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An Approach Based
Application of Several Time Series Models in Prediction
A Generalized Kalman Filter with Applications to Seismic Problems
4 other sections not shown
algorithm AMINZADEH applications Autocorrelation autoregressive average bandpass filter breath sounds classification codebook corresponding data compression death of tumour deconvolution distance distribution engine equation error extraction feature vector flaw signal forecasts formula frequency Gaussian grain noise group delay IEEE inequality initial codebook input Instrumental Variables IPACS Kalman filter limit cycle linear Linear-Quadratic-Gaussian Control loss function LQG problem matrix measured method multi-dimensional AR model MULTI-STAGE PLANNING non-stationary time series nondestructive testing obtained optimal solution order selection paper parameters pattern recognition pattern vector performance population predicted value prediction accuracy random variables rate of death recognition system recursive reference represents sample segment seismic series analysis series model SETAR model signal processing simulation speaker-independent spectrum step stochastic subset Table techniques tendency term Theorem training set transform true order Tsinghua University ultrasonic ultrasonic nondestructive testing vector quantization waveform wavelet Wiener filter x(fc