• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.5
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• pp.410-416
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• 2003

In the affine projection algorithm(APA), the projection dimension depends on a number of projection basis and of elements of input vector used for updating of coefficients of the adaptive filter. The projection dimension is closely related to a convergence speed of the APA, and it determines computational complexity. As the adaptive filter approaches to steady state, convergence speed is decreased. Therefore it is possible to reduce projection dimension that determines convergence speed. In this paper, we proposed the variable dimension affine projection algorithm (VDAPA) that controls the projection dimension and uses the relation between variations of coefficients of the adaptive filter and convergence speed of the APA. The proposed method reduces computational complexity of the APA by modifying the number of projection basis on convergence state. For demonstrating the good performances of the proposed method, simulation results are compared with the APA and normalized LMS algorithm in convergence speed and computational quantity.

• The Transactions of the Korean Institute of Power Electronics
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• v.23 no.1
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• pp.66-71
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• 2018

We propose an online parameter estimation method for surface-mounted permanent-magnet synchronous motor (SPMSM) using an affine projection algorithm (APA). The proposed method estimates parameters with two APAs based on the discrete-time model equation of SPMSM during motor operation. The first APA is designed to estimate inductance, and the second APA is designed to estimate resistance and flux linkage. However, in case when the d-axis current is controlled to 0A, the second APA cannot estimate resistance and flux linkage simultaneously because the matrix rank in APA becomes deficient. To overcome this problem, we temporarily inject a negative reference current input to the d-axis control loop, and the matrix in the APA then becomes full rank, which enables the simultaneous estimation of resistance and flux linkage. The proposed method is verified by PSIM simulation and an actual experiment, and the results reveal that SPMSM parameters can be estimated online during motor operation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.1C
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• pp.82-91
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• 2005

This paper introduces an efficient affine projection algorithm(APA) using iterative hyperplane projection. Among various fast converging adaptation algorithms, APA has been preferred to be employed for various applications due to its inherent effectiveness against the rank deficient problem. However, the amount of complexity of the conventional APA could not be negligible because of the accomplishment of sample matrix inversion(SMI). Moreover, the 'shifting invariance property' usually exploited in single channel case does not hold for the application of space-time decision-directed equalizer(STDE) deployed in single-input-multi-output(SIMO) systems. Thus, it is impossible to utilize the fast adaptation schemes such as fast transversal filter(FlF) having low-complexity. To accomplish such tasks, this paper introduces the low-complexity APA by employing hyperplane projection algorithm, which shows the excellent tracking capability as well as the fast convergence. In order to confirm th validity of the proposed method, its performance is evaluated under wireless SIMO channel in respect to bit error rate(BER) behavior and computational complexity.

• The Journal of the Acoustical Society of Korea
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• v.33 no.6
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• pp.420-426
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• 2014

In this paper, we propose a new affine projection sign algorithm (APSA) to improve the convergence speed of the conventional APSA which has been proposed to enable the affine projection algorithm (APA) to operate robustly in impulsive noise environment. The conventional APSA has two advantages; it operates robustly against impulsive noise and does not need calculation for the inverse matrix. The proposed algorithm also has the conventional algorithm's advantages and furthermore, better convergence speed than the conventional algorithm. In the conventional algorithm, each input signal is normalized by $l_2$-norm of all input signals, but the proposed algorithm uses input signals normalized by their corresponding $l_2$-norm. We carried out a performance comparison of the proposed algorithm with the conventional algorithm using a system identification model. It is shown that the proposed algorithm has the faster convergence speed than the conventional algorithm.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.895-896
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• 2008

We propose a adaptive combination of affine projection algorithm (APA) filters with different update interval. Two APA filters with different update interval are adapted independently in order to keep the advantages of both component filters. This novel scheme provides improvement of performance in term of the convergence rate and the steady-state error. Experimental results show good properties of the proposed algorithm.