高等学校化学学报 ›› 2004, Vol. 25 ›› Issue (11): 2004-2009.

• 研究论文 • 上一篇    下一篇

中药复杂组效关系的变结构神经网络辨识方法

范骁辉, 程翼宇   

  1. 浙江大学药物信息学研究所, 杭州310027
  • 收稿日期:2004-02-12 出版日期:2004-11-24 发布日期:2004-11-24
  • 基金资助:

    国家自然科学基金重大研究计划重点项目(批准号:90209005);国家重点基础研究发展计划项目(批准号:G1999054405)资助

Recognition Method of a New Feedforward Neural Network with Adaptive Structure for QCAR Modeling of Chinese Medicine

FAN Xiao-Hui, CHENG Yi-Yu   

  1. Pharmaceutical Informatics Institute, Zhejiang University, Hangzhou 310027, China
  • Received:2004-02-12 Online:2004-11-24 Published:2004-11-24

摘要: 针对中药复杂组效关系的辨识问题,研究了变结构多层前馈神经网络,推导出一种新型的变结构网络学习算法,成功地应用于中药川芎药效活性预测计算.该方法从一个规模较小的网络出发,当网络无法达到预定的学习精度时,自动增加隐含层神经元个数,并在原有学习结果的基础上确定新的网络参数,自适应地确定前馈神经网络结构,可用于处理复杂化学模式信息.计算机仿真实验结果表明,该方法能有效地确定多层前馈神经网络的最佳结构,提高网络学习效率和函数逼近精度,解决复杂非线性函数映射关系准确建模问题.

关键词: 神经网络, 网络结构, 定量组效关系, 药效预测, 中药

Abstract: In general, the performances of Multilayer Feedforward Neural Network(MFNN), e.g. function approximation accuracy and network efficiency, are not often sufficiently satisfactory, when it is used for modeling of complex non-linear chemical system. In this work, a novel learning approach, which can adaptively construct the optimal architecture of MFNN, is developed for modeling Quantitative Composition-Activity Relationship(QCAR) of Chinese Medicine, and it is successfully applied to predicting the bioactivity of Chuanxiong. The approach taken by the methodology described here is to train an initial neural network that is smaller than necessary and then adaptively increase the number of hidden layer′s neural nodes when the approximation accuracy is insufficient. Once a new hidden node has been added to network, weights of original nodes are frozen. The parameters of the new node, new-added weights as well as the corresponding change of old weights, are determined on the basis of current learning results. It is also verified by simulated experiments that the presented approach can satisfyingly determine the optimal architecture of MFNN, and its learning efficiency is high. As a consequence, the approach can be used for modeling of complex non-linear chemical system.

Key words: Neural network, Network architecture, QCAR modeling, Activity prediction, Chinese medicine

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