1. SOFT COMPUTING

2. DEFINITION: • A field of study that encompasses computational techniques for performing tasks that require intelligence when performed by humans. • Simulation of human behavior and cognitive processes on a computer. OTHER NAMES: • Intelligent control • Artificial Intelligence

3. Latest intelligent systems: • Expert systems • Artificial neural networks • Genetic Algorithm • Fuzzy systems • Swarm intelligence • Ant Colony optimization • Tabu Search method

4. Purpose of AI: To increase man’s understanding, reasoning, learning and perception for building new developmental tools.

5. Expert systems: Knowledge based program that provides expert quality solutions to problems in a specific domain

6. Architecture of an expert system user Expansion facility Knowledge update facility User interface Knowledge base Inference engine

7. Characteristics of expert systems: Expertise Exhibit expert performance Have high level of skill Have adequate robustness Symbolic reasoning Represent knowledge symbolically Reformulate symbolic knowledge Depth handle difficult problem domains use complex rules Self knowledge Examine its own operation

8. GENETIC ALGORITHM Search mechanism based on the Darwinian principle of natural evolution

9. Components of GA • Chromosome • Fitness function • Initial population • GA operators Reproduction Cross over Mutation • GA control parameters

10. Characteristics of GA • Multi point search – reducing the probability of getting stuck in the local optima • Stochastic operators with guided search instead of deterministic rules • Objective function need not be differentiable • Implementation simpler – only information needed is objective function • Can solve non-linear , discontinuous optimal problems • perform well in noisy functions

11. ARTIFICIAL NEURAL NETWORKS Information processing systems which are constructed and implemented to model the human brain

12. OBJECTIVE OF ANN To develop a computational device for modeling the brain to perform various computational tasks at a faster rate than the traditional systems

13. Basic entities of ANN: • the model’s synaptic interconnections • the training or learning rules adopted for updating and adjusting weights • their activation functions THE MAIN PROPERTY OF ANN IS ITS CAPABILITY TO LEARN

14. Kinds of learning in ANN: • Supervised learning: The learning is performed with the help of teacher. The correct target output values are known for each input pattern • Unsupervised learning: self organizing in which exact clusters are formed by discovering similarities and dissimilarities among the objects • Reinforcement learning: learning with a critic as opposed to learning with a teacher

15. Advantages of ANN: • Adaptive learning • Self-organization • Real-time operation • Fault tolerance via redundant information coding

16. FUZZY SYSTEMS Technique to deal with imprecision and information granularity

17. Fuzzification: • Process of transforming a crisp set to a fuzzy set (fuzzy quantities) Defuzzification: • mathematically termed as “rounding it off” • Mapping process from a space of fuzzy control actions defines over an output universe of discourse into a space of crisp control actions

18. Kinds of fuzzy decision making: • Individual decision making • Multiperson decision making • Multiobjective decision making • Multiattribute decision making • Fuzzy Bayesian decision making

19. Applications of Fuzzy systems: Widely used in non-linear, time varying, ill-defined systems, complex systems like • traffic control • Steam engine • Aircraft flight control • Missile control • Adaptive control • Fault detection control unit • Power systems control

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