Systems, Man, and Cybernetics

 

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• [P29] Reduced Order Modeling Using Genetic-Fuzzy algorithm
  Author(s): Ahmad Taha Abdulsadda, Kamran Iqbal
  
  

  Abstract
  Many high-order systems have a large state space. Such systems additional computation time for complex calculation to find the output response. Traditionally, into iteration methods has been applied to solve this problem. In this paper advantages of stability equation method and the error minimization by genetic-fuzzy algorithm have been combined to propose a new method for order reduction of linear dynamic systems. Genetic has been used to find the optimal solution(s) to minimize the objective function "J" that depends on the error term between the actual outputs and desired or reduced output. Fuzzy sets have been used to determine the step size action (point crossover or multiple crossover) depending upon fuzzy rules based on the current and previous error terms. An example of reduced order modeling from power systems is presented to illustrate the algorithm.

  
   

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