Soft Computing

Soft computing is the use of approximate calculations to provide imprecise but usable solutions to complex computational problems. The approach enables solutions for problems that may be either unsolvable or just too time-consuming to solve with current hardware. Soft computing is sometimes referred to as computational intelligence.

Soft computing provides an approach to problem-solving using means other than computers. With the human mind as a role model, soft computing is tolerant of partial truths, uncertainty, imprecision and approximation, unlike traditional computing models. The tolerance of soft computing allows researchers to approach some problems that traditional computing can't process.

Soft Computing became a formal area of study in Computer Science in the early 1990s. Earlier computational approaches could model and precisely analyze only relatively simple systems. More complex systems arising in biology, medicine, the humanities, management sciences, and similar fields often remained intractable to conventional mathematical and analytical methods. However, it should be pointed out that complexity of systems is relative and that many conventional mathematical models have been very productive in spite of their complexity.

Soft computing uses component fields of study in:

- Fuzzy logic
- Machine learning
- Probabilistic reasoning
- Evolutionary computation
- Perceptron
- Genetic algorithms
- Differential algorithms
- Support vector machines
- Metaheuristics
- Swarm intelligence
- Ant colony optimization
- Particle optimization
- Bayesian networks
- Artificial neural networks
- Expert systems

Course Detail

Course Detail

- Syllabus
- Reference Books
- Practical List
- Additional Experiments
- Assignment - I
- Learning Resources
- Journals

Video Lectures

- Introduction to Crisp Sets
- Introudction to Fuzzy Sets
- Fuzzy set Examples
- Fuzzy Terminologies
- Fuzzy Membership Functions
- Linguistic Variables and Hedges
- Proeprties of Crisp Sets
- Properties of Fuzzy Sets
- Fuzzy Operations - I
- Fuzzy Operations - II
- Distance and Similarity Measures
- Crisp Relations
- Fuzzy Relations
- Properties of Relations
- Classical and Fuzzy Logic - I
- Classical and Fuzzy Logic - II
- Defuzzification Methods - I
- Defuzzification Methods - II
- Examples on Defuzzification Methods
- Introduction to Fuzzy Inference System
- Mamdani Approach
- Takagi-Sugeno Approach
- Tsukamoto Model
- Fuzzy Control System

Syllabus for Internal Examination

**Introduction to Soft Computing and Neural Networks:**Evolution of Computing: Soft Computing Constituents, From Conventional AI to Computational Intelligence: Machine Learning Basics**Fuzzy Logic:**Fuzzy Sets, Operations on Fuzzy Sets, Fuzzy Relations, Membership Functions: Fuzzy Rules and Fuzzy Reasoning, Fuzzy Inference Systems, Fuzzy Expert Systems, Fuzzy Decision Making.**Genetic Algorithms:**Goals of optimization, Comparison with traditional methods, schemata, Terminology in GA - String, Structure, Parameter string, data structures, operators, coding, fitness function, algorithm, Applications of GA in Machine Learning: Machine Learning Approach to Knowledge Acquisition.

GTU Question papers

2019.05.27 | 2018.11.29 | 2018.05.05 | 2017.11.15 | 2017.05.06 | 2016.05.18 |

2015.12.16 | 2015.05.04 | 2014.11.27 | 2014.05.29 | 2013.11.28 | 2012.12.27 |

2012.05.28 | 2011.11.26 |

GTU Question papers (Old Courses)

SCC_2019.05.09 | SCC_2018.11.15 | SCC_2018.04.30 | SCC_2017.11.10 |

SCC_2017.05.04 | SCA_2019.11.30 | SCA_2018.12.03 | SCA_2017.11.18 |

Course Detail

Syllabus for Internal Examination

**Introduction to Soft Computing and Neural Networks:**Evolution of Computing: Soft Computing Constituents, From Conventional AI to Computational Intelligence: Machine Learning Basics**Fuzzy Logic:**Fuzzy Sets, Operations on Fuzzy Sets, Fuzzy Relations, Membership Functions: Fuzzy Rules and Fuzzy Reasoning, Fuzzy Inference Systems, Fuzzy Expert Systems, Fuzzy Decision Making.**Neural Networks:**Machine Learning Using Neural Network, Adaptive Networks, Feed forward Networks, Supervised Learning Neural Networks, Radial Basis Function Networks : Reinforcement Learning, Unsupervised Learning Neural Networks, Adaptive Resonance architectures, Advances in Neural networks

Course Detail

- Syllabus
- Referene Books
**Neuro-Fuzzy and Soft Computing**,*Jyh-Shing Roger Jang, Chuen-Tsai Sun, EijiMizutani,*Prentice-Hall of India, 2003**Fuzzy Sets and Fuzzy Logic - Theory and Applications**,*George J. Klir and Bo Yuan*, Prentice Hall, 1995.

Syllabus for Internal Examination

**Introduction to Soft Computing and Neural Networks:**Evolution of Computing: Soft Computing Constituents, From Conventional AI to Computational Intelligence: Machine Learning Basics**Fuzzy Logic:**Fuzzy Sets, Operations on Fuzzy Sets, Fuzzy Relations, Membership Functions: Fuzzy Rules and Fuzzy Reasoning, Fuzzy Inference Systems, Fuzzy Expert Systems, Fuzzy Decision Making.**Neural Networks:**Machine Learning Using Neural Network, Adaptive Networks, Feed forward Networks, Supervised Learning Neural Networks, Radial Basis Function Networks : Reinforcement Learning, Unsupervised Learning Neural Networks, Adaptive Resonance architectures, Advances in Neural networks

Video Tutorials

- Neural Networks for Machine Learning by Geoffrey Hinton
- Artificial Neural Networks by IIT - Kharagpur
- Beginner Introduction to Neural Networks
- Fuzzy Logic & Set Theory by NIT, Rourkela (Subhro Mukherjee)
- Fuzzy Logic Tutorials
- Soft Computing and Optimization Algorithms
- Soft Computing Series(Muo Sigma Classes)
- Soft Computing (Sanjay Pathak)
- Soft Computing - Neural Networks
- Soft Computing (Red Apple Tutorial)
- Applications of Soft Computing (AKTU TechQuantum)

Web Resources