Название: A Comprehensive Guide to Neural Network Modeling
Автор: Steffen Skaar
Издательство: Nova Science Publishers, Inc.
Год: 2020
Страниц: 176
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

As Artificial Neural Networks (ANNs) 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 modeling of industrial food processes. The authors emphasize the main achievements of Artificial Neural Networks modeling in recent years in the field of quantitative structure–activity relationships and quantitative structure–retention relationships. In the closing study, Artificial Intelligence (AI) 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.

Artificial Neural Networks are of great interest because of their ability to solve problems connected to interpretation of results obtained by various analytical methods. These results sometimes differ from the ordinary form in term of vast number of results for one measurement. Examples of those results include Near Infrared Spectroscopy (NIRs) spectra or results that have to be in a specific interval. ANNs are composed of group of nonlinear regression and discrimination statistical methods and are often used for their ability of visualization and prediction which is based on their learned and trained knowledge. Use of ANNs has been widely studied since they correspond to computational systems that aim to imitate some properties of biological neurons. Basically, the ANN is a system which corresponds to human brain in term of neurons that are linked by synaptic connections.








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