• Title/Summary/Keyword: radial basis function networks

검색결과 183건 처리시간 0.033초

Using Neural Networks to Forecast Price in Competitive Power Markets

  • Sedaghati, Alireza
    • 제어로봇시스템학회:학술대회논문집
    • /
    • 제어로봇시스템학회 2005년도 ICCAS
    • /
    • pp.271-274
    • /
    • 2005
  • Under competitive power markets, various long-term and short-term contracts based on spot price are used by producers and consumers. So an accurate forecasting for spot price allow market participants to develop bidding strategies in order to maximize their benefit. Artificial Neural Network is a powerful method in forecasting problem. In this paper we used Radial Basis Function(RBF) network to forecast spot price. To learn ANN, in addition to price history, we used some other effective inputs such as load level, fuel price, generation and transmission facilities situation. Results indicate that this forecasting method is accurate and useful.

  • PDF

A two-step approach for joint damage diagnosis of framed structures using artificial neural networks

  • Qu, W.L.;Chen, W.;Xiao, Y.Q.
    • Structural Engineering and Mechanics
    • /
    • 제16권5호
    • /
    • pp.581-595
    • /
    • 2003
  • Since the conventional direct approaches are hard to be applied for damage diagnosis of complex large-scale structures, a two-step approach for diagnosing the joint damage of framed structures is presented in this paper by using artificial neural networks. The first step is to judge the damaged areas of a structure, which is divided into several sub-areas, using probabilistic neural networks with natural Frequencies Shift Ratio inputs. The next step is to diagnose the exact damage locations and extents by using the Radial Basis Function (RBF) neural network with the second Element End Strain Mode of the damaged sub-area input. The results of numerical simulation show that the proposed approach could diagnose the joint damage of framed structures induced by earthquake action effectively and has reliable anti-jamming abilities.

신경망을 이용한 고성능 콘크리트의 배합설계 (High Performance Concrete Mixture Design using Artificial Neural Networks)

  • 양승일;윤영수;이승훈;김규동
    • 한국콘크리트학회:학술대회논문집
    • /
    • 한국콘크리트학회 2002년도 봄 학술발표회 논문집
    • /
    • pp.545-550
    • /
    • 2002
  • Concrete is one of the essential structural materials in the construction. But, concrete consists of many materials and is affected by many factors such as properties of materials, site environmental situations, and skill of constructor. Therefore, concrete mixes depend on experiences of experts. However, it is more and more difficult to determine concrete mixes design by empirical means because more ingredients like mineral and chemical admixtures are included. Artificial Neural Networks(ANN) are a mimic models of human brain to solve a complex nonlinear problem. They are powerful pattern recognizers and classifiers, also their computing abilities have been proven in the fields of prediction, estimation and pattern recognition. Here, among them, the back propagation network and radial basis function network are used. Compositions of high-performance concrete mixes are eight components(water, cement, fine aggregate, coarse aggregate, fly ash, silica fume, superplasticizer and air-entrainer). Compressive strength and slump are measured. The results show that neural networks are proper tools to minimize the uncertainties of the design of concrete mixtures.

  • PDF

빅 데이터 처리를 위한 증분형 FCM 기반 RBF Neural Networks 패턴 분류기 설계 (Design of Incremental FCM-based RBF Neural Networks Pattern Classifier for Processing Big Data)

  • 이승철;오성권;노석범
    • 대한전기학회:학술대회논문집
    • /
    • 대한전기학회 2015년도 제46회 하계학술대회
    • /
    • pp.1343-1344
    • /
    • 2015
  • 본 연구에서는 증분형 FCM(Incremental Fuzzy C-Means: Incremental FCM) 클러스터링 알고리즘을 기반으로 방사형 기저함수 신경회로망(Radial Basis Function Neural Networks: RBFNN) 패턴 분류기를 설계한다. 방사형 기저함수 신경회로망은 조건부에서 가우시안 함수 또는 FCM을 사용하여 적합도를 구하였지만, 제안된 분류기에서는 빅 데이터간의 적합도를 구하기 위해 증분형 FCM을 사용한다. 또한, 빅 데이터를 학습하기 위해 결론부에서 재귀최소자승법(Recursive Least Square Estimation: RLSE)을 사용하여 다항식 계수를 추정한다. 마지막으로 추론부에서는 증분형 FCM에서 구한 적합도와 재귀최소자승법으로 구한 다항식을 이용하여 최종 출력을 구한다.

