83 results about "Akaike information criterion" patented technology

The invention discloses a high-order multi-stage auto-regressive distributed lag modeling method of thermal error compensation of a numerical control machine. The method is characterized by comprising the following steps of: providing a high-order multi-stage auto-regressive distributed lag modeling formula of a thermal error of a numerical control machine containing a coefficient to be solved, selecting numerical control machine thermal error lag phases and numerical control machine temperature lag phases as 1, 2, 3 and 4 respectively, and substituting experimental data to fit the coefficient to be solved in the formula according to a least square method so as to determine high-order multi-stage auto-regressive distributed lag models of thermal error compensation of the numerical control machine in different lag phases; substituting the experimental data into each model to obtain a residual sum of squares of each model; and substituting the residual sum of squares of each model into an akaike information criterion to determine an optimal lag phase to determine a high-order multi-stage auto-regressive distributed lag model of a thermal error of the numerical control machine. The invention discloses a modeling method of thermal error compensation of the numerical control machine, which has the advantages of convenience in application, easiness in modeling and high stability, and has higher accuracy than a traditional ADL (automatic data logger) model.

The invention provides a method for estimating weak signal separation on the basis of positioning for external radiation sources. The method includes steps of performing direct-wave and multi-path cancellation for a sample sequence x (t) of a main channel by means of NLMS (normalized least mean square) to obtain output signals y<NLMS>(t); estimating the number of source signals of the output signals y<NLMS>(t) by an AIC (Akaike information criterion) or an MDL (minimum description length) criterion, preliminarily whitening the output signals y<NLMS>(t) to obtain whitened signals z(t), and updating a separation matrix W by an ICA (independent component analysis) algorithm to obtain M channels of estimated signals y(t); respectively performing time-frequency two-dimensional correlation for the M channels of estimated signals y<1>(t), ..., y<M>(t) and reference signals s<ref>(t); and searching a peak point in absolute correlation values phi(tau, f) corresponding to the M channels of estimated signals, and determining time delay and Doppler frequency shift which correspond to the peak point as time delay and Doppler frequency shift of a target when a signal-to-noise ratio of a certain channel of signals is larger than or equal to a threshold value.