Low Complexity Auto-Adaptive Algorithm for Finite State Prediction Given Historical Observations

The spherical subspace tracker is a low complexity adaptive algorithm that converges to the dominant (or subdominant) subspace. Spherical subspace updating is a fast way to monitor slowly time-varying subspaces, which arise in high resolution direction of arrival tracking algorithms. In real time sonar and radar array processing applications, it becomes necessary to find parallel algorithms and pipelined architectures that provide high speed solutions to these tracking problems. In this paper the spherical subspace tracker is parallelized and mapped onto systolic array architectures. The arrays are ideally suited for implementation with custom VLSI or networks of available parallel numeric processing chips such as iWarps and TMS320C40s. C-language like cell programs specify the functionality and timing of the systolic array in concrete terms.

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Finite state residual vector quantization with neural network state prediction

10.1117/12.205246 ◽

1995 ◽

Author(s):

Syed A. Rizvi ◽

Nasser M. Nasrabadi

Keyword(s):

Neural Network ◽

Vector Quantization ◽

Residual Vector ◽

Network State ◽

State Prediction ◽

Finite State ◽

Residual Vector Quantization

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Critical-Path Analysis and Low-Complexity Implementation of the LMS Adaptive Algorithm

IEEE Transactions on Circuits and Systems I Regular Papers ◽

10.1109/tcsi.2013.2284173 ◽

2014 ◽

Vol 61 (3) ◽

pp. 778-788 ◽

Cited By ~ 42

Author(s):

Pramod Kumar Meher ◽

Sang Yoon Park

Keyword(s):

Path Analysis ◽

Adaptive Algorithm ◽

Critical Path ◽

Low Complexity ◽

Critical Path Analysis ◽

Low Complexity Implementation

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A Novel Approach to Active Noise Control using Normalized Clipped Adaptive Algorithm

International Journal of Manufacturing Materials and Mechanical Engineering ◽

10.4018/ijmmme.2013070105 ◽

2013 ◽

Vol 3 (3) ◽

pp. 76-88

Author(s):

A. Chandra Mouli ◽

Ch. Ratnam

Keyword(s):

Adaptive Algorithm ◽

Noise Control ◽

Signal To Noise Ratio ◽

Active Noise Control ◽

Low Complexity ◽

Least Mean Square ◽

Mechatronic Systems ◽

Active Noise ◽

Novel Approach ◽

Systems Simulation

In this paper, an efficient normalization based adaptive algorithm is used for active noise control in mechanical systems in order to reject extensive disturbances. The proposed implementations are suitable in applications like various motors, generators, aircrafts, battle field and elevators, etc where noise reduction is very important. In the experiments, the authors used several variants of the familiar Filtered X Least Mean Square (FXLMS) algorithm. In FXLMS the vector of past inputs is first filtered by the secondary path transfer function, hence it is named as filtered X LMS. These modified results normalized FXLMS (NFXLMS) and normalized clipped FXLMS (NCFXLMS) algorithms, leads to fast convergence, better noise rejection capability. The NCFXLMS algorithm requires only half of the multiplications requires than NFXLMS. This type of low complexity strategy is not used in active noise control application in mechatronic systems. Simulation results prove that the proposed active noise cancellers provide better performance in terms of signal to noise ratio than the conventional FXLMS.

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