This question paper is from the B.Tech Semester III examination for the subject Introduction to Soft Computing (KOE036).
It carries 100 marks and covers the foundational concepts of artificial neural networks, fuzzy logic, genetic algorithms, neuro-fuzzy systems, and machine-learning-based decision-making.
The exam is structured into three sections – A, B, and C, progressing from basic conceptual questions to in-depth analytical and application-based problems.
SECTION A – Short Conceptual Questions (20 Marks)
This section includes 10 questions (2 marks each) focusing on basic definitions and core ideas of soft computing, such as:
Counting layers in neural networks
Purpose of single-layer perceptron
Human-like intelligence through fuzzy logic
Improving decision-making systems
Working of ANFIS in MATLAB
How classification & regression trees operate
Most expensive genetic algorithm operation
Fitness calculation in GA
Searching technique used in GA
Technologies behind neuro-fuzzy hybrid systems
This section tests fundamental understanding and recall of soft computing concepts.
SECTION B – Analytical & Application Questions (30 Marks)
Students must attempt any three out of five questions. Topics include:
Definition & characteristics of Artificial Neural Networks
Operations in fuzzy sets with examples
Applications of Kohonen’s Self-Organizing Map & properties of ART
Travelling Salesman Problem using genetic algorithms
Solving a nonlinear optimization problem using GA
These questions check understanding of ANN behaviour, fuzzy operations, unsupervised learning, GA-based search, and problem-solving techniques.
SECTION C – Long Answer Questions (50 Marks)
Section C contains five groups (Q3 to Q7), each with two choices. Students must attempt one question per group.
Q3 – Neural Networks & Machine Learning (10 Marks)
Why neural networks are called parallel distributed processing
OR applications of supervised machine learning in businesses
Q4 – Multilayer Networks & Fuzzy Algebra (10 Marks)
Multilayer feed-forward architecture with diagram
OR algebraic sum of two fuzzy sets
Q5 – ANN vs ANFIS / Neuro-Fuzzy Regression (10 Marks)
Difference between ANN & ANFIS
OR neuro-fuzzy modeling for regression test case prioritization
Q6 – Mutation & Evolution (10 Marks)
Meaning of “survival of the fittest” with example
OR types of mutation techniques in soft computing
Q7 – Fuzzy Control & MATLAB Simulation (10 Marks)
Using fuzzy logic control + GA for structural optimization
OR how to calculate MATLAB simulation computational time
This section assesses deep reasoning, technical understanding, system design, and hybrid soft-computing techniques.
OVERALL SUMMARY
The Introduction to Soft Computing (KOE036) paper evaluates a student's understanding of:
Neural networks (structure, learning, ANN vs ANFIS)
Fuzzy logic (sets, operations, control systems)
Genetic algorithms (fitness, mutation, optimization, TSP)
Neuro-fuzzy hybrid systems
Decision-making & machine learning concepts
Soft computing applications in business, engineering, and optimization
The paper combines theory, diagrams, derivations, computational questions, and real-world applications, ensuring a complete assessment of soft computing fundamentals.
