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Evolutionary neuro-fuzzy control

Evolutionary neuro-fuzzy control

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This chapter focuses on the use of genetic algorithms (GAs) in the design of FLC. An approach of adopting genetic algorithm search is adopted to determine optimal FLC scaling factors. The approach is then extended by adoption of neural network learning of the scaling factors leading to a neuro-fuzzy control method. This is further combined with genetic algorithm for optimisation of shape of activation function of the neural network. Case study experimental investigation exercises are presented demonstrating the performances of the developed paradigms in the control of a single-link flexible manipulator system.

Chapter Contents:

  • 14.1 Introduction
  • 14.2 Evolutionary fuzzy control
  • 14.2.1 PD-PI-like fuzzy control
  • 14.3 GA-fuzzy control
  • 14.3.1 Chromosome representation
  • 14.3.1.1 Chromosome representation for membership functions
  • 14.3.1.2 Chromosome representation of rule-base
  • 14.3.1.3 Encoding scheme
  • 14.3.2 Objective function
  • 14.3.3 Evaluation
  • 14.3.4 Initialisation
  • 14.3.5 Crossover
  • 14.3.6 Mutation
  • 14.3.7 Selection
  • 14.3.8 Case study 14.1: GA-fuzzy control scheme
  • 14.4 Neuro-fuzzy control
  • 14.4.1 Parameters of PD-PI fuzzy controller
  • 14.4.2 Tuning of membership functions
  • 14.4.3 Reducing the number of scaling parameters
  • 14.4.4 Neural network tuning of the scaling factors
  • 14.4.5 Backpropagation learning of neural network
  • 14.4.6 Learning with non-linear activation function
  • 14.4.7 Case study 14.2: neuro-fuzzy control scheme
  • 14.5 GA-based neuro-fuzzy control
  • 14.5.1 Integration of fuzzy logic, neural networks and genetic algorithms
  • 14.5.2 Sigmoid function shape learning
  • 14.5.3 Neural network learning using genetic algorithms
  • 14.5.4 Case study 14.3: GA-neuro-fuzzy control scheme
  • 14.6 Summary

Inspec keywords: control system synthesis; search problems; genetic algorithms; learning systems; neurocontrollers; flexible manipulators; fuzzy control

Other keywords: evolutionary neurofuzzy control; GA; optimal scaling factors; FLC design; neural network learning; fuzzy logic controllers; single-link flexible manipulator system; shape; activation function; genetic algorithm search; optimisation

Subjects: Self-adjusting control systems; Neurocontrol; Manipulators; Fuzzy control; Optimisation techniques; Control system analysis and synthesis methods

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