3 resultados para Grunwald-Letnikov fractional derivative

em WestminsterResearch - UK


Relevância:

20.00% 20.00%

Publicador:

Resumo:

This paper applies Gaussian estimation methods to continuous time models for modelling overseas visitors into the UK. The use of continuous time modelling is widely used in economics and finance but not in tourism forecasting. Using monthly data for 1986–2010, various continuous time models are estimated and compared to autoregressive integrated moving average (ARIMA) and autoregressive fractionally integrated moving average (ARFIMA) models. Dynamic forecasts are obtained over different periods. The empirical results show that the ARIMA model performs very well, but that the constant elasticity of variance (CEV) continuous time model has the lowest root mean squared error (RMSE) over a short period.

Relevância:

20.00% 20.00%

Publicador:

Resumo:

This paper presents a novel technique for the design of narrow-band sigma-delta modulators with an embedded tunable centre frequency mechanism. This method demonstrates that the use of sum filters combined with a fractional delayer provide the flexibility of tuning the noise shaping band for any desired variable centre frequency input signal.

Relevância:

20.00% 20.00%

Publicador:

Resumo:

This paper presents the design analysis of novel tunable narrow-band bandpass sigma-delta modulators, that can achieve concurrent multiple noise-shaping for multi-tone input signals. This approach utilises conventional comb filters in conjunction with FIR, or allpass IIR fractional delay filters, to deliver the desired nulls for the quantisation noise transfer function. Detailed simulation results show that FIR fractional delay comb filter based sigma-delta modulators tune accurately to most centre frequencies, but suffer from degraded resolution at frequencies close to Nyquist. However, superior accuracies are obtained from their allpass IIR fractional delay counterpart at the expense of a slight shift in noise-shaping bands at very high frequencies.