Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Small hydro power plant identification using NNARX structure
Neural Computing and Applications
An Investigation on Pruned NNARX Identification Model of Hydropower Plant
Engineering with Computers
Nonlinear predictive control for a NNARX hydro plant model
Neural Computing and Applications
Experiences with fuzzy logic and neural networks in a controlcourse
IEEE Transactions on Education
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
Unsupervised adaptive neural-fuzzy inference system for solving differential equations
Applied Soft Computing
Prediction of liquefaction potential based on CPT up-sampling
Computers & Geosciences
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In this paper, the hydro power plant model (with penstock-wall elasticity and compressible water column effect) is simulated at random load disturbance variation with output as turbine speed for random gate position as input. The multilayer perceptron neural network (i.e. NNARX) and fused neural network and fuzzy inference system (i.e. ANFIS) for identification of turbine speed as output variable are reported. Emphasis is put on obtaining a generalized model, using (i) NNARX model and (ii) ANFIS model with membership functions defined by subtractive clustering for plant model representation under different values of water time constant. The comparative performance study between the two approaches is also addressed. In the end of the paper, an application of adaptive noise cancellation based on ANFIS model to identify the turbine speed dynamics is also discussed.