title: [Back-propagation neural network and genetic algorithm for multi-objective optimization of extraction technology of Cortex Fraxini].
abstract: OBJECTIVE
  To introduce Back-propagation (BP) neural network and genetic algorithm for multi-objective optimization of extraction technology of Cortex Fraxini.
  
  
  METHODS
  BP neural network was established and optimized with uniform design. Genetic algotithm was used for multi-objective optimization of extraction technology of cortex fraxini.
  
  
  RESULTS
  the optimization of extraction was as follows: extraction temperature was 99 degrees C, concentration of EtOH was 50%, liquid-solid ratio was 7, extraction time was 94 min. The proportional error between predictive value and practical measured value was just -1.16% and -5.14%.
  
  
  CONCLUSIONS
  Back-propagation neural network and genetic algorithm for multi-objective optimization of extraction technology of cortex fraxini is advisable.
authors.affiliation: Medicament Department Of Longhua Hospital Affiliated to Shanghai Univesity of Traditional Chinese Medicine, Shanghai 200032, China. yangpluszhu@sina.com
authors.forename: Ming
authors.initials: M
authors.lastname: Yang
authors.forename: Min-ying
authors.initials: MY
authors.lastname: Yu
authors.forename: Xiu-feng
authors.initials: XF
authors.lastname: Shi
authors.forename: Yan-ping
authors.initials: YP
authors.lastname: Teng
mesh_terms: D031319:Aesculus; D000465:Algorithms; D004365:Drugs, Chinese Herbal / Q000737:chemistry*; D016571:Neural Networks, Computer
chemical_list: D004365:Drugs, Chinese Herbal; C517354:horse chestnut seed