• Title/Summary/Keyword: Square network

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Genetically Optimization of Fuzzy C-Means Clustering based Fuzzy Neural Networks (Subtractive Clustering 알고리즘을 이용한 퍼지 RBF 뉴럴네트워크의 동정)

  • Choi, Jeoung-Nae;Oh, Sung-Kwun;Kim, Hyun-Ki
    • Proceedings of the KIEE Conference
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    • 2008.10b
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    • pp.239-240
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    • 2008
  • 본 논문에서는 Subtractive clustering 알고리즘을 이용한 Fuzzy Radial Basis Function Neural Network (FRBFNN)의 규칙 수를 자동적으로 생성하는 방법을 제시한다. FRBFNN은 멤버쉽 함수로써 기존 RBFNN에서 가우시안이나 타원형 형태의 특정 RBF를 사용하는 구조와 달리 Fuzzy C-Means clustering 알고리즘에서 사용하는 거리에 기한 멤버쉽 함수를 사용하여 전반부의 공간 분할 및 활성화 레벨을 결정하는 구조이다. 본 논문에서는 데이터의 밀집도에 기반을 두어 클러스터링을 하는 Subtractive clustering 알고리즘을 사용하여 퍼지 규칙의 수와 같은 의미를 갖는 분할할 입력공간의 수와 분할된 입력공간의 중심값을 동정하며, Least Square Estimator (LSE) 알고리즘을 사용하여 후반부 다항식의 계수를 추정 한다.

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Development of Solar Power Output Prediction Method using Big Data Processing Technic (태양광 발전량 예측을 위한 빅데이터 처리 방법 개발)

  • Jung, Jae Cheon;Song, Chi Sung
    • Journal of the Korean Society of Systems Engineering
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    • v.16 no.1
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    • pp.58-67
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    • 2020
  • A big data processing method to predict solar power generation using systems engineering approach is developed in this work. For developing analytical method, linear model (LM), support vector machine (SVN), and artificial neural network (ANN) technique are chosen. As evaluation indices, the cross-correlation and the mean square root of prediction error (RMSEP) are used. From multi-variable comparison test, it was found that ANN methodology provides the highest correlation and the lowest RMSEP.

A Neural Multiple LMS Based ANC System for Reducing Acoustic Noise of High-Speed Trains (신경회로망 다중 LMS 기법을 이용한 고속철도의 실내소음저감을 위한 ANC 시스템)

  • Cho, Hyun-Cheol;Lee, Kwon-Soon;Nam, Hyun-Do
    • The Transactions of the Korean Institute of Electrical Engineers P
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    • v.58 no.4
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    • pp.385-390
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    • 2009
  • This paper presents a novel active noise control (ANC) system using least mean square (LMS) algorithm and neural network approach for decreasing acoustic noise signals inside high-speed trains. We construct a LMS framework as a nominal ANC system and additionally design an artificial single-layered perceptron model as an auxiliary ANC which is aimed to reduce real-time residuary noise due to its nonstationary and uncertain nature. Parameter vector of the hybrid ANC is determined through online estimation to realize an adaptive ANC configuration by means of the steepest descent algorithm. We achieve simulation experiment to demonstrate the proposed ANC system employing realistic acoustic noise signals measured in Korea Train eXpress (KTX).

Harmonic Elimination and Reactive Power Compensation with a Novel Control Algorithm based Active Power Filter

  • Garanayak, Priyabrat;Panda, Gayadhar
    • Journal of Power Electronics
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    • v.15 no.6
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    • pp.1619-1627
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    • 2015
  • This paper presents a power system harmonic elimination using the mixed adaptive linear neural network and variable step-size leaky least mean square (ADALINE-VSSLLMS) control algorithm based active power filter (APF). The weight vector of ADALINE along with the variable step-size parameter and leakage coefficient of the VSSLLMS algorithm are automatically adjusted to eliminate harmonics from the distorted load current. For all iteration, the VSSLLMS algorithm selects a new rate of convergence for searching and runs the computations. The adopted shunt-hybrid APF (SHAPF) consists of an APF and a series of 7th tuned passive filter connected to each phase. The performance of the proposed ADALINE-VSSLLMS control algorithm employed for SHAPF is analyzed through a simulation in a MATLAB/Simulink environment. Experimental results of a real-time prototype validate the efficacy of the proposed control algorithm.

