Intelligent Control Systems and Optimization

Genetic Algorithms are heuristic search approaches that are applicable to a wide range of optimization problems. This flexibility makes them attractive for many optimization problems in practice. Evolution is the basis of Genetic Algorithms. It follows 3 rules and they are Selection rule, Cross over rule and Mutation Rule. Genetic operators change the solutions. Crossover operators combine the genomes of two or more solutions. Mutation adds randomness to solutions and should be scalable, drift-less, and reach each location in solution space. Genetic Algorithms are search based algorithms based on the concepts of natural selection and genetics. Genetic Algorithms are a subset of a much larger branch of computation known as Evolutionary Computation. Genetic algorithms optimize a given function by means of a random search. They are best suited for optimization and tuning problems in the cases where no prior information is available. As an optimization method genetic algorithm are much more effective than a random search. Genetic Algorithms are adaptive heuristic search algorithms that belong to the larger part of evolutionary algorithms. Genetic Algorithms have demonstrated to be effective procedures for solving multicriterial optimization problems. It is a very popular meta-heuristic technique for solving optimization problems. These algorithms mimic models of natural evolution and can adaptively search large spaces in near-optimal ways. They are commonly used to generate high-quality solutions for optimisation problems and search problems.

Neural network tries an attempt to simulate human brain. The simulating is based on the present knowledge of brain function, and this knowledge is even at its best primitive. So, it is not wrong to claim that artificial neural networks probably have no close relationship to operation of human brains. The operation of brain is believed to be based on simple basic elements called neurons which are connected to each other with transmission lines called axons and receptive lines called dendrites. The learning may be based on two mechanisms: the creation of new connections, and the modification of connections. Each neuron has an activation level which, in contrast to Boolean logic, ranges between some minimum and maximum value. In artificial neural networks the inputs of the neuron are combined in a linear way with different weights. The result of this combination is then fed into a non-linear activation unit, which can in its simplest form be a threshold unit.

  • Genetic Algorithms
  • Fuzzy Control
  • Decision Support Systems
  • Machine Learning in Control Applications
  • Knowledge-Based Systems Applications
  • Hybrid Learning Systems
  • Distributed Control Systems
  • Evolutionary Computation and Control
  • Optimization Algorithms
  • Soft Computing
  • Software Agents for Intelligent Control Systems
  • Neural Networks based Control Systems
  • Planning and Scheduling
  • Intelligent Fault Detection and Diagnosis
  • Engineering Applications

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