• Journal of information and communication convergence engineering
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• v.12 no.3
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• pp.140-144
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• 2014

A general linearly constrained adaptive array is examined in the weight vector space to illustrate the array performance with respect to the gain factor. A narrowband linear adaptive array is implemented in a coherent signal environment. It is shown that the gain factor in the general linearly constrained adaptive array has an effect on the linear constraint gain of the conventional linearly constrained adaptive array. It is observed that a variation of the gain factor of the general linearly constrained adaptive array results in a variation of the distance between the constraint plane and the origin in the translated weight vector space. Simulation results are shown to demonstrate the effect of the gain factor on the nulling performance.

• Journal of information and communication convergence engineering
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• v.15 no.3
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• pp.137-142
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• 2017

A general linearly constrained narrowband adaptive array is examined in the eigenvector space. The optimum weight vector in the eigenvector space is shown to have the same performance as in the standard coordinate system, except that the input signal correlation matrix and look direction steering vector are replaced with the eigenvalue matrix and transformed steering vector. It is observed that the variation in gain factor results in the variation in the distance between the constraint plane and the origin in the translated weight vector space such that the increase in gain factor decreased the distance from the constraint plane to the origin, thus affecting the nulling performance. Simulation results showed that the general linearly constrained adaptive array performed better at an optimal gain factor compared with the conventional linearly constrained adaptive array in a coherent signal environment and the former showed similar performance as the latter in a noncoherent signal environment.

• Journal of information and communication convergence engineering
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• v.15 no.2
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• pp.73-78
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• 2017

A general linearly constrained broadband adaptive array is examined in the eigenvector space with respect to the optimal weight vector and the adaptive algorithm. The optimal weight vector and the general adaptive algorithm in the eigenvector space are obtained by eigenvector matrix transformation. Their operations are shown to be the same as in the standard coordinate system except for the relevant transformed vectors and matrices. The nulling performance of the general linearly constrained broadband adaptive array depends on the gain factor such that the constraint plane is shifted perpendicularly to the origin by an increase in the gain factor. The general linearly constrained broadband adaptive array is observed to perform better than a conventional linearly constrained adaptive array in a coherent signal environment, while the former performs similarly to the latter in a non-coherent signal environment.

• The Journal of the Acoustical Society of Korea
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• v.36 no.3
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• pp.151-157
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• 2017

A general linearly constrained adaptive array is proposed to improve the nulling performance. The nulling performance is examined in the array weight vector space. It is shown that the constraint plane is shifted to the origin perpendicularly by the gain factor such that the increase of the gain factor results in the decrease of the distance from the constraint plane to the origin. Thus the variation of the gain factor has an effect on the extent of orthogonality between the weight vector and the steering vectors for the interferences such that the nulling performance of the general linearly constrained adaptive array is improved by the gain factor. It is observed that the proposed adaptive array with an optimum value of the gain factor yields a better nulling performance in coherent signal environment and a similar nulling performance in noncoherent signal environment compared to the conventional linearly constrained adaptive array.

• International journal of advanced smart convergence
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• v.10 no.2
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• pp.175-186
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• 2021

We have studied the speed regulation of the permanent magnet synchronous motor (PMSM) servo system in this paper. To optimize the PMSM servo system's speed-control performance with disturbances, a non-linear speed-control technique using a back-propagation neural network (BP-NN) algorithm forthe controller design of the PMSM speed loop is introduced. To solve the slow convergence speed and easy to fall into the local minimum problem of BP-NN, we develope an improved BP-NN control algorithm by limiting the range of neural network outputs of the proportional coefficient Kp, integral coefficient Ki of the controller, and add adaptive gain factor β, that is the internal gain correction ratio. Compared with the conventional PI control method, our improved BP-NN control algorithm makes the settling time faster without static error, overshoot or oscillation. Simulation comparisons have been made for our improved BP-NN control method and the conventional PI control method to verify the proposed method's effectiveness.

• Journal of information and communication convergence engineering
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• v.10 no.2
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• pp.108-116
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• 2012

A novel approach to general linearly constrained adaptive arrays is presented to improve the nulling performance in coherent and noncoherent environments. The narrowband and broadband linearly constrained adaptive arrays are implemented to examine the array performance. It is shown that the proposed approach performs better than the conventional adaptive arrays and the nulling performance depends on the gain factor for the desired response.

• Proceedings of the KOSOMBE Conference
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• v.1991 no.11
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• pp.44-47
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• 1991

There exists a transform trellis code that is optimal for stationary Gaussian sources and the squared-error distortion measure at all rates. In this paper, we train an asymptotically optimal version of such a code to obtain one which is matched better to the statistics of real world data. The training algorithm uses the M-algorithm to search the trellis codebook and the LBG-algorithm to update the trellis codebook. To adapt the codebook for the varying input data. we use two gain-adaptive methods. The gain-adaptive scheme 1, which normalizes input block data by its gain factor, is applied to images at rate 0.5 bits/pixel. When each block is encoded at the same rate, the nonstationarity among the block variances leads to a variation in the resulting distortion from one block to another. To alleviate the non-uniformity among the encoded image, we design four clusters from the block power, in which each cluster has its own trellis codebook and different rates. The rate of each cluster is assigned through requiring a constant distortion per-letter. This gain-adaptive scheme 2 produces good visual and measurable quality at low rates.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.758-760
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• 1995

The purpose of noise cancellation is to estimating signals corrupted by additive noise or interference. In this paper, an adaptive noise canceller is built from a Walsh filter with a new adaptive algorithm. The Walsh filter consists of a Walsh function. Since the Walsh functions are either even or odd functions, the covariance matrix in the tap gain adjustment algorithm can be reduced to a simple form. In this paper, minimization of the mean squre error is accomplished by a proposed adaptive algorithm. The conventional adaptation techniques use a fixed time constant convergence factor by trial and error methods. In this paper, a convergence factor is obtained that is tailored for each adaptive filter coefficient and is updated at each block iteration.

• Journal of the Korean Institute of Telematics and Electronics
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• v.19 no.5
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• pp.12-19
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• 1982

In general the design methods of adaptive filter are divided into two categories, one is based upon the local parameter optimization theory and the other is based upon stability theory. Among the various design techniques, the LMS algorithm by steepest-descent method which is based upon local parameter optimization theory is used widely. In designing the adaptive filter, the most important factor is the convergence rate of the algorithm. In this paper a new algorithm is proposed to improve the convergence rate of adaptive firter compared with the commonly used LMS algorithm. The faster convergence rate is obtained by adjusting the adaptation gain of LMS algorithm. And various aspects of improvement of the adaptive filter characteristics are discussed in detail.

• The Transactions of the Korean Institute of Power Electronics
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• v.17 no.5
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• pp.423-430
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• 2012

An adaptive maximum power point tracking (MPPT) scheme employing a variable scaling factor is presented. A MPPT control loop was constructed analytically and the magnitude variation in the MPPT loop gain according to the operating point of the PV array was identified due to the nonlinear characteristics of the PV array output. To make the crossover frequency of the MPPT loop gain consistent, the variable scaling factor was determined using an approximate curve-fitted polynomial equation about linear expression of the error. Therefore, a desirable dynamic response and the stability of the MPPT scheme were maintained across the entire MPPT voltage range. The simulation and experimental results obtained from a 3 KW rated prototype demonstrated the effectiveness of the proposed MPPT scheme.