Automatic Knowledege Acquisition for Multivariable Fuzzy Control Using Neural Network Approach
- Resource Type
- Conference
- Authors
- Nie, Junhong; Linkens, D. A.
- Source
- 1993 American Control Conference American Control Conference, 1993. :767-771 Jun, 1993
- Subject
- Robotics and Control Systems
Computing and Processing
Components, Circuits, Devices and Systems
Fuzzy control
Neural networks
Fuzzy reasoning
Fuzzy systems
Control systems
Pattern matching
Systems engineering and theory
Fuzzy neural networks
Computer networks
Pressure control
- Language
This paper introduce a simple and systematic scheme capable of self-organizing and self-learning the required control knowledge for use with multivariable fuzzy controllers. The starting point of the approach is to structurally map a simplified fuzzy control algorithm (SFCA) into a counterpropagation network (CPN) in such a way that the control knowledge is explicitly represented in the form of connection weights of the nets, the control rule-base is gradually self-constructed with the fulfillment of the prespecified performance requirements, and finally the approximate reasoning is carried out by replacing a winner-take-all competitive scheme with a soft matching cooperative strategy. Two problems of multivariable control of blood pressure and anaesthesia have been studied as demonstration examples.