AI Techniques — Summer 2025

Explain the following terms related to GA: (1) Fitness Function (2) Selection (3) Reproduction.

What is Artificial Intelligence? Briefly describe characteristics and advantages of it.

What is learning in ANN? Discuss various types of learning methods in ANN.

Write a note on feed forward neural network architecture with suitable diagram.

Write a note on Elman back propagation neural network.

State and explain the different types of learning methods employed for neural Networks.

Discuss various steps required to solve optimization problem using GA.

What is significance of cross-over related to GA. Explain any three cross-over techniques with the help of suitable example.

Explain the following genetic representations with appropriate illustrations.

Octal (b) Binary (c) Hexadecimal

Differentiate between fuzzy and crisp logic. Also explain Fuzzy operators like Union, Intersection and complement with corresponding examples.

Explain GA based Unit Commitment in power system.

Compare various defuzzication methods based on various associated aspects.

Represent vector and Matrix notation of 2 neurons in input layer, 2 neurons in hidden layer and 2 neurons in the output layer.

Define the term Membership Function related to Fuzzy logic. Discuss various types of Membership Functions in brief.

Discuss the application or ANN for load forecasting. Specify NN structure, inputs, output, training algorithm and convergence of network with testing data.

What is the potential utility of fuzzy logic in power system voltage control?

What does GA mutation mean? How can mutation be applied, Describe the different programming logics employed in the same.