Prediction of phase equilibrium properties for complicated macromolecular systems by HGALM neural networks

Xuezhong He, Xiangping Zhang, Soujiang Zhang*, Jindun Liu, Chunshan Li

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Traditional error back propagation is a widely used training algorithm for feed forward neural networks (FFNNs). However, it generally encounters two problems of slow learning rate and relative low accuracy. In this work, a hybrid genetic algorithm combined with modified Levenberg-Marquardt algorithm (HGALM) was proposed for training FFNNs to improve the accuracy and decrease the time depletion comparing to the traditional EBP algorithm. The FFNNs based on HGALM were used to predict the binodal curve of water-DMAc-PSf system and protein solubility in lysozyme-NaCl-H2O system. The results would be used for guiding experimental researches in preparation of asymmetry polymer membrane and optimization of protein crystal process.

Original languageEnglish
Pages (from-to)52-57
Number of pages6
JournalFluid Phase Equilibria
Volume238
Issue number1
DOIs
StatePublished - 25 Nov 2005
Externally publishedYes

Keywords

  • Feed forward neural networks
  • Genetic algorithm
  • Polymer system
  • Prediction
  • Protein system

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