Manipulator neural network control based on fuzzy genetic algorithm

P. Cui*, G. Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The three-layer forward neural networks are used to establish the inverse kinematics models of robot manipulators. The fuzzy genetic algorithm based on the linear scaling of the fitness value is presented to update the weights of neural networks. To increase the search speed of the algorithm, the crossover probability and the mutation probability are adjusted through fuzzy control and the fitness is modified by the linear scaling method in FGA. Simulations show that the proposed method improves considerably the precision of the inverse kinematics solutions for robot manipulators and guarantees a rapid global convergence and overcomes the drawbacks of SGA and the BP algorithm.

Original languageEnglish
Pages (from-to)63-66
Number of pages4
JournalHigh Technology Letters
Volume7
Issue number1
Publication statusPublished - Mar 2001
Externally publishedYes

Keywords

  • Fitness function
  • Fuzzy control
  • Genetic algorithm
  • Inverse kinematics
  • Neural networks

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