Fast Algorithm For Identification Research Articles (Page 1)

Research on rapid extraction of internal resistance of lithium battery based on short-time transient response

Eulerian fast motion identification algorithm for deformation measurement of cable-stayed bridge

A fast and reliable nuclide identification algorithm is important to the detection of radioactive materials, and the emergence of a sequential Bayesian algorithm provides a new direction of research on radioactive detection. This article proposes a Bayesian nuclide identification algorithm based on the energy spectrum and deploys the algorithm on a microprocessor for real-time online processing. The algorithm builds a probability model based on the natural background radiation and radioactive source radiation and a decision function based on sequence testing. The processor conducts a posteriori estimation on the collected energy spectrum at fixed time intervals and updates the decision function. A laboratory self-developed CeBr3 detector was used in testing. Four radioactive sources, namely 241Am, 133Ba, 137Cs, and 60Co, were used in experiments with fixed and moving radioactive sources. The experimental results show that the proposed algorithm can rapidly identify mixed and mobile radioactive sources.

MIMO system identification is a fundamental concern in a variety of applications. Various iterative and recursive MIMO system identification algorithms exist in the literature. The iterative algorithms suffer from high computational cost due to large matrix computations and recursive algorithms suffer from slow convergence speed. This paper proposes a fast recursive exact least squares algorithm for MIMO system identification with fast convergence speed and low computational cost. The recursions of the algorithm give rise to a lattice structure. The lattice structure is less sensitive to round-off errors, coefficients variations and can also be used for model order reduction. Although in this work we estimate an FIR (finite impulse response) model of the MIMO system, the framework can also be used for IIR (infinite impulse response) models, and for estimating linear periodically time varying (LPTV) and multivariable systems. The theory proposed is validated using simulation results.

基于多特征匹配的快速星图识别

With the continuous development of optical fiber sensing technology, the Optical Fiber Pre-Warning System (OFPS) has been widely used in various fields. The OFPS identifies the type of intrusion based on the detected vibration signal to monitor the surrounding environment. Aiming at the real-time requirements of OFPS, this paper presents a fast algorithm to accelerate the detection and recognition processing of optical fiber intrusion signals. The algorithm is implemented in an embedded system that is composed of a digital signal processor (DSP). The processing flow is divided into two parts. First, the dislocation processing method is adopted for the sum processing of original signals, which effectively improves the real-time performance. The filtered signals are divided into two parts and are parallel processed by two DSP boards to save time. Then, the data is input into the identification module for feature extraction and classification. Experiments show that the algorithm can effectively detect and identify the optical fiber intrusion signals. At the same time, it accelerates the processing speed and meets the real-time requirements of OFPS for detection and identification.

Recently, the amount of data being stored has increased dramatically, and the amount of data backed up on servers increases yearly. However, the share of duplicate data in that backup data is also increasing, and because of this, the time spent on duplicate data processing is greatly increasing. Therefore, in this paper, we design a Bloom filter-based data deduplication algorithm for fast identification and removal of duplicate data. The results from evaluation of the implemented algorithm show that execution time is 17% less than with an existing deduplication algorithm.

Fast ℓ1-recursive total least squares algorithm for sparse system identification

Ellipticity pivot star method for autonomous star identification

Cooperation coevolution with fast interdependency identification for large scale optimization

An algorithm for fast extraction and identification of star target

The key of fast identification algorithm of time-varying modal parameter based on subspace tracking is to find efficient and fast subspace-tracking algorithm.This paper presents a new version of NIC(Novel Information Criterion) using two-layer linear neural network learning for subspace tracking.Comparing with the original algorithm, there is no need to set a key control parameter in advance.Simulation experiments show that new algorithm has a faster convergence in the initial period.

A Fast Approximation Algorithm for Set-Membership System Identification

A derivation of fundamental fast transversal filter equations comes to a 7M fast RLS algorithm for system identification. Yet, this algorithm is not stable due to numerical errors resulting from implementation on a hardware with finite precision arithmetic. The known stabilization methods with feedback of numerical errors require a judicious choice of the feedback gains and depend on the properties of the exciting sequence, as simulations have shown. A new method to stabilize the algorithm is introduced that does not depend on the characteristics of the excitation signal. By inserting leakage factors in the filter equations, a steady contraction of error components is reached. >

A very flexible Levinson-type recursion for a class of non-Toeplitz systems of linear equations is demonstrated. A complete solution is expressed as a linear combination of a partial solution and three auxiliary solutions. The class of systems possesses a special structure in that the coefficient matrices can be partitioned into four block Toeplitz submatrices. The number of multiplications and additions required to compute an n-dimensional solution is O(n/sup 2/). The recursion is then applied to multichannel IIR filtering. Specifically, a lattice structure is established for linear minimum mean square error predictors having independently and arbitrarily specified numbers of poles and zeros. Next the recursion is used to develop a fast time and order recursive least-squares algorithm for ARX system identification. The novelty of the algorithm is that it can be used to efficiently determine parameter estimates of a family of ARX models. >