selected parameters and tensile-shear strength.
      
For the RBF neural network model, which is more effective for monitoring weld quality than the others, the average error validated is 2.88% and the maximal error validated is under 10%.
      
The provision of residents of the European North of Russia with vitamin B2 (riboflavin (RBF)) and the activity of the erythrocytic RBF-dependent enzyme glutathione reductase (EC 1.6.4.2) were studied.
      
in residents of the European North of juvenile and senile age The provision with RBF showed a tendency toward a decrease as compared to other age groups of the population.
      
The parameters of provision with RBF were correlated significantly with the level of physical activity, alcohol status, and season of the year.
      
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RBF neural network is used in stock market trend prediction. The algorithm for s electing the radial basis function center is the nearest neighbor clustering al gorithm. The modeling and forecasting results about Shanghai stock market price index and Yuyang fund price show that the network has reinforcement learning cap acities and mapping ability. Ideal results are obtained in stock market trend pr ediction.

将 RBF神经网络应用在股市趋势预测中 ,RBF网络中心点的选取采用最近邻聚类学习算法 ,以上证指数和基金裕阳为对象进行建模与预测 ,结果表明 ,此种网络具有较好的学习和泛化能力 ,在股市趋势预测中取得了较好的效果。

A method of stock price prediction is presented by hypothesis of stock market being non-linear dynamic system and analyzing method of chaos theory for chaos time series in this paper. Meanwhile, structures of radial basic function (RBF) network and pairs of training samples are determined by embedding dimension and delay time of reconstruct phase space respectively. Predicting results for real world stock time series show that the method is able to do effectively short-term prediction. In comparison with...

A method of stock price prediction is presented by hypothesis of stock market being non-linear dynamic system and analyzing method of chaos theory for chaos time series in this paper. Meanwhile, structures of radial basic function (RBF) network and pairs of training samples are determined by embedding dimension and delay time of reconstruct phase space respectively. Predicting results for real world stock time series show that the method is able to do effectively short-term prediction. In comparison with traditional forward feedback neural network (BP), the method can make better predicting performance, thus it can be widely used in stock price prediction.

根据股票市场是非线性动力系统的假设,利用混沌理论对混沌时间序列的分析方法,提出了股票价格预测方法。同时利用重构相空间的嵌入维数和延迟时间分别确定经向基函数模型网络的结构和训练样本对,对实际的股票时间序列预测结果表明,该方法能有效地进行短期预测,并与前馈神经网络模型相比,可得到较好的预测结果,因而在股票时间序列预测中有广泛的实用价值。

It is highly important for modern commercial banks to effectively control credit risks. Credit risk system is a very complicated non-linear dynamic system. A credit risk forecast and control model based on RBF neural network with the characteristics of adaptive learning, parallel distribution processing, strong robustness and fault tolerance has been found and used to make a theoretical probe into non-linear intelligence control of credit risks. Simulation indicates that credit risks can be limited within...

It is highly important for modern commercial banks to effectively control credit risks. Credit risk system is a very complicated non-linear dynamic system. A credit risk forecast and control model based on RBF neural network with the characteristics of adaptive learning, parallel distribution processing, strong robustness and fault tolerance has been found and used to make a theoretical probe into non-linear intelligence control of credit risks. Simulation indicates that credit risks can be limited within an optimum risk range. So, the forecast and control model is applicable to commercial banks for credit risk control.

对信用风险的有效控制与管理,在现代商业银行日常运行过程中具有举足轻重的地位。基于信用风险系统是一个高度复杂的非线性动态系统,利用神经网络的自适应学习、并行分布处理和较强的鲁棒性及容错性等特性,建立基于RBF神经网络的信用风险预测控制模型,从理论上探寻信用风险非线性智能控制。仿真试验表明,信用风险度能被控制在以最佳风险度为中心的一定范围内。因此,该预测控制系统适合于商业银行信用风险的控制。

 
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