  • PDF

다항식 방사형기저함수 신경회로망을 이용한 ASP 모델링 및 시뮬레이터 설계 (Design of Modeling & Simulator for ASP Realized with the Aid of Polynomiai Radial Basis Function Neural Networks)

  • 김현기;이승주;오성권
    • 전기학회논문지
    • /
    • 제62권4호
    • /
    • pp.554-561
    • /
    • 2013
  • In this paper, we introduce a modeling and a process simulator developed with the aid of pRBFNNs for activated sludge process in the sewage treatment system. Activated sludge process(ASP) of sewage treatment system facilities is a process that handles biological treatment reaction and is a very complex system with non-linear characteristics. In this paper, we carry out modeling by using essential ASP factors such as water effluent quality, the manipulated value of various pumps, and water inflow quality, and so on. Intelligent algorithms used for constructing process simulator are developed by considering multi-output polynomial radial basis function Neural Networks(pRBFNNs) as well as Fuzzy C-Means clustering and Particle Swarm Optimization. Here, the apexes of the antecedent gaussian functions of fuzzy rules are decided by C-means clustering algorithm and the apexes of the consequent part of fuzzy rules are learned by using back-propagation based on gradient decent method. Also, the parameters related to the fuzzy model are optimized by means of particle swarm optimization. The coefficients of the consequent polynomial of fuzzy rules and performance index are considered by the Least Square Estimation and Mean Squared Error. The descriptions of developed process simulator architecture and ensuing operation method are handled.

AWS자료 기반 SVR과 뉴로-퍼지 알고리즘 구현 호우주의보 가이던스 연구 (A Study on Heavy Rainfall Guidance Realized with the Aid of Neuro-Fuzzy and SVR Algorithm Using AWS Data)

  • 임승준;오성권;김용혁;이용희
    • 전기학회논문지
    • /
    • 제63권4호
    • /
    • pp.526-533
    • /
    • 2014
  • In this study, we introduce design methodology to develop a guidance for issuing heavy rainfall warning by using both RBFNNs(Radial basis function neural networks) and SVR(Support vector regression) model, and then carry out the comparative studies between two pattern classifiers. Individual classifiers are designed as architecture realized with the aid of optimization and pre-processing algorithm. Because the predictive performance of the existing heavy rainfall forecast system is commonly affected from diverse processing techniques of meteorological data, under-sampling method as the pre-processing method of input data is used, and also data discretization and feature extraction method for SVR and FCM clustering and PSO method for RBFNNs are exploited respectively. The observed data, AWS(Automatic weather wtation), supplied from KMA(korea meteorological administration), is used for training and testing of the proposed classifiers. The proposed classifiers offer the related information to issue a heavy rain warning in advance before 1 to 3 hours by using the selected meteorological data and the cumulated precipitation amount accumulated for 1 to 12 hours from AWS data. For performance evaluation of each classifier, ETS(Equitable Threat Score) method is used as standard verification method for predictive ability. Through the comparative studies of two classifiers, neuro-fuzzy method is effectively used for improved performance and to show stable predictive result of guidance to issue heavy rainfall warning.

부분방전 패턴인식을 위해 EMC센서를 이용한 최적화된 RBFNNs 분류기 설계 (Design of Optimized Radial Basis Function Neural Networks Classifier Using EMC Sensor for Partial Discharge Pattern Recognition)

  • 정병진;이승철;오성권
    • 전기학회논문지
    • /
    • 제66권9호
    • /
    • pp.1392-1401
    • /
    • 2017
  • In this study, the design methodology of pattern classification is introduced for avoiding faults through partial discharge occurring in the power facilities and local sites. In order to classify some partial discharge types according to the characteristics of each feature, the model is constructed by using the Radial Basis Function Neural Networks(RBFNNs) and Particle Swarm Optimization(PSO). In the input layer of the RBFNNs, the feature vector is searched and the dimension is reduced through Principal Component Analysis(PCA) and PSO. In the hidden layer, the fuzzy coefficients of the fuzzy clustering method(FCM) are tuned using PSO. Raw datasets for partial discharge are obtained through the Motor Insulation Monitoring System(MIMS) instrument using an Epoxy Mica Coupling(EMC) sensor. The preprocessed datasets for partial discharge are acquired through the Phase Resolved Partial Discharge Analysis(PRPDA) preprocessing algorithm to obtain partial discharge types such as void, corona, surface, and slot discharges. Also, when the amplitude size is considered as two types of both the maximum value and the average value in the process for extracting the preprocessed datasets, two different kinds of feature datasets are produced. In this study, the classification ratio between the proposed RBFNNs model and other classifiers is shown by using the two different kinds of feature datasets, and also we demonstrate the proposed model shows superiority from the viewpoint of classification performance.

