• Journal of Information Technology Applications and Management
• /
• v.9 no.2
• /
• pp.1-16
• /
• 2002

Recently, neural network methods have been studied to find out more valuable information in data bases. But the supervised learning methods of neural networks have an overfitting problem, which leads to errors of target patterns. And the unsupervised learning methods can distort important information in the process of regularizing data. Thus they can't efficiently classify data, To solve the problems, this paper introduces a hybrid neural networks HACAB(Hybrid Algorithm combining a Competition learning model And BP Algorithm) combining a competition learning model and 8P algorithm. HACAB is designed for cases which there is no target patterns. HACAB makes target patterns by adopting a competition learning model and classifies input patterns using the target patterns by BP algorithm. HACAB is evaluated with random input patterns and Iris data In cases of no target patterns, HACAB can classify data more effectively than BP algorithm does.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.19 no.4
• /
• pp.461-469
• /
• 2008

This paper proposes a hybrid symbol offset estimation algorithm for MIMO(Multiple Input Multiple Output) OFDM system. As MIMO OFDM systems are multiple transmitter and receiver antenna systems, apart from SISO(Single Input Single Output) system, it is possible to use several combining techniques which are used in multiple receive antenna system. In this paper, we propose hybrid symbol offset estimation algorithms using combining techniques in multiple receive antenna systems, simulate and show the performances in MIMO system environments. The proposed equal gain combining correlation algorithm has better performance 1.8 times in searching the ideal symbol offset rather than the conventional early symbol offset algorithm in severe ISI channel.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.05a
• /
• pp.182-185
• /
• 2003

This paper presents a new design algorithm for the combination with the fuzzy classifier and the Bayesian classifier. Only few attempts have so far been made at providing an effective design algorithm combining the advantages and removing the disadvantages of two classifiers. Specifically, the suggested algorithms are composed of three steps: the combining, the fuzzy-set-based pruning, and the fuzzy set tuning. In the combining, the multi-inputs and multi-outputs (MIMO) fuzzy model is used to combine two classifiers. In the fuzzy-set-based pruning, to effectively decrease the complexity of the fuzzy-Bayesian classifier and the risk of the overfitting, the analysis method of the fuzzy set and the recursive pruning method are proposesd. In the fuzzy set tuning for the misclassified feature vectors, the premise parameters are adjusted by using the gradient decent algorithm. Finally, to show the feasibility and the validity of the proposed algorithm, a computer simulation is provided.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.5
• /
• pp.1588-1603
• /
• 2014

In space diversity combining, conventional methods such as maximal ratio combining (MRC), equal gain combining (EGC) and selection combining (SC) are commonly used to improve the output signal-to-noise ratio (SNR) provided that the channel is perfectly estimated at the receiver. However, in practice, channel estimation is often imperfect and this indeed deteriorates the system performance. In this paper, diversity combining techniques based on two evolutionary algorithms, namely genetic algorithm (GA) and particle swarm optimization (PSO) are proposed and compared. Numerical results indicate that the proposed methods outperform the conventional MRC, EGC and SC methods when the channel estimation is imperfect while it shows similar performance as that of MRC when the channel is perfectly estimated.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.45 no.5
• /
• pp.21-29
• /
• 2008

This paper proposes a carrier frequency of set estimation algorithm for IEEE802.11n system. As IEEE802.11n is a multiple input multiple output(MIMO) system, so there are several combining techniques which are used in multiple receive antenna system. In this paper, we propose hybrid carrier frequency offset estimation algorithms using combining techniques in multiple receive antenna systems, and show that the proposed selection combining carrier frequency offset (CFO) estimation algorithm can estimate carrier frequency offset within 1/10 MSE error at SNR 10 dB in channel B and within 1/2 MSE error at SNR 10 dB in channel D rather than the conventional MIMO CFO one.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.8 no.2
• /
• pp.281-288
• /
• 2004

In this paper, we consider the diversity combining method for multiple symbol differential detection (MSDD) over the slow fading diversity channel. Though the performance of the optimum maximum-likelihood sequence estimator for MSDD approaches the performance of maximal-ratio combining with differential encoding, the complexity increases exponentially as the size of MSDD block is increased. This new pre-combining method can make use of the efficient MSDD algorithm that has a complexity increasing linearly with the block length or MSDD. Thus, in many wireless scenarios where it is not possible to perform coherent detection. this pre-combined diversity MSDD can be applied to obtain substantial gain compare to conventional differential detection.

