Neural Networks
C.R. Chen and H.S. Ramaswamy
CONTENTS
4.1 Introduction...108 4.2 The Inspiration from Biological Neurons...109 4.3 Principles of a Basic Artificial Model ...110 4.3.1 Neural Network Architecture ...110 4.3.2 Artificial Neurons ...111 4.3.3 Learning Rules...112 4.4 Developing Neural Networks ...113 4.5 Applications in Food Thermal Processing...116
4.5.1 Neural Network Modeling of Heat Transfer to Liquid Particle Mixtures in Cans Subjected to End-over-End
Processing ...117 4.5.2 A Neuro-Computing Approach for Modeling of Residence
Time Distribution of Carrot Cubes in a Vertical
Scraped-Surface Heat Exchanger...119 4.5.3 Modeling and Optimization of Constant Retort Temperature
Thermal Processing Using Coupled Neural Networks
and Genetic Algorithms...119 4.5.4 ANN Model-Based Multiple-Ramp Variable Retort
(MRV) Temperature Control for Optimization
of Thermal Processing...122 4.5.5 Analysis of Critical Control Points in Deviant Thermal
Processes Using Artificial Neural Networks...125 4.6 Conclusions...126 References ...129
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108 Thermal Food Processing: New Technologies and Quality Issues
4.1 INTRODUCTION
Artificial neural networks (ANNs) are being successfully applied for a wide range of problem domains in diverse areas, including engineering, physics, finance, medicine, and others related to purposes of prediction, classification, or control.
This extensive success can be attributed to many factors:
1. Power of modeling — Neural networks are very sophisticated tech-niques capable of modeling extremely complex functions. A priori knowledge of the system is not needed for constructing the ANN because the ANN will learn its internal representation from the input/output data of its environment and response.
2. Ease of use — Neural networks learn by example. The user of neural networks gathers representative data and then invokes training algo-rithms to automatically learn the structure of the data. Although the user does need to have some heuristic knowledge of how to select and prepare data, how to select an appropriate neural network, and how to interpret the results, the level of user knowledge needed to successfully apply neural networks is much lower than that needed to use some more traditional nonlinear statistical methods.
3. High computational speed — The ANN is an inherently parallel archi-tecture. The result comes from the collective behavior of a large number of simple parallel processing units. Therefore, once trained, ANN can calculate results from a given input very quickly. Because of this feature, ANNs have a greater potential to be used for the online control system than conventional modeling methods.
The concept of neural networks was based on the research in artificial intel-ligence, which was specifically intended to mimic the fault tolerance and capacity of biological neural systems by modeling the low-level structure of the brain.
Warren McCulloch and Walter Pitts1 in 1943 were the first to open the idea on how neurons might work, and they modeled a simple neural network using electrical circuits. As computers became more advanced in the 1950s, it was finally possible to simulate a hypothetical neural network. In 1959, Bernard Widrow and Marcian Hoff developed models called ADALINE and MADA-LINE.2,3 In 1962, the same authors developed a learning procedure that examined the value before the weight adjustment (i.e., 0 or 1), which was one of the important fundamentals to the following success of neural networks.4 However, the neural network concepts did not result in practical applications until the 1980s, when several new approaches, such as bidirectional lines, the hybrid network, and multilayer neural networks, were developed.2–6 In addition to these advances in algorithms, the rapid development of computer technologies, including both hardware and software, became an important driving force for neural networks as a computing technique to be used not only in computing science, but also in other areas as a tool for prediction, classification, and optimization.
Modeling Food Thermal Processes Using Artificial Neural Networks 109
4.2 THE INSPIRATION FROM BIOLOGICAL NEURONS
The human brain principally consists of over 10 billion neurons, each connected to about 10,000 other neurons. A typical biological neuron, as shown in Figure 4.1, contains neuronal cell bodies (soma), dendrites, and axons. Each neuron receives electrochemical inputs from other neurons at the dendrites. If the sum of these electrical inputs is sufficiently powerful to activate the neuron, it transmits an electrochemical signal along the axon and passes this signal to the other neurons, whose dendrites are attached at any of the axon terminals. These attached neurons may then fire. It is important to note that a neuron fires only if the total signal received at the cell body exceeds a certain level. The entire brain is composed of these interconnected electrochemical transmitting neurons. From a very large number of extremely simple processing units (each performing a weighted sum of its inputs, and then firing a binary signal if the total input exceeds a certain level), the brain manages to perform extremely complex tasks.
This is the model on which artificial neural networks are based. However, it should be noted that artificial neural networks only represent extremely simplified formal models of biological neurons and their interconnections, without making any attempt to model the biological system itself. Their importance lies in the fact that artificial networks are brain-inspired computational tools for solving complex problems.
FIGURE 4.1 The structure of a typical biological neuron.
Cell
Axon
Dendrite
110 Thermal Food Processing: New Technologies and Quality Issues
4.3 PRINCIPLES OF A BASIC ARTIFICIAL MODEL 4.3.1 NEURAL NETWORK ARCHITECTURE
Neural networks consist of a set of neurons, called processing units, which are arranged in several parallel layers. The most commonly used neural network archi-tecture is the multilayer feed-forward network using back-propagation of error in the learning mechanism, which is shown in Figure 4.2. This neural network has an input layer, two hidden layers, and one output layer. Each layer is essential to the operation of the network. A neural network can be viewed as a black box into which a specific input to each node in the input layer is sent. The network processes this information through the interconnections between nodes, but this entire processing step is hidden. Finally, the network gives an output from the nodes on the output layer. The function of each layer is described as follows:
• Input layer — Receives information from an external source and passes this information to the network for processing.
• Hidden layers — Receives information from the input layer and does all of the information processing, which is hidden from view. The number of hidden layers can be one to three, dependent on the problem being investigated.
• Output layer — Receives processed information from the network and sends the results out to an external receptor.
When the input layer receives the information from an external source, it will be activated and emit signals to its neighbors. The neighbors, which receive excitations from an input layer, in turn emit signals to their neighbors. Depending on the strength
FIGURE 4.2 A typical multilayer neural network with one hidden layer.
Input
Output
Hidden Y1
Ym X1
X2
Xj