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Last Updated : 03/2016
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  • Variable step-size sign algorithm against impulsive noises

    JinWoo Yoo, JaeWook Shin, PooGyeon Park

    Signal Processing, IET

    Abstract This paper proposes a new variable step-size sign algorithm through the minimisation of mean-square deviation (MSD). As it is difficult to obtain the MSD accurately, the upper bound of the MSD is derived for calculating the step size at each iteration. The proposed algorithm is not only robust to impulsive noises, but also has improved filter performance in aspects of convergence rate and steady-state estimation error owing to the proposed variable step-size strategy. The simulation results verify that the proposed algorithm has better performance than the existing algorithms in a system-identification scenario in the presence of impulsive noises.
  • An optimal variable step-size affine projection algorithm for the modified filtered-x active noise control

    Ju-man Song, PooGyeon Park

    Signal Processing

    Abstract This paper introduces an optimal variable step-size affine projection algorithm for the modified filtered-x active noise control systems. First, the recursion form of the error covariance from the tap weight update equation is constructed, not ignoring the dependency between the estimation error and the secondary noise signal. Such consideration has not been concerned previously for the analysis of the modified filtered-x affine projection algorithm. Second, a recursion form of the mean square deviation is derived from that of the error covariance. From the recursion form, an optimal step size is decided to get the fastest convergence rate. Both the recursion forms of the mean square deviation and the optimal step size require scalar additions and multiplications that do not contribute to the overall complexity seriously. The simulation results on the active noise control environments show both fast convergence rate and low steady-state error.
  • Normalized Least-Mean-Square Algorithm with a Pseudo-Fractional Number of Orthogonal Correction Factors

    Sang Mok Jung, Ji-Hye Seo, PooGyeon Park

    Journal of Advanced in Computer networks

    Abstract This paper proposes a normalized LMS algorithm(LMS) that automatically deter ines the number of orthogonal correction factors (OCFs) by using a pseudo-fractional method, which relaxes the constraint that the number of OCFs in the NLMS algorithm must be integral and introduces the concept of a pseudo-fractional OCF number in the adaptation rule. The pseudo-fractional OCF number is adjusted by using the difference between the averages of the accumulated squared-output errors. The experimental results show that the proposed algorithm has not only a fast convergence rate but also a small steady-state estimation error with low computational complexity in comparison to existing algorithms with multiple input vectors.
  • Stabilization of Markovian jump systems with incomplete knowledge of transition probabilities and input quantization

    Bum Yong Park, Nam Kyu Kwon, PooGyeon Park

    Journal of the Franklin Institute

    Abstract This paper introduces the stabilization condition for the Markovian jump systems (MJSs) with incomplete knowledge of transition probabilities and input quantization. To obtain the less conservative stabilization condition, an appropriate weighting method is proposed by using all possible slack variables from the relationship of the transition probabilities, which does lead to a form of linear matrix inequalities (LMIs). Further, a proposed controller not only stabilizes the MJS with incomplete knowledge of transition probabilities but also eliminates the effect of input quantization. Simulation examples report the effectiveness of the proposed criterion.

  • A variable step-size diffusion affine projection algorithm

    Jin Woo Yoo, In Sun Song, Jae Wook Shin, PooGyeon Park

    International Journal of Communication Systems

    AbstractThis paper presents a new variable step-size diffusion affine projection algorithm (VSS-DAPA) to advance the filter performance of the diffusion affine projection algorithm (DAPA). The proposed VSS strategy is developed for the DAPA, which can solve the distributed estimation problem over diffusion networks well. To obtain the optimal step size reasonably, we seek the update recursion of mean-square deviation (MSD) that is suitable for the DAPA. The step size is optimally given through the minimization for the MSD of the DAPA at each iteration. The derived step size through the MSD minimization improves the filter performance with respect to the convergence and the estimation error in steady state. The results based on simulations demonstrate that the proposed VSS-DAPA performs better than the existing algorithms with a system-identification scenario in diffusion network. Copyright ⓒ 2015 John Wiley & Sons, Ltd.
  • Proportionate Sign Subband Adpative Filtering Algorithm for Network Echo Cancellers

