SIOC English
化学学报
高级检索

化学学报 ›› 2013, Vol. 71 ›› Issue (07): 1053-1058.DOI: 10.6023/A13020193 上一篇    下一篇

研究论文

基于混沌自适应粒子群人工神经网络的气体在聚合物中的溶解模型

李孟山a,b, 黄兴元a, 柳和生a, 柳炳祥b, 武燕b, 艾凡荣a   

  1. a 南昌大学机电工程学院 南昌 330031;
    b 景德镇陶瓷学院信息工程学院 景德镇 333001
  • 投稿日期:2013-02-15 发布日期:2013-03-15
  • 通讯作者: 黄兴元, E-mail: huangxingyuan001@126.com E-mail:huangxingyuan001@126.com
  • 基金资助:

    项目受国家自然科学基金(No. 20664002)和南昌大学研究生创新专项资金(No. cx2012011)资助.

Solubility Prediction of Gases in Polymers based on Chaotic Self-adaptive Particle Swarm Optimization Artificial Neural Networks

Li Mengshana,b, Huang Xingyuana, Liu Heshenga, Liu Bingxiangb, Wu Yanb, Ai Fanronga   

  1. a Nanchang University, College of Mechanical and Electric Engineering, Nanchang 330031;
    b JingDeZhen Ceramic Institute, School of Information Engineering, JingDeZhen 333001
  • Received:2013-02-15 Published:2013-03-15
  • Supported by:

    Project supported by the National Natural Science Foundation of China (No. 20664002) and Graduate Student Innovation Fund by Nanchang University (No. cx2012011).

PDF

1488

被引次数

20 | 10

为提高溶解预测模型的效率和关联度, 建立基于混沌理论、自适应粒子群优化(PSO)算法和反向传播(BP)算法的混沌自适应PSO-BP神经网络模型, 并对二氧化碳(CO2)在聚苯乙烯(PS)和聚丙烯(PP)中、氮气(N2)在PS中的溶解度进行预测试验. 模型选用压力和温度作为输入参数, 使用试探法确定隐含层结点个数为8, 输出为预测的溶解度. 模型融合混沌理论、自适应PSO和BP算法各自的优势, 提高了训练速度和预测精度. 结果表明, 混沌自适应PSO-BP神经网络有很好的预测能力, 预测值与实验值相当吻合, 通过与传统BP神经网络和PSO-BP神经网络的比较可知, 其预测精度和相关性均明显较优, 预测平均绝对误差(AAD), 标准偏差(SD)和平方相关系数(R2)分别为0.0058, 0.0198和0.9914.

关键词: 溶解预测, 聚合物, 神经网络, 粒子群, 混沌理论

Solubility is one of the most important physicochemical properties of polymer compounds, which determines the compatibility of blending system. To enhance the performance of artificial neural networks (ANN) and improve the efficiency and correlation of prediction of gas solubility in polymers, in this work, a novel ANN model based on chaos theory, self-adaptive particle swarm optimization (PSO) algorithm and back propagation (BP) algorithm is proposed, hereafter called CSAPSO-BP ANN. In the CSAPSO-BP ANN, the conventional PSO algorithm is modified by using chaos theory and self-adaptive inertia weight factor to overcome its premature convergence problem. Then the CSAPSO-BP ANN trained by hybrid algorithm which combined the modified PSO and BP algorithm has been employed to investigate carbon dioxide (CO2) solubility in polystyrene (PS), polypropylene (PP) and nitrogen (N2) solubility in PS, respectively. The CSAPSO-BP ANN model which consisted of three layers with one hidden layer, two input nodes including temperature and pressure, 8 hidden nodes which obtained by heuristics and one output node that is the solubility of gases in polymers was designed. The model combined the abilities of chaos theory, PSO algorithm and BP algorithm, accelerated the training speed of ANN and improved the prediction accuracy. Results obtained in this work indicate that the CSAPSO-BP ANN is an effective method for prediction of gas solubility in polymers in a wide range of pressure and temperature. The comparison between different neural networks was carried out in detail to reveal the proposed CSAPSO-BP ANN outperforms the traditional BP NN and PSO-BP NN. The values of average absolute deviation (AAD), standard deviation (SD) and squared correlation coefficient (R2) are 0.0058, 0.0198 and 0.9914, respectively. The statistical data demonstrate that the CSAPSO-BP ANN model is a faster, more reliable and accurate method, and has an excellent prediction capability with high-accuracy and has a good correlation between prediction values and experimental data.

Key words: solubility prediction, polymer, neural network, particle swarm optimization, chaos theory