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soft-computing

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  • 1.  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
  • 2.  Expert systems Artificial neural networks Genetic Algorithm Fuzzy systems Swarm intelligence Ant Colony optimization Tabu Search method
  • 3. To increase man’s understanding, reasoning, learning and perception for building new developmental tools.
  • 4. Knowledge based program that provides expert quality solutions to problems in a specific domain
  • 5. user Knowledge Expansion User interface facilityupdate facility Knowledge Inference base engine
  • 6. Expertise Exhibit expert performance Have high level of skill Have adequate robustnessSymbolic reasoning Represent knowledge symbolically Reformulate symbolic knowledgeDepth handle difficult problem domains use complex rulesSelf knowledge Examine its own operation
  • 7. Search mechanism based on the Darwinian principle of natural evolution
  • 8.  Chromosome Fitness function Initial population GA operators Reproduction Cross over Mutation GA control parameters
  • 9.  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
  • 10. Information processing systems which are constructed and implemented to model the human brain
  • 11. To develop a computational device for modeling the brain toperform various computational tasks at a faster rate than the traditional systems
  • 12.  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
  • 13.  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
  • 14.  Adaptive learning Self-organization Real-time operation Fault tolerance via redundant information coding
  • 15. Technique to deal with imprecision and information granularity
  • 16.  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
  • 17.  Individual decision making Multiperson decision making Multiobjective decision making Multiattribute decision making Fuzzy Bayesian decision making
  • 18. 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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