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Chapter 3: Artificial neural networks Introduction; ANN representations, Perceptron Training, Multilayer networks and Backpropagation algorithm, Remarks on the Backpropagation algorithm, Neural network application development, Benefits and limitations of ANN, ANN Applications. | Chapter 3 ARTIFICIAL NEURAL NETWORKS Outline 1. Introduction 2. ANN representations 3. Perceptron Training 4. Multilayer networks and Backpropagation algorithm 5. Remarks on the Backpropagation algorithm 6. Neural network application development 7. Benefits and limitations of ANN 8. ANN Applications INTRODUCTION Biological Motivation Human brain is a densely interconnected network of approximately 1011 neurons, each connected to, on average, 104 others. Neuron activity is excited or inhibited through connections to other neurons. The fastest neuron switching times are known to be on the order of 10-3 sec. The cell itself includes a nucleus (at the center). To the right of cell 2, the dendrites provide input signals to the cell. To the right of cell 1, the axon sends output signals to cell 2 via the axon terminals. These axon terminals merge with the dendrites of cell 2. Portion of a network: two interconnected cells. Signals can be transmitted unchanged or they . | Chapter 3 ARTIFICIAL NEURAL NETWORKS Outline 1. Introduction 2. ANN representations 3. Perceptron Training 4. Multilayer networks and Backpropagation algorithm 5. Remarks on the Backpropagation algorithm 6. Neural network application development 7. Benefits and limitations of ANN 8. ANN Applications INTRODUCTION Biological Motivation Human brain is a densely interconnected network of approximately 1011 neurons, each connected to, on average, 104 others. Neuron activity is excited or inhibited through connections to other neurons. The fastest neuron switching times are known to be on the order of 10-3 sec. The cell itself includes a nucleus (at the center). To the right of cell 2, the dendrites provide input signals to the cell. To the right of cell 1, the axon sends output signals to cell 2 via the axon terminals. These axon terminals merge with the dendrites of cell 2. Portion of a network: two interconnected cells. Signals can be transmitted unchanged or they can be altered by synapses. A synapse is able to increase or decrease the strength of the connection from the neuron to neuron and cause excitation or inhibition of a subsequence neuron. This is where information is stored. The information processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. One motivation for ANN is to capture this kind of highly parallel computation based on distributed representations. 2. NEURAL NETWORK REPRESENTATION An ANN is composed of processing elements called or perceptrons, organized in different ways to form the network’s structure. Processing Elements An ANN consists of perceptrons. Each of the perceptrons receives inputs, processes inputs and delivers a single output. The input can be raw input data or the output of other perceptrons. The output can be the final result (e.g. 1 means yes, 0 means no) or it can be inputs to other perceptrons. .
