IT-701 RGPV B.Tech
Soft Computing (SC)
Previous year papers and the official syllabus are free to read, no account needed.
Previous Year Papers
4 papers, latest Dec 2025
Syllabus, notes and important questions for IT-701
Syllabus units
Topics as the official RGPV syllabus lists them.
Unit 1 Introduction to Neural Network 10 topics
- Concept
- Biological neural network
- Comparison of ANN with biological NN
- Evolution of artificial neural network
- Basic models
- Types of learning
- Linear separability
- XOR problem
- McCulloch-Pitts neuron model
- Hebb rule
Unit 2 Supervised Learning 9 topics
- Perceptron learning
- Single layer/multilayer
- Adaline
- Madaline
- Back propagation network
- RBFN
- Application of Neural network in forecasting
- Data compression
- Image compression
Unit 3 Unsupervised learning 6 topics
- Introduction
- Fixed weight competitive nets
- Kohonen SOM
- Counter Propagation networks (Theory, Architecture, Flow Chart, Training Algorithm and applications)
- Introduction to Convolutional neural networks (CNN)
- Recurrent neural networks (RNN)
Unit 4 Fuzzy Set 9 topics
- Introduction
- Basic Definition and Terminology
- Properties and Set‑theoretic Operations
- Fuzzy Relations
- Membership Functions and their assignment
- Fuzzy rules and fuzzy Reasoning
- Fuzzy if‑then Rules
- Fuzzy Inference Systems
- Application of Fuzzy logic in solving engineering problems
Unit 5 Genetic Algorithm 8 topics
- Introduction to GA
- Simple Genetic Algorithm
- Terminology and operators of GA (individual, gene, fitness, population, data structure, encoding, selection, crossover, mutation, convergence criteria)
- Reasons for working of GA and Schema theorem
- GA optimization problems like TSP (Travelling salesman problem)
- Network design routing
- Introduction to Ant Colony optimization (ACO)
- Particle swarm optimization (PSO)
Exam insights
Counted from 4 past IT-701 papers.
Marks asked per paper, by unit
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Unit 1 Introduction to Neural Network 16 marks
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Unit 2 Supervised Learning 30 marks
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Unit 3 Unsupervised learning 18 marks
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Unit 4 Fuzzy Set 21 marks
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Unit 5 Genetic Algorithm 18 marks
Average marks of the questions from each unit in one paper. You answer only some of them, so the total is more than the paper’s maximum.
Asked again and again
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Explain the Kohonen Self-Organizing Map (SOM) network, its architecture and working processes/algorithm with neat diagram and flowchart.
Asked 5 times: 2025, 2024, 2023, 2020
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Explain the backpropagation algorithm and its phases for training multilayer neural networks
Asked 3 times: 2025, 2024
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Explain the architecture (with sketch) and training/testing algorithm of ADALINE.
Asked 3 times: 2025, 2023