Non-linear Volterra Model Identification of Industrial Glue-application System

2007 ◽  
Author(s):  
Pavol Mikulik ◽  
Linus Michaeli ◽  
Jan Saliga
Keyword(s):  
Model Identification ◽  
Volterra Model ◽  
Application System ◽  
Non Linear

Rotorcraft control response using linearised and non-linear flight dynamic models with different inflow models

2017 ◽  
Vol 121 (1238) ◽  
pp. 553-575 ◽  
Author(s):  
T. Sakthivel ◽  
C. Venkatesan
Keyword(s):  
Dynamic Model ◽  
Dynamic Models ◽  
Model Identification ◽  
Flight Test ◽  
Step Size ◽  
Response Characteristics ◽  
Wide Range ◽  
Non Linear ◽  
Steady Level ◽  
Control Response

ABSTRACTThe aim of the present study is to develop a relatively simple flight dynamic model which should have the ability to analyse trim, stability and response characteristics of a rotorcraft under various manoeuvring conditions. This study further addresses the influence of numerical aspects of perturbation step size in linearised model identification and integration timestep on non-linear model response. In addition, the effects of inflow models on the non-linear response are analysed. A new updated Drees inflow model is proposed in this study and the applicability of this model in rotorcraft flight dynamics is studied. It is noted that the updated Drees inflow model predicts the control response characteristics fairly close to control response characteristics obtained using dynamic inflow for a wide range of flight conditions such as hover, forward flight and recovery from steady level turn. A comparison is shown between flight test data, the control response obtained from the simple flight dynamic model, and the response obtained using a more detailed aeroelastic and flight dynamic model.

scholarly journals Wavelets-based non-linear model for real-time daily flow forecasting in Krishna River

2013 ◽  
Vol 15 (3) ◽  
pp. 1022-1041 ◽  
Author(s):  
R. Maheswaran ◽  
Rakesh Khosa
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Model ◽  
Volterra Model ◽  
Lead Times ◽  
Discrete Wavelet ◽  
Promising Alternative ◽  
Daily Streamflow ◽  
Non Linear ◽  
Artificial Neural

In this study, a multi-scale non-linear model based on coupling a discrete wavelet transform (DWT) and the second-order Volterra model, i.e. the wavelet Volterra coupled (WVC) model, is applied for daily inflow forecasting at Krishna Agraharam, Krishna River, India. The relative performance of the WVC model was compared with regular artificial neural networks (ANN), wavelet-artificial neural networks (WA-ANN) models and other baseline models such as auto-regressive moving average with exogenous variables (ARMAX) for lead times of 1–5 days. The models were applied for the forecasting of daily streamflow at Krishna Agraharam Station at Krishna River. The WVC performed very well, especially when compared with the WA-ANN model for lead times of 4 and 5 days. The results indicate that the WVC model is a promising alternative to the other traditional models for short-term flow forecasting.

A non-linear and non-stationary perspective for downscaling mean monthly temperature: a wavelet coupled second order Volterra model

2017 ◽  
Vol 31 (9) ◽  
pp. 2159-2181 ◽  
Author(s):  
Anchit Lakhanpal ◽  
Vinit Sehgal ◽  
R. Maheswaran ◽  
R. Khosa ◽  
Venkataramana Sridhar
Keyword(s):  
Second Order ◽  
Volterra Model ◽  
Monthly Temperature ◽  
Non Linear
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