• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.823-828
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• 1992

A novel steepest descent adaptive filter algorithm, which uses the instantaneous stochastic gradient for the steepest descent direction, is derived from a newly devised performance index function. The performance function for the new algorithm is improved from that for the LMS in consideration that the stochastic steepest descent method is utilized to minimize the performance index iterativly. Through mathematical analysis and computer simulations, it is verified that there are substantial improvements in convergence and misadjustments even though the computational simplicity and the robustness of the LMS algorithm are hardly sacrificed. On the other hand, the new algorithm can be interpreted as a variable step size adaptive filter, and in this respect a heuristic method is proposed in order to reduce the noise caused by the step size fluctuation.

• 한국통신학회논문지
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• 제25권7A호
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• pp.978-985
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• 2000

블라인드 등화기는 학습신호를 이용하지 않고 저송된 데이터의 알려진 특성을 이용해 신호를 복원하며 일반적으로 가장 많이 이용되는 알고리즘은 구현이 간단한 Steepest Gradient Descent 계열의 알고리즘으로서 CMA나 Sato 알고리즘이 여기에 속한다. 본 논문에선, CMA 및 Normalized CMA (NCMA)의 장점과 이중모드 위상복원 알고리즘의 장점을 결합하는 이중모드 NCMA 알고리즘을 제안하고 QAM 시스템에 적용한 컴퓨터 시뮬레이션을 수행하여 제안한 알고리즘이 CMA와 이중모드 CMA 보다 더 빠른 수렴속도와 더 적은 정상상태 잔류에서 특성을 가짐을 확인하다.

• Journal of Electrical Engineering and information Science
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• 제2권6호
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• pp.191-196
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• 1997

Fuzzy clustering has been playing an important role in solving many problems. Fuzzy c-Means(FCM) algorithm is most frequently used for fuzzy clustering. But some fixed point of FCM algorithm, know as Tucker's counter example, is not a reasonable solution. Moreover, FCM algorithm is impossible to perform the on-line learning since it is basically a batch learning scheme. This paper presents unsupervised learning networks as an attempt to improve shortcomings of the conventional clustering algorithm. This model integrates optimization function of FCM algorithm into unsupervised learning networks. The learning rule of the proposed scheme is a result of formal derivation based on the gradient descent procedure of a fuzzy objective function. Using the result of formal derivation, two algorithms of fuzzy cluster analysis, the batch learning version and on-line learning version, are devised. They are tested on several data sets and compared with FCM. The experimental results show that the proposed algorithms find out the reasonable solution on Tucker's counter example.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 하계종합학술대회 논문집
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• pp.1033-1036
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• 1999

This paper proposes an efficient fault diagnosis for digital circuits using multilayer neural networks. The efficient learning algorithm is also proposed for the multilayer neural network, which is combined the steepest descent for high-speed optimization and the dynamic tunneling for global optimization. The fault-diagnosis system using the multilayer neural network of the proposed algorithm has been applied to the parity generator circuit. The simulation results shows that the proposed system is higher convergence speed and rate, in comparision with system using the backpropagation algorithm based on the gradient descent.

• 한국컴퓨터정보학회논문지
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• 제22권4호
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• pp.9-16
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• 2017

Machine learning has found widespread implementations and applications in many different domains in our life. Logistic regression is a type of classification in machine leaning, and is used widely in many fields, including medicine, economics, marketing and social sciences. In this paper, we present the MapReduce implementation of three existing algorithms, this is, Gradient Descent algorithm, Cost Minimization algorithm and Newton-Raphson algorithm, for logistic regression on RHadoop that integrates R and Hadoop environment applicable to large scale data. We compare the performance of these algorithms for estimation of logistic regression coefficients with real and simulated data sets. We also compare the performance of our RHadoop and RHIPE platforms. The performance experiments showed that our Newton-Raphson algorithm when compared to Gradient Descent and Cost Minimization algorithms appeared to be better to all data tested, also showed that our RHadoop was better than RHIPE in real data, and was opposite in simulated data.

• 한국멀티미디어학회논문지
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• 제21권4호
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• pp.471-477
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• 2018

As an important key technology using on electronic devices, face recognition has become one of the hottest technology recently. The traditional BP Neural network has a strong ability of self-learning, adaptive and powerful non-linear mapping but it also has disadvantages such as slow convergence speed, easy to be traversed in the training process and easy to fall into local minimum points. So we come up with an algorithm based on BP neural network but also combined with the PCA algorithm and other methods such as the elastic gradient descent method which can improve the original network to try to improve the whole recognition efficiency and has the advantages of both PCA algorithm and BP neural network.

• Transactions on Control, Automation and Systems Engineering
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• 제3권1호
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• pp.58-65
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• 2001

This paper proposes a self tuning fuzzy inference method by the genetic algorithm in the fuzzy-sliding mode control for a robot. Using this method, the number of inference rules and the shape of membership functions are optimized without an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method. And, it is guaranteed that he selected solution become the global optimal solution by optimizing the Akaikes information criterion expressing the quality of the inference rules. The trajectory tracking simulation and experiment of the polishing robot show that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the proposed fuzzy-sliding mode controller provides reliable tracking performance during the polishing process.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제8권4호
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• pp.153-162
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• 2019

최근 철도소음으로 인해 발생하는 궤도 주변 구조물의 민원 방지와 궤도 주변 산업단지의 초정밀 장비들의 정상적인 운영을 위해 철도 진동을 정량적으로 평가할 수 있는 기술개발이 필요하다. 기존의 해석적인 방법은 매우 복잡한 동적 응답 모델이 요구되며, 요구 모델의 부정확성으로 인한 결과의 신뢰성을 확보하기 어려운 문제가 있다. 따라서, 본 논문에서는 철도 진동에 영향을 주는 요소들을 분류한 국내 철도진동 실측 데이터베이스를 기반으로 Linear Regression, Gradient Descent 기법을 이용해 철도 운행으로부터 발생되는 진동값을 추론하는 철도진동 평가 알고리즘 및 시스템을 제안한다. 제안된 알고리즘으로 얻은 추론결과는 기존의 해석적 방법에 비해 높은 효율성과 정확성을 보인다.

• 말소리와 음성과학
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• 제7권4호
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• pp.17-25
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• 2015

For blind source separation of convolutive mixtures, FDICA(Frequency Domain Independent Component Analysis) algorithms are generally used. Since FDICA algorithm such as Sawada FDICA, IVA(Independent Vector Analysis) works on the frequency bin basis with a natural gradient descent method, it takes much time to converge. In this paper, we propose a new method to improve convergence speed in FDICA algorithm. The proposed method reduces the number of iteration drastically in the process of natural gradient descent method by applying a weighted inner product constraint of unmixing matrix. Experimental results have shown that the proposed method achieved large improvement of convergence speed without degrading the separation performance of the baseline algorithms.

• ETRI Journal
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• 제32권3호
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• pp.486-489
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• 2010

This letter presents a modified Pegasos algorithm for graph-based dependency parsing. We show that the modified Pegasos algorithm outperforms the margin infused relaxed and passive aggressive algorithms in three languages.