• 제목/요약/키워드: radial basis function networks

검색결과 183건 처리시간 0.033초

차분 진화 알고리즘 기반 방사형 기저 함수 신경회로망 분류기의 최적화 방법 (Optimization Method of Differential Evolution-based Radial Basis Function Neural Networks)

  • 마창민;오성권
    • 대한전기학회:학술대회논문집
    • /
    • 대한전기학회 2011년도 제42회 하계학술대회
    • /
    • pp.1962-1963
    • /
    • 2011
  • 본 연구에서는 패턴분류를 위해 최적화된 방사형 기저 함수 신경회로망(Radial Basis Function Neural Networks) 분류기를 제안한다. RBFNN은 입력층, 은닉층, 출력층의 3층 구조로 되어 있으며 Multi Dimension, Predictive ability, Robustness한 특징이 있다. RBFNN의 은닉층에는 기존의 활성함수가 아닌 Fuzzy C-means 클러스터링 알고리즘을 사용하여 입력 데이터의 특성을 고려한 적합도를 사용하였다. RBFNN은 은닉층의 노드수와 FCM 클러스터링의 퍼지화 계수, 연결가중치의 다항식 타입이 모델의 성능의 향상에 영향을 미치기 때문에 최적화가 필요하며 본 논문에서는 Differential Evolution(DE) 알고리즘을 사용하여 모델의 구조 및 파라미터를 최적화시켜 모델의 성능을 향상시켰다. 제안된 모델을 평가하기 위해 패턴분류에 많이 사용되는 Iris 데이터와 Wine 데이터를 이용하였다.

  • PDF

Relations among the multidimensional linear interpolation fuzzy reasoning , and neural networks

  • Om, Kyong-Sik;Kim, Hee-Chan;Byoung-Goo
    • 한국지능시스템학회:학술대회논문집
    • /
    • 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
    • /
    • pp.562-567
    • /
    • 1998
  • This paper examined the relations among the multidimensional linear interpolation(MDI) and fuzzy reasoning , and neural networks, and showed that an showed that an MDI is a special form of Tsukamoto's fuzzy reasoning and regularization networks in the perspective of fuzzy reasoning and neural networks, respectively. For this purposes, we proposed a special Tsukamoto's membership (STM) systemand triangular basis function (TBF) networks, Also we verified the condition when our proposed TBF becomes a well-known radial basis function (RBF).

  • PDF

패턴분류를 위한 통계적 RBF 모델 (Statistical Radial Basis Function Model for Pattern Classification)

  • 최준혁;임기욱;이정현
    • 전자공학회논문지CI
    • /
    • 제41권1호
    • /
    • pp.1-8
    • /
    • 2004
  • 인터넷의 발달과 데이터베이스의 구축이 보편화됨에 따라 막대한 양의 데이터 속에서 의사 결정에 필요한 지식을 찾아내는 작업은 결코 쉬운 일이 아니다 본 논문에서는 대규모 데이터의 효율적인 분석을 위하여 지식의 탐사 이전에 데이터에 대한 축소 작업을 수행하기 위한 효과적인 차원 축소 전략에 의한 패턴분류 기법을 제안한다. 이를 위해 본 논문에서는 통계적학습 모형인 Support Vector Machine의 VC-dimension에 기반한 RBF 신경망 모형을 제안한다. 기존의 RBF 신경망 모형은 주로 퍼셉트론 모형의 전처리 작업만을 수행하지만 제안하는 신경망 모형은 VD-dimension과 연계한 독자적으로 데이터를 분석할 수 있는 능력을 갖춘 모형을 구축하고 이를 바탕으로 개체들을 정확한 레이블로 분류한다. 기계 학습 데이터를 이용하여 본 논문에서 제안하는 모형의 성능을 비교 평가한 결과 기존의 여러 분류 알고리즘에 비해 우수한 성능을 보임이 실험을 통해 확인되었다.

