Artificial Neural Networks in Chemometrics: History, Examples

نویسندگان

  • F. Marini
  • R. Bucci
  • A. L. Magrì
  • A. D. Magrì
چکیده

Artificial Neural Networks (ANNs) are non-linear computational tools suitable to a great host of practical application due to their flexibility and adaptability. However, their application to the resolution of chemometric problems is relatively recent (early ‘90s). In this communication, different artificial neural networks architectures are presented and their application to different kinds of chemometric problems (mainly classification and regression) is discussed by means of examples taken from the authors’ experience, stressing the pros and cons of ANNs with respect to traditional chemometric techniques

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تاریخ انتشار 2006