A Comprehensive Guide to Neural Network Modeling

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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