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基于改进神经网络的实验方案优选系统

The Optimal Selection System of Experiment ConditionsBased on the Improved ANN

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【作者】 潘丹罗干英黄茜肖诗铁

【Author】 Pan Dan1) Luo Ganying1) Huang Qian1) Xiao Shitie2)(1)Dept.of Electronics and Communication Engineering;2)Dept.of Chemical Eng.,South China University of Technology,510641,Guangzhou,China)

【机构】 华南理工大学电子与通信工程系

【摘要】 构造了一类新的高效分段活化函数,很好地解决了BP算法学习收敛速度慢的问题,并提出了一种自适应调整网络参数的新算法,从而大大提高了算法的学习效率和综合性能.

【Abstract】 In this paper,not only a kind of new activation functions,called segment activation function(SAF),but also the improved back propagation algorithm in which networks parameters can be adaptively adjusted is proposed to solve two key problems:slow convergence and low learning efficiency which exist in the conventional BP ANN and restrict its applications,so the learning efficiency and comprehensive properties are greatly improved.Moreover,the procedure of modeling for the optional selection systems which have been applied to the optimal selection for fine chemical experiment conditions is discussed.The application results are very satisfactory.

  • 【文献出处】 暨南大学学报(自然科学与医学版) ,JOURNAL OF JINAN UNIVERSITY(NATURAL SCIENCE & MEDICINE EDITION) , 编辑部邮箱 ,1998年01期
  • 【分类号】TP18
  • 【被引频次】3
  • 【下载频次】47
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