Extreme learning ANFIS for control applications
- Resource Type
- Conference
- Authors
- Pillai, G. N.; Pushpak, Jagtap; Nisha, M. Germin
- Source
- 2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA) Computational Intelligence in Control and Automation (CICA), 2014 IEEE Symposium on. :1-8 Dec, 2014
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Training
Mathematical model
Equations
Neural networks
Prediction algorithms
Training data
Algorithm design and analysis
extreme learning machines
fuzzy neural systems
nonlinear model predictive control
inverse control
- Language
- ISSN
- 2328-1448
2328-1464
This paper proposes a new neuro-fuzzy learning machine called extreme learning adaptive neuro-fuzzy inference system (ELANFIS) which can be applied to control of nonlinear systems. The new learning machine combines the learning capabilities of neural networks and the explicit knowledge of the fuzzy systems as in the case of conventional adaptive neuro-fuzzy inference system (ANFIS). The parameters of the fuzzy layer of ELANFIS are not tuned to achieve faster learning speed without sacrificing the generalization capability. The proposed learning machine is used for inverse control and model predictive control of nonlinear systems. Simulation results show improved performance with very less computation time which is much essential for real time control.