ANFIS and neural network techniques for non-parametric modelling of a twin rotor system
Toha, SF and Tokhi, MO (2010). ANFIS and neural network techniques for non-parametric modelling of a twin rotor system. Systems Science. 36 (4), pp. 13-31.
|Authors||Toha, SF and Tokhi, MO|
Interest in system identification especially for nonlinear systems has significantly increased in the past few decades. Soft-computing methods which concern computation in an imprecise environment have gained significant attention amid widening studies of explicit mathematical modelling. In this research, three different soft computing techniques that are Multi-layered perceptron neural network using Levenberg-Marquardt (LM), Elman recurrent neural network and adaptive neuro-fuzzy inference system (ANFIS) network are deployed and used for modelling a twin rotor multi-input multi-output system (TRMS). The system is perceived as a challenging engineering problem due to its high nonlinearity, cross coupling between horizontal and vertical axes and inaccessibility of some of its states and outputs for measurements. Accurate modelling of the system is thus required so as to achieve satisfactory control objectives. It is demonstrated experimentally that soft computing methods can be effectively used for modelling the system with highly accurate results. The accuracy of the modelling results are demonstrated through validation tests including training and test validation and correlation tests.
|Keywords||Multi-layered perceptron neural network; Levenberg-Marquardt (LM); Elman recurrent neural network; Adaptive neuro-fuzzy inference system; Soft-computing; Twin rotor multi-input multi-output system|
|Journal citation||36 (4), pp. 13-31|
|Publication process dates|
|Accepted||01 Jan 2010|
|Deposited||29 Apr 2020|
|Accepted author manuscript|
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