• Journal of the Korean Institute of Telematics and Electronics A
• /
• v.32A no.5
• /
• pp.14-23
• /
• 1995

A new dual-mode algorithm for blind equalization of quadrature amplitude modulation (QAM) signals is proposed. To solve the problem that the constant modulus algorithm (CMA) converges to the constellation with the arbitrary phase rotation, with the modification of the CMA, the proposed algorithm accomplishes blind equalization and carrier phase recovery simultaneously. In addition, the dual-mode algorithm combining the modified constant modulus algorithm (MCMA) with decision-directed (DD) algorithm achieves the performance enhancement of blind convergence speed and steady-state residual ISI. So we can refer the proposed algorithm to as a scheme for joint blind equalization and carrier phase recovery. Simulation results for i.i.d. input signals confirm that the dual-mode algorithm results in faster convergence speed, samller residual ISI, and better carrier phase recovery than those of the CMA and DD algorithm without any significant increase in computational complexity.

• Proceedings of the IEEK Conference
• /
• 2001.06a
• /
• pp.233-236
• /
• 2001

Orthogonal freqency division multiplexing (OFDM) is an attractive technique for achieving high-bit-rate wireless data transmission. However, the potentially large peak-to-average power ratio (PAPR) has limited its application; An OFDM signal with the large PAPR can cause power degradation (In-band distortion) and spectral spreading (Out-of-band distortion) by being clipped passing through a power amplifier. Thus, we propose the combining algorithm of Hadamard transform and phase shift, which is ascribed to the relation between the correlation of the IFFT input sequence function and PAPR. Extensive computer simulations show that the combining algorithm is an effective technique to reduce PAPR.

• Journal of Institute of Control, Robotics and Systems
• /
• v.17 no.6
• /
• pp.610-618
• /
• 2011

For improved localization around the indoor/outdoor transit area of buildings, this paper proposes an efficient algorithm combining the measurements from the WLAN (Wireless Local Area Network) and the GPS (Global Positioning System) for. The proposed hybrid localization algorithm considers both multipath errors and NLOS (Non-Line-of-Sight) errors, which occur in most wireless localization systems. To detect and isolate multipath errors occurring in GPS measurements, the propose algorithm utilizes conventional multipath test statistics. To convert WLAN signal strength measurements to range estimates in the presence of NLOS errors, a simple and effective calibration algorithm is designed to compute conversion parameters. By selecting and combining the reliable GPS and WLAN measurements, the proposed hybrid localization algorithm provides more accurate location estimates. An experiment result demonstrates the performance of the proposed algorithm.

• Korean Journal of Optics and Photonics
• /
• v.31 no.6
• /
• pp.247-258
• /
• 2020

In this paper, based on a stochastic parallel gradient descent (SPGD) algorithm we study phase control of a coherent-beam-combining system under turbulent atmospheric conditions. Based on the statistical theory of atmospheric turbulence, we carry out the analysis of the phase and wavefront distortion of a laser beam propagating through a turbulent atmospheric medium. We also conduct numerical simulations of a coherent-beam-combining system with 7- and 19-channel laser beams distorted by atmospheric turbulence. Through numerical simulations, we characterize the phase-control characteristics and efficiency of the coherent-beam-combining system under various degrees of atmospheric turbulence. It is verified that the SPGD algorithm is capable of realizing 7-channel coherent beam combining with a beam-combining efficiency of more than 90%, even under the turbulent atmospheric conditions up to cn2 of 10-13 m-2/3. In the case of 19-channel coherent beam combining, it is shown that the same turbulent atmospheric conditions result in a drastic reduction of the beam-combining efficiency down to 60%, due to the elevated impact of the corresponding refractive-index inhomogeneity. In addition, by putting together the number of iterations of the SPGD algorithm required for phase locking under atmospheric turbulence and the time intervals of atmospheric phenomena, which typically are of the order of ㎲, it is estimated that hundreds of MHz to a few GHz of computing bandwidth of SPGD-based phase control may be required for a coherent-beam-combining system to confront such turbulent atmospheric conditions. We expect the results of this paper to be useful for quantitatively analyzing and predicting the effects of atmospheric turbulence on the SPGD-based phase-control performance of a coherent-beam-combining system.