RBF 신경망을 이용한 비선형 근사 (Nonlinear Approximations Using RBF Neural Networks)

  • 박주영
    • 한국지능시스템학회논문지
    • /
    • 제6권2호
    • /
    • pp.26-35
    • /
    • 1996
  • In this paper, some fundamental problems concerning RBF(radial-basis-function) networks and approximation of functions are addressed. First, a comprehensive introduction to RBF networks is given with typical RBF networks classified into three classes. Next, sharp conditions are given under which continuous functions of a finite number of real variables can be approximated arbitrarily well by a certain class of RBF networks. Finally, a related result is given concerning the representation of functions in the form of distributed RBF networks.

  • PDF

Implementation of Elbow Method to improve the Gases Classification Performance based on the RBFN-NSG Algorithm

  • Jeon, Jin-Young;Choi, Jang-Sik;Byun, Hyung-Gi
    • 센서학회지
    • /
    • 제25권6호
    • /
    • pp.431-434
    • /
    • 2016
  • Currently, the radial basis function network (RBFN) and various other neural networks are employed to classify gases using chemical sensors arrays, and their performance is steadily improving. In particular, the identification performance of the RBFN algorithm is being improved by optimizing parameters such as the center, width, and weight, and improved algorithms such as the radial basis function network-stochastic gradient (RBFN-SG) and radial basis function network-normalized stochastic gradient (RBFN-NSG) have been announced. In this study, we optimized the number of centers, which is one of the parameters of the RBFN-NSG algorithm, and observed the change in the identification performance. For the experiment, repeated measurement data of 8 samples were used, and the elbow method was applied to determine the optimal number of centers for each sample of input data. The experiment was carried out in two cases(the only one center per sample and the optimal number of centers obtained by elbow method), and the experimental results were compared using the mean square error (MSE). From the results of the experiments, we observed that the case having an optimal number of centers, obtained using the elbow method, showed a better identification performance than that without any optimization.

주성분 분석법과 선형판별 분석법을 이용한 최적화된 방사형 기저 함수 신경회로망 분류기의 설계 (Design of Optimized Radial Basis Function Neural Networks Classifier with the Aid of Principal Component Analysis and Linear Discriminant Analysis)

  • 김욱동;오성권
    • 한국지능시스템학회논문지
    • /
    • 제22권6호
    • /
    • pp.735-740
    • /
    • 2012
  • 본 연구에서는 주성분 분석법 및 선형 판별 분석법을 이용한 다항식 방사형 기저 함수 신경회로망 분류기의 설계 방법론을 소개한다. 주성분 분석법과 선형판별 분석법을 사용하여 주어진 데이터의 정보 손실을 최소화한 특징데이터를 생성하고 이를 다항식 방사형 기저함수 신경회로망의 입력데이터로 사용한다. 방사형 기저 함수 신경회로망의 은닉층은 FCM 클러스터링 알고리즘으로 구성되며 연결가중치는 1차 선형식을 사용하였다. 최적의 분류기 설계를 위해서 최근에 제안된 Artificial Bee Colony(ABC) 최적화 알고리즘을 사용하여 구조 및 파라미터를 동조하였다. ABC 알고리즘을 통해 주성분 분석법과 선형판별 분석법의 고유벡터의 수 및 FCM 클러스터링 알고리즘의 퍼지화 계수등의 파라미터를 동조한다. 제안된 분류기는 대표적인 Machine Learning(ML) 데이터를 사용하여 성능을 평가하며 기존 분류기와 성능을 비교한다.

