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A Comprehensive Guide to Neural Network Modeling

Om A Comprehensive Guide to Neural Network Modeling

As artificial neural networks have been gaining importance in the field of engineering, this compilation aims to review the scientific literature regarding the use of artificial neural networks for the modelling and optimization of food drying processes. The applications of artificial neural networks in food engineering are presented, particularly focusing on control, monitoring and modelling of industrial food processes. The authors emphasize the main achievements of artificial neural network modelling in recent years in the field of quantitative structure -- activity relationships and quantitative structure -- retention relationships. In the closing study, artificial intelligence techniques are applied to river water quality data and artificial intelligence models are developed in an effort to contribute to the reduction of the cost of future on-line measurement stations.

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  • Språk:
  • Engelska
  • ISBN:
  • 9781536184662
  • Format:
  • Häftad
  • Sidor:
  • 155
  • Utgiven:
  • 18. september 2020
  • Mått:
  • 152x229x0 mm.
  • Vikt:
  • 260 g.
  Fri leverans
Leveranstid: 2-4 veckor
Förväntad leverans: 20. december 2024
Förlängd ångerrätt till 31. januari 2025

Beskrivning av A Comprehensive Guide to Neural Network Modeling

As artificial neural networks have been gaining importance in the field of engineering, this compilation aims to review the scientific literature regarding the use of artificial neural networks for the modelling and optimization of food drying processes. The applications of artificial neural networks in food engineering are presented, particularly focusing on control, monitoring and modelling of industrial food processes. The authors emphasize the main achievements of artificial neural network modelling in recent years in the field of quantitative structure -- activity relationships and quantitative structure -- retention relationships. In the closing study, artificial intelligence techniques are applied to river water quality data and artificial intelligence models are developed in an effort to contribute to the reduction of the cost of future on-line measurement stations.

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