18EE745 Artificial Neural Network With Applications to Power Systems syllabus for EE

Fundamental Concepts and Models of Artificial Neural Systems

Biological Neurons and their artificial models – Biological Neuron, McCulloch-Pitts Neuron Model, Neuron modeling for Artificial neural systems. Models for Artificial Neural Networks – Feed forward Network, Feedback network.

Neural Processing, Learning and Adaptation, Neural Network Learning Rules

Neural Processing. Learning and Adaptation – Learning as Approximation or Equilibria Encoding, Supervised and Unsupervised Learning. Neural Network Learning Rules – Hebbian Learning Rule, Perceptron Learning Rule, Delta Learning Rule, Widrow-Hoff Learning Rule, Correlation Learning Rule, Winner-Take-All Learning Rule, Outstar Learning Rule, Summary of Learning Rules.

Multilayer Feedforward Networks

Feedforward Recall and Error Back-Propagation Training – Feedforward Recall, Error Back-Propagation Training, Training Errors and Multilayer Feedforward Networks as Universal Approximators (Excluding Examples). Learning Factors – Initial Weights, Cumulative Weight Adjustment versus Incremental Updating, Steepness of the Activation Function, Learning Constant, Momentum Method, Network Architectures Versus Data Representation, Necessary Number of Hidden Neurons.

Neural Network and its Ancillary Techniques as Applied to Power Systems

Introduction, Learning versus Memorization, Determining the Best Net Size, Network Saturation, Feature Extraction, Inversion of Neural Networks, Alternative Training Method: Genetic Based Neural Network, Fuzzified Neural Network.

Control of Power Systems

Introduction, Background, Neural Network Architectures for modeling and control, Supervised Neural Network Structures, Diagonal Recurrent Neural Network based Control System, Convergence and Stability.

Course Outcomes:

At the end of the course the student will be able to:

• Develop Neural Network and apply elementary information processing tasks that neural network can solve.
• Develop Neural Network and apply powerful, useful learning techniques.
• Develop and Analyze multilayer feed forward network for mapping provided through the first network layer and error back propagation algorithm.
• Analyze and apply algorithmic type problems to tackle problems for which algorithms are not available.
• Develop and Analyze supervised/unsupervised, learning modes of Neural Network for different applications.

Question paper pattern:

• The question paper will have ten full questions carrying equal marks.
• Each full question will be for 20 marks.
• There will be two full questions (with a maximum of four sub- questions) from each module.
• Each full question will have sub- question covering all the topics under a module.
• The students will have to answer five full questions, selecting one full question from each module.

Textbooks

1 Introduction to Artificial Neural Systems. Jacek M. Zurada JAICO Publishing House 2006

2 Artificial Neural Networks with Applications to Power Systems Edited by – Mohamed El – Sharkawi and Dagmar Niebur IEEE, Inc. 1996