KLAPS 재분석 자료를 이용한 진화최적화 RBFNNs 기반 호우특보 판별 모델 설계 (Design of Heavy Rain Advisory Decision Model Based on Optimized RBFNNs Using KLAPS Reanalysis Data)

  • 김현명;오성권;이용희
    • 한국지능시스템학회논문지
    • /
    • 제23권5호
    • /
    • pp.473-478
    • /
    • 2013
  • 본 논문에서는 KLAPS(Korea Local Analysis and Prediction System)의 재분석 자료를 이용하여 지능형 뉴로-퍼지 알고리즘 RBFNNs(Polynomial-based Radial Basis Function Neural Networks) 기반 호우특보 판별 모델을 개발한다. 기존의 호우예측 시스템들의 예측능력은 일반적으로 기상데이터의 가공 기법의 영향을 받는다. 본 연구에서는 이를 보완하기 위하여 기상데이터의 전처리를 통한 호우예측 방법을 소개한다. 기상 데이터 전처리 기법은 KLAPS 데이터를 기반으로 지점별 변환, 누적강수량 생성, 시계열 데이터 가공, 호우특보 추출 방식에 의하여 설계된다. 최종적으로, 향후 t(t=1,2,3) 시간 후 6시간 동안 누적강수량에 대해 예측하고 호우특보를 결정하기 위한 정보를 제공한다. 또한 다항식의 형태, 규칙의 개수, 퍼지화 계수와 같은 제안된 모델의 중요 파라미터는 최적화 기법인 차분 진화(Differential Evolution; DE)를 이용하여 최적화한다.

Synchronous DS-CDMA 시스템에서의 간략화된 RBF 다중사용자 수신기 (Simplified RBF Multiuser Receivers of Synchronous DS-CDMA Systems)

  • 고균병;이충용;강창언;홍대식
    • 한국통신학회논문지
    • /
    • 제28권5C호
    • /
    • pp.555-560
    • /
    • 2003
  • 본 논문에서는 Synchronous DS-CDMA(direct sequence-code division multiple access) 시스템에서 준 최적의 RBF(radial basis function) 수신기를 제안한다. 제안된 수신기는 병렬적인 RBF Network들이 결합된 형태를 갖으며, 각 RBF Network는 일반적인 RBF 수신기의 구조를 갖는다. 각각의 RBF Network는 다른 RBF Network에 할당된 사용자들에 의해 야기되는 간섭 성분으로 인해 성능이 저하된다. 따라서, 이러한 간섭 영향을 완화시킬 수 있는 병렬 간섭제거 기법(PIC)을 각 RBF Network들간에 적용한다. 본 논문에서는 제안된 수신기가 요구되는 RBF의 개수(RBF의 중심값)를 줄일 수 있는 구조를 갖고, 수신과정에서 하나의 정보열당 요구되는 연산량 또한 줄일 수 있는 구조임을 확인하였다. 그리고, AWGN 채널에서의 모의실험을 통해 일반적인 수신기보다 복잡도를 줄인 제안된 수신기가 최적의 수신기와 유사한 성능을 나타냄을 확인하였다. 또한, 제안된 수신기가 다양한 시스템 요구사항에 대처할 수 있음을 확인하였다.

기상레이더를 이용한 뉴로-퍼지 알고리즘 기반 에코 분류기 설계 (Design of Echo Classifier Based on Neuro-Fuzzy Algorithm Using Meteorological Radar Data)

  • 오성권;고준현
    • 전기학회논문지
    • /
    • 제63권5호
    • /
    • pp.676-682
    • /
    • 2014
  • In this paper, precipitation echo(PRE) and non-precipitaion echo(N-PRE)(including ground echo and clear echo) through weather radar data are identified with the aid of neuro-fuzzy algorithm. The accuracy of the radar information is lowered because meteorological radar data is mixed with the PRE and N-PRE. So this problem is resolved by using RBFNN and judgement module. Structure expression of weather radar data are analyzed in order to classify PRE and N-PRE. Input variables such as Standard deviation of reflectivity(SDZ), Vertical gradient of reflectivity(VGZ), Spin change(SPN), Frequency(FR), cumulation reflectivity during 1 hour(1hDZ), and cumulation reflectivity during 2 hour(2hDZ) are made by using weather radar data and then each characteristic of input variable is analyzed. Input data is built up from the selected input variables among these input variables, which have a critical effect on the classification between PRE and N-PRE. Echo judgment module is developed to do echo classification between PRE and N-PRE by using testing dataset. Polynomial-based radial basis function neural networks(RBFNNs) are used as neuro-fuzzy algorithm, and the proposed neuro-fuzzy echo pattern classifier is designed by combining RBFNN with echo judgement module. Finally, the results of the proposed classifier are compared with both CZ and DZ, as well as QC data, and analyzed from the view point of output performance.