A Comprehensive Guide to Neural Network Modeling

✍ Scribed by Steffen Skaar (editor)

• Publisher: Nova Science Pub Inc
• Year: 2020
• Tongue: English
• Leaves: 176
• Category: Library

✦ Synopsis

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 modeling of industrial food processes. The authors emphasize the main achievements of artificial neural network modeling 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.

✦ Table of Contents

Contents
Preface
Chapter 1
Application of Artificial Neural Networks (ANNS) Modelling in Drying Technology of Food Products: A Comprehensive Survey
Abstract
Introduction to the Drying of Foods
The Need to Control Drying Parameters
Generation I
Generation II
Generation III
Generation IV
The Principles of ANN Modelling and Algorithms
Model of a Biological Neuron
The Perceptron Model
Training Algorithms
Supervised Learning
Reinforced Learning
Unsupervised Mode
Network Architectures
Feedforward Artificial Neural Network
A Single Layer Perceptron Feedforward ANN Model
Multiple Layer Feedforward ANN
Recurrent Neural Networks (RNNs)
Adaptive Neural-Fuzzy Interface System (ANFIS)
Hybrid Neural Network (HNN) Model
Hybrid Mathematical-Neural Model
Selection of Optimal ANN
The Application of ANN to the Drying of Foods
Application of ANNs to Predict the Drying Properties of Agricultural Products
Application of the ANFIS Model to Predict the Drying Properties of Agricultural Products
Application of Different Models to Predict Energy and Exergy Parameters of Agricultural Products in Dryers
Applied Recommendations and Considerations
Acknowledgments
References
Chapter 2
Application of Artificial Neural Networks in the Food Engineering
Abstract
Introduction
1. Application of Artificial Neural Networks (ANNs) in food Technology and Engineering
1.1. Virtualization and Visualization Using ANNs
1.2. Food Quality and Characteristics
1.3. Application of Near Infrared Spectroscopy, Coupled with Multivariate Tools and ANNs Modelling, for the Assessment of Food Quality
1.3.1. Application of ANNs on Experimental Data and Its Efficiency in Output Prediction
Conclusion
References
Chapter 3
Artificial Neural Networks as a Chemometric Tool in Analysis of Biologically Active Compounds
Abstract
Introduction
Artificial Neural Networks as a Regression Tool
Preprocessing of Biological and Chromatographic Data in ANN Modeling
Artificial Neural Networks in Quantitative Structure - (Chromatographic) Retention Relationships
Artificial Neural Networks in Quantitative Structure - (Biological) Activity Relationships
Conclusion
Acknowledgment
References
Chapter 4
River Water Quality Modelling Using Artificial Intelligence Techniques
Abstract
Introduction
Methods
Modelling Approaches
Artificial Neural Network (ANN)
Support Vector Machine (SVR)
Least Squares Support Vector Machine (LS-SVM)
Extreme Learning Machine (ELM)
Kernel Extreme Learning Machine
Data Collection and Study Area
Results
Artificial Neural Network (ANN) Model Results
Support Vector Regression (SVR) Model Results
Least Squares Support Vector Machine (LS–SVM) Model
Extreme Learning Machine and Kernel Extreme Learning Machine Results
Model Computation
Conclusion
References
Index
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