    Ji-Hye Seo, Sang Mok Jung, PooGyeon Park

    Journal of Advanced in Computer networks

    Abstract This paper proposes a proportionate sign subband adaptive filtering (PSSAF) and an improved PSSAF (IPSSAF) algorithm for network echo cancellers that deal with impulsive interferences and sparse echo paths. Based on a sign subband adaptive filtering (SSAF) algorithm that is robust against impulsive interferences, the proposed algorithm minimizes L1-norm of the subband a posteriori error vector subject to a weighted constraint on the filter coefficients. A positive definite weighting matrix is used for the constraint. In this paper, we adopt a diagonal proportionate matrix for the constraint to achieve fast initial convergence rate when the impulse response is sparse. The components of it is roughly proportional to the absolute value of current estimate of the filter, so the resulting algorithm is called PSSAF algorithm. To achieve fast initial convergence rate even for rather non-sparse impulse responses, an improved proportionate matrix is also used for the constraint, and the resulting algorithm is named as IPSSAF algorithm. Experimental results show that the proposed algorithms are more robust against highly correlated input signals, impulsive interferences, and double-talk than the original normalized subband adaptive filtering algorithm and the class of roportionate subband adaptive filtering algorithms
  • Improved stability criteria for recurrent neural networks with interval time-varying delays via new Lyapunov functionals

    Won Il Lee, Seok Young Lee, PooGyeon Park

    Neurocomputing

    Abstract This paper considers the stability problem of recurrent neural networks with interval time-varying delays. Based on a new augmented Lyapunov-Krasovskii functional that contains four triple integral terms and additional terms obtained from the activation function condition, a stability condition is derived in terms of linear matrix inequalities (LMIs). Also, a further improved stability criterion is derived by bounding the derivative of a special case of the proposed Lyapunov?Krasovskii functional based on a new inequality proposed in Seuret and Gouaisbaut (2013) [27]. A numerical example shows the improvement of the proposed approach both in terms of computational complexity and conservatism.
  • Auxiliary Function-based Integral Inequalities for Quadratic Functions and their Applications to Time-delay Systems

    PooGyeon Park, Won Il Lee, Seok Young Lee

    Journal of The Franklin Institute

    Abstract Finding integral inequalities for quadratic functions plays a key role in the field of stability analysis. In such circumstances, the Jensen inequality has become a powerful mathematical tool for stability analysis of time-delay systems. This paper suggests a new class of integral inequalities for quadratic functions via intermediate terms called auxiliary functions, which produce more tighter bounds than what the Jensen inequality produces. To show the strength of the new inequalities, their applications to stability analysis for time-delay systems are given with numerical examples.

  • {\ mathcal {H}} _\ infty control of continuous-time uncertain linear systems with quantized-input saturation and external disturbances

    Bum Yong Park, Sung Wook Yun, PooGyeon Park

    Nonlinear Dynamics

    Abstract Abstract This paper introduces an { mathcal {H}} _ infty state- feedback controller for uncertain linear systems with quantized-input saturation and external disturbances. The proposed controller comprises two parts: a linear control part to achieve an H∞ performance against model uncertainties and the mismatched part of the disturbances and a nonlinear control part to eliminate the effect of input quantization and the matched part of the disturbances, which provides the better disturbance attenuation performance than a controller that deals with a unified disturbance regardless of the presence of matched and mismatched parts. Simulation results confirm the effectiveness of the proposed controller.

  • An Improved NLMS Algorithm in Sparse Systems Against Noisy Input Signals

    Jin Woo Yoo, Jae Wook Shin, PooGyeon Park

    Electronics Letters

    Abstract This brief proposes a novel normalized least mean square algorithm that is characterized by robustness against noisy input signals. To compensate for the bias caused by the input noise that is added at the filter input, a derivation method based on reasonable assumptions finds a bias-compensating vector. Moreover, the proposed algorithm has a fast convergence rate when applied to sparse systems, owing to its L0-norm cost in the proposed update equation. The simulation results verify that the proposed algorithm improves the performance of the filter, in terms of system identification in sparse systems, in the presence of noisy input signals.