Artificial Neural Networks in Food Processing: Modeling and Predictive Control

✍ Scribed by Mohamed Tarek Khadir

Publisher: De Gruyter
Year: 2021
Tongue: English
Leaves: 200
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

Artificial Neural Networks (ANNs) is a powerful computational tool to mimic the learning process of the mammalian brain. This book gives a comprehensive overview of ANNs including an introduction to the topic, classifications of single neurons and neural networks, model predictive control and a review of ANNs used in food processing. Also, examples of ANNs in food processing applications such as pasteurization control are illustrated.

Discusses theory and current applications of ANNs in food processing technology.
A valuable tool for students and researchers in food science.
Code available for download.

✦ Table of Contents

Acknowledgement
Contents
Introduction
1 Biological inspiration and single artificial neurons
2 Artificial neural networks for food processes: a survey
3 Multi-layered perceptron
4 Radial basis function networks
5 Self-organising feature maps or Kohonen maps
6 Deep artificial neural networks
7 Overview of model predictive control theory and applications in food science using ANN
Index

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