An Elliptical Basis Function Network for Classification of Remote-Sensing Images

  • Luo, Jian-Cheng;Chen, Qiu-Xiao;Zheng, Jiang;Leung, Yee;Ma, Jiang-Hong
    • 대한원격탐사학회:학술대회논문집
    • /
    • 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
    • /
    • pp.1326-1328
    • /
    • 2003
  • An elliptical basis function (EBF) network is proposed in this study for the classification of remotely sensed images. Though similar in structure, the EBF network differs from the well-known radial basis function (RBF) network by incorporating full covariance matrices and uses the expectation-maximization (EM) algorithm to estimate the basis functions. Since remotely sensed data often take on mixture -density distributions in the feature space, the proposed network not only possesses the advantage of the RBF mechanism but also utilizes the EM algorithm to compute the maximum likelihood estimates of the mean vectors and covariance matrices of a Gaussian mixture distribution in the training phase. Experimental results show that the EM-based EBF network is faster in training, more accurate, and simpler in structure.

  • PDF

포즈 추정 기반 얼굴 인식 시스템 설계 : 포즈 추정 알고리즘 비교 연구 (Design of Face Recognition System Based on Pose Estimation : Comparative Studies of Pose Estimation Algorithms)

  • 김진율;김종범;오성권
    • 전기학회논문지
    • /
    • 제66권4호
    • /
    • pp.672-681
    • /
    • 2017
  • This paper is concerned with the design methodology of face recognition system based on pose estimation. In 2-dimensional face recognition, the variations of facial pose cause the deterioration of recognition performance because object recognition is carried out by using brightness of each pixel on image. To alleviate such problem, the proposed face recognition system deals with Learning Vector Quantizatioin(LVQ) or K-Nearest Neighbor(K-NN) to estimate facial pose on image and then the images obtained from LVQ or K-NN are used as the inputs of networks such as Convolution Neural Networks(CNNs) and Radial Basis Function Neural Networks(RBFNNs). The effectiveness and efficiency of the post estimation using LVQ and K-NN as well as face recognition rate using CNNs and RBFNNs are discussed through experiments carried out by using ICPR and CMU PIE databases.

PCA와 입자 군집 최적화 알고리즘을 이용한 얼굴이미지에서 특징선택에 관한 연구 (A Study on Feature Selection in Face Image Using Principal Component Analysis and Particle Swarm Optimization Algorithm)

  • 김웅기;오성권;김현기
    • 전기학회논문지
    • /
    • 제58권12호
    • /
    • pp.2511-2519
    • /
    • 2009
  • In this paper, we introduce the methodological system design via feature selection using Principal Component Analysis and Particle Swarm Optimization algorithms. The overall methodological system design comes from three kinds of modules such as preprocessing module, feature extraction module, and recognition module. First, Histogram equalization enhance the quality of image by exploiting contrast effect based on the normalized function generated from histogram distribution values of 2D face image. Secondly, PCA extracts feature vectors to be used for face recognition by using eigenvalues and eigenvectors obtained from covariance matrix. Finally the feature selection for face recognition among the entire feature vectors is considered by means of the Particle Swarm Optimization. The optimized Polynomial-based Radial Basis Function Neural Networks are used to evaluate the face recognition performance. This study shows that the proposed methodological system design is effective to the analysis of preferred face recognition.

최적화된 pRBF 뉴럴 네트워크에 의한 정적 상황 인지 시스템에 관한 연구 (A Study on Static Situation Awareness System with the Aid of Optimized Polynomial Radial Basis Function Neural Networks)

  • 오성권;나현석;김욱동
    • 전기학회논문지
    • /
    • 제60권12호
    • /
    • pp.2352-2360
    • /
    • 2011
  • In this paper, we introduce a comprehensive design methodology of Radial Basis Function Neural Networks (RBFNN) that is based on mechanism of clustering and optimization algorithm. We can divide some clusters based on similarity of input dataset by using clustering algorithm. As a result, the number of clusters is equal to the number of nodes in the hidden layer. Moreover, the centers of each cluster are used into the centers of each receptive field in the hidden layer. In this study, we have applied Fuzzy-C Means(FCM) and K-Means(KM) clustering algorithm, respectively and compared between them. The weight connections of model are expanded into the type of polynomial functions such as linear and quadratic. In this reason, the output of model consists of relation between input and output. In order to get the optimal structure and better performance, Particle Swarm Optimization(PSO