Chapter 7: Neural Networks present about Neural Networks Representation, Appropriate problems for Neural Network Learning, Perceptrons, Multilayer Networks and the Backpropagation algorithm, Remarks on the Backpropagation algorithm, Neural network application development. | Chapter 7: Neural Networks Assoc. Prof. Dr. Duong Tuan Anh HCMC University of Technology July 2015 Outline 1. Neural Networks Representation 2. Appropriate problems for Neural Network Learning 3. Perceptrons 4. Multilayer Networks and the Backpropagation algorithm 5. Remarks on the Backpropagation algorithm 6. Neural network application development 7. Benefits and Limitations of Neural networks 8. Neural network applications 9. RBF neural network 1. 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 (. 1 means yes, 0 means no) or it can be inputs to other perceptrons. The network Each ANN is composed of a collection of perceptrons | Chapter 7: Neural Networks Assoc. Prof. Dr. Duong Tuan Anh HCMC University of Technology July 2015 Outline 1. Neural Networks Representation 2. Appropriate problems for Neural Network Learning 3. Perceptrons 4. Multilayer Networks and the Backpropagation algorithm 5. Remarks on the Backpropagation algorithm 6. Neural network application development 7. Benefits and Limitations of Neural networks 8. Neural network applications 9. RBF neural network 1. 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 (. 1 means yes, 0 means no) or it can be inputs to other perceptrons. The network Each ANN is composed of a collection of perceptrons grouped in layers. A typical structure is shown in . Note the three layers: input, intermediate (called the hidden layer) and output. Several hidden layers can be placed between the input and output layers. Figure 2 2. Appropriate Problems for Neural Network ANN learning is well-suited to problems in which the training data corresponds to noisy, complex sensor data. It is also applicable to problems for which more symbolic representations are used. The backpropagation (BP) algorithm is the most commonly used ANN learning technique. It is appropriate for problems with the characteristics: Input is high-dimensional discrete or real-valued (. raw sensor input) Output is discrete or real valued Output is a vector of values Possibly noisy data Long training times accepted Fast evaluation of the learned function required. Not important for humans to understand the weights Examples: Speech phoneme recognition Image classification Financial prediction 3. PERCEPTRONS A .