Application of Multivariate Adaptive Regression Spline-Assisted Objective Function on Optimization of Heat Transfer Rate Around a Cylinder

  • Dey, Prasenjit;Das, Ajoy K.
    • Nuclear Engineering and Technology
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    • v.48 no.6
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    • pp.1315-1320
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    • 2016
  • The present study aims to predict the heat transfer characteristics around a square cylinder with different corner radii using multivariate adaptive regression splines (MARS). Further, the MARS-generated objective function is optimized by particle swarm optimization. The data for the prediction are taken from the recently published article by the present authors [P. Dey, A. Sarkar, A.K. Das, Development of GEP and ANN model to predict the unsteady forced convection over a cylinder, Neural Comput. Appl. (2015) 1-13]. Further, the MARS model is compared with artificial neural network and gene expression programming. It has been found that the MARS model is very efficient in predicting the heat transfer characteristics. It has also been found that MARS is more efficient than artificial neural network and gene expression programming in predicting the forced convection data, and also particle swarm optimization can efficiently optimize the heat transfer rate.

Reflectarray Antenna Capable of 1-Bit Switchable W-Band Beamforming Network

  • Asamani, Bismark;Pyo, Seongmin
    • Journal of IKEEE
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    • v.25 no.2
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    • pp.408-411
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    • 2021
  • This paper presents a new reflectarray antenna capable with 1-bit switchable capability for W-band beamforming network. The proposed antenna has been optimized using two unit-cells with sizes of 1.0 mm and 1.3 mm to form a total number of 193 radiating elements on a square aperture surface of length 30 mm. These radiating elements are spaced 0.5 wavelengths apart and fed by a 15 dBi pyramidal horn antenna as the feed antenna placed 53 mm away from the aperture center. The proposed reflectarray achieves a realized peak gain of 22.52 dBi, a half-power beamwidth of 5.1° in both E- and H-planes at the test frequency of 80 GHz and operates over a wide bandwidth from 74 GHz to 90 GHz.

A Study on Temperature and Velocity Profiles of Natural Convection in a Square Enclosure (사각 밀폐공간내의 자연대류의 온도 및 속도 분포에 관한 연구)

  • Chang, Tae-Hyun;Lee, Jong-Boong
    • Journal of the Korean Society of Industry Convergence
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    • v.7 no.4
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    • pp.391-397
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    • 2004
  • This paper presented results of experimental and numerical work for natural convection in a square enclosure by using PIV technique. 2D PIV technique and liquid crystal are employed for velocity and temperature measurement in water. The numerical method used this work is a CFD corde, STAR-CD. The experimental work are compared with these of numerical results.

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Design of self-tuning controller utilizing neural network (신경회로망기법을 이용한 자기동조제어기 설계)

  • 구영모;이윤섭;김대종;임은빈;우광방
    • 제어로봇시스템학회:학술대회논문집
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    • 1989.10a
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    • pp.399-401
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    • 1989
  • Utilizing an interconnected set of neuron-like elements, the present study is to provide a method of parameter estimation for a second order linear time invariant system of self-tuning controller. The result from the proposed method is evaluated by comparing with those obtained by the recursive least square (RLS) identification algorithm and extended recursive least square (ERLS) algorithm, and it shows that, although the smoothness of system performance is still to be improved, the effectiveness of shorter computing time is demonstrated which may be of considerable value to real time computing.

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Forecasting Internet Traffic by Using Seasonal GARCH Models

  • Kim, Sahm
    • Journal of Communications and Networks
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    • v.13 no.6
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    • pp.621-624
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    • 2011
  • With the rapid growth of internet traffic, accurate and reliable prediction of internet traffic has been a key issue in network management and planning. This paper proposes an autoregressive-generalized autoregressive conditional heteroscedasticity (AR-GARCH) error model for forecasting internet traffic and evaluates its performance by comparing it with seasonal autoregressive integrated moving average (ARIMA) models in terms of root mean square error (RMSE) criterion. The results indicated that the seasonal AR-GARCH models outperformed the seasonal ARIMA models in terms of forecasting accuracy with respect to the RMSE criterion.

A Polarization Diversity Patch Antenna with a Reconfigurable Feeding Network

  • Lee, Sung Woo;Sung, Youngje
    • Journal of electromagnetic engineering and science
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    • v.15 no.2
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    • pp.115-119
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    • 2015
  • This paper proposes a reconfigurable square-patch antenna with polarization diversity. The proposed antenna consists of a square radiating patch and a Y-shaped feed structure with two PIN diodes. The shape of the feed structure can be changed by adjusting the bias states of the two PIN diodes, which helps switch between two orthogonal linear polarizations. The polarization diversity characteristic is validated by the simulated current distribution and the measured radiation pattern.