1_BASICS OF SOFT COMPUTATION OF KNOWLEDGE
- 1. BASICS OF SOFT COMPUTATION
- 2. Course content • Introduction to soft computing • Artificial neural networks (ANN), Supervised and unsupervised learning of ANN • Evolutionary algorithms • Fuzzy logic and fuzzy inference systems • Hybrid Systems
- 3. Introduction of Soft Computing • Concept of Computing • Characteristics of computing • Hard Computing • Hard Computing Vs Soft computing • Soft Computing • Basic Tools of Soft Computing • Characteristics of Soft Computing • Differences between Hard Computing and Soft Computing
- 4. Artificial neural networks (ANN), Supervised and unsupervised learning of ANN • Biological Neuron: Two types of neurons; Basic functions of a neuron; various parts of a neuron; • Analogy between Artificial Neuron and Biological Neuron • Artificial Neuron model • Activation function / Transfer functions • Advantages of ANN • Fundamental classes of ANN architectures and modelling • Training of Artificial Neural Networks; Types of Learning
- 5. Evolutionary algorithms • Optimization • Classical optimization methods • Difficulties of classical optimization methods • Different Evolutionary Algorithms • Genetic Algorithm
- 6. Fuzzy logic and fuzzy inference systems • Crisp set and Fuzzy Set • Membership Function (Fuzzy set) • Some Key properties of fuzzy sets/ MFDs • Fuzzy vs. Probability and Prediction vs. Forecasting • Types of Fuzzy Membership Function • Fuzzy Set Operators: Fuzzy intersection; Fuzzy union; Fuzzy complement Product; Sum and Differences; Equality; Power of a fuzzy set • Properties of Fuzzy Set • Fuzzy Relation • Fuzzy implication methods • Fuzzy aggregation • Fuzzy Logic Rule • Fuzzy Inferences and Fuzzy Inference System • Fuzzy model
- 7. Hybrid Systems • Advantage and disadvantage of hybrid systems in Soft Computing • Genetic-Fuzzy system