ARTIFICIAL NEURAL NETWORK
Course Objective:
To introduce the concept of artificial network as an alternative options for solving engineering problems.
 Working with data (4 hours)
 Data types; data, information and knowledge; concept of data mining; Dimension reduction of data matrix: Principal component analysis.
 Introducton of Artifical Neural Network (ANN)
(6 hours)
 Biological Analogy, Historical development; ANN terminology; network structure; basis functions; activation functions; advantages of ANN; application areas of ANN.
 Learning process & optimization techniques
(10 hours)
 supervised learning: Error correction learning, memory based learning
 unsupervised learning: Hebian learning, competitive learning
 learning with critic
 gradient descent and least mean square
 Derivative free optimization techniques: advantages of derivative free techniques; genetic algorithm: fundamental of GA and biological background.; GA operators & GA operation.
 Simulated annealing: theoretical background and algorithm.
 Supervised network (8 hours)
 McCullotch and Pitt Neuron; LTUs, simple perceptron and perceptorn learning. Limitation of simple percepron.
 ADDALINE network and delta rule
 Multilayer perceptron: Needs of multilayer network, generalized delta rule (error‐backpropagation), effect of momentum term and learning rate
 Error backpropagation learning of sigmoidal units; drawbacks of error‐backpropagation
 Unsupervised network (4 hours)
 competitive network: network structure & working;
 dissimilarity measures;
 Self Organizing Map and Kohonen learning;
 applications
 Special networks (4 hours)
 Radial basis function network: structure and working procedure, advantages
 LVQ network: structure and learning approach
 Hopefield network
 Autoassociative memory network: general structure and Purpose, Autocorrelator; Heterocorrelator
 Application of ANN in Electrical Engineering (8 hours)
 Fault diagnosis
 Control application
 Network planning
 Forecasting task.
 State estimation
 Unit commitment
Practical:
 Computer simulation of PCA.
 Computer simulation of perceptron network
 computer simulation of back propagation network
 A Shortterm case study demonstrating ANN application for a specific purpose.
References:
 Simon Hykin, "Neural networks A Comprehensive Foundation", second edition; Pearson Education.
