📦 Soft Computing Lab Practicals – CSP 3035 Spring 2025 (Complete Resource Pack)
This release delivers the complete, ready-to-use package of all programs, resources, and reference materials for the Soft Computing Lab (CSP 3035) conducted in Spring 2025, taught to the B.Tech 6th Semester Students of School of Computer Science and Engineering in Shri Mata Vaishno Devi University (SMVDU),Katra, India
It is designed for students, educators, and researchers who want a reproducible, well-documented, and accessible set of lab materials.
📂 Contents
Neural Network Implementations (C Language)
- Perceptron Network for AND Gate (Bipolar inputs & targets)
- Adaline Network for OR Gate (Bipolar inputs & targets)
- Madaline Network for XOR Gate (Bipolar inputs & targets)
- Madaline Network for XOR Gate (Bipolar inputs & binary targets)
- Back-Propagation Network for XOR Gate (Bipolar inputs & targets)
Fuzzy & Classical Set Operations
- Primitive operations on classical sets
- Laws associated with classical sets
- Primitive operations on fuzzy sets (dynamic components)
- Laws associated with fuzzy sets
- Cartesian product of fuzzy sets
- Max–Min composition of matrices from Cartesian product
- Max–Product composition of matrices from Cartesian product
- Combined program for Cartesian product + Max–Min + Max–Product compositions
Evolutionary Computing
- Genetic Algorithm to maximize ( F(X) = X^2 ) for ( 0 < X < 31 )
Extras
- Activation function visualizer (Step, Signum, Binary/Bipolar Sigmoid, ReLU) with plotting instructions
- Answer keys for lab exams/quizzes (Groups AX, AY, BX, BY)
- Principles of Soft Computing reference book covering:
- Fuzzy Logic & Systems
- Neural Networks
- Genetic Algorithms
- Hybrid Systems
- Applications of Soft Computing
Environment Setup Guides
- GCC installation for Windows (x86_64 & ARM), Linux, macOS, and WSL2
- Step-by-step PATH configuration
- Verification commands
🎯 Key Features
- Complete & Self-Contained – All code, resources, and references in one ZIP.
- Cross-Platform Ready – Works on Windows, Linux, macOS, and WSL2.
- Educational & Practical – Covers theory, implementation, and exam preparation.
- Reproducible & Accessible – Clear instructions for setup and execution.
- Open for Contributions – Licensed under MIT for academic and personal use.
📜 License
This project is licensed under the MIT License – free to use, modify, and distribute with attribution.