Course Objective:
To introduce the concept of artificial network as an alternative options for solving engineering problems.

  1. Working with data (4 hours)
    1. Data types; data, information and knowledge; concept of data mining; Dimension reduction of data matrix: Principal component analysis.

  2. Introducton of Artifical Neural Network (ANN) (6 hours)
    1. Biological Analogy, Historical development; ANN terminology; network structure; basis functions; activation functions; advantages of ANN; application areas of ANN.

  3. Learning process & optimization techniques (10 hours)
    1. supervised learning: Error correction learning, memory based learning
    2. unsupervised learning: Hebian learning, competitive learning
    3. learning with critic
    4. gradient descent and least mean square
    5. Derivative free optimization techniques: advantages of derivative free techniques; genetic algorithm: fundamental of GA and biological background.; GA operators & GA operation.
    6. Simulated annealing: theoretical background and algorithm.

  4. Supervised network (8 hours)
    1. McCullotch and Pitt Neuron; LTUs, simple perceptron and perceptorn learning. Limitation of simple percepron.
    2. ADDALINE network and delta rule
    3. Multilayer perceptron: Needs of multilayer network, generalized delta rule (error‐backpropagation), effect of momentum term and learning rate
    4. Error backpropagation learning of sigmoidal units; drawbacks of error‐backpropagation

  5. Unsupervised network (4 hours)
    1. competitive network: network structure & working;
    2. dissimilarity measures;
    3. Self Organizing Map and Kohonen learning;
    4. applications

  6. Special networks (4 hours)
    1. Radial basis function network: structure and working procedure, advantages
    2. LVQ network: structure and learning approach
    3. Hopefield network
    4. Autoassociative memory network: general structure and Purpose, Autocorrelator; Heterocorrelator

  7. Application of ANN in Electrical Engineering (8 hours)
    1. Fault diagnosis
    2. Control application
    3. Network planning
    4. Forecasting task.
    5. State estimation
    6. Unit commitment


  1. Computer simulation of PCA.
  2. Computer simulation of perceptron network
  3. computer simulation of back propagation network
  4. A Shortterm case study demonstrating ANN application for a specific purpose.


  1. Simon Hykin, "Neural networks A Comprehensive Foundation", second edition; Pearson Education.

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