A Comprehensive Guide to Neural Network Modeling

Description

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. 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 Blank Page Blank Page