Stages of Optimization of Random Process Forecasting Algorithm by Analogs Complexation

A. G. IVAKHNENKO; N. N. BOGACHENKO; LI TIAN MIN

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Stages of Optimization of Random Process Forecasting Algorithm by Analogs Complexation

机译：类比络合优化随机过程预测算法的阶段

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The optimization problem of random process forecasting algorithms by analogs complexation is considered. By selecting the discrete values of the number of factors, the number of time instants taken into account, the number of analogs being complexated, the values of the analogs weight coefficients significant accuracy is attained. The accuracy can be increased by making use of neuronet with active neurons.

机译：考虑了类似物复杂化的随机过程预测算法的优化问题。通过选择因子数量的离散值，考虑的时间数量，复杂的类似物数量，可以得到相当准确的类似物权重系数值。通过使用带有活动神经元的神经网络可以提高准确性。

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来源
《Journal of automation and information sciences》 |2000年第7期|p.78-83|共6页
作者
A. G. IVAKHNENKO; N. N. BOGACHENKO; LI TIAN MIN;
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收录信息美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类自动化技术、计算机技术;
关键词
random process; forecasting algorithm; weight coefficients;

机译：随机过程预测算法;权重系数;

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Stages of Optimization of Random Process Forecasting Algorithm by Analogs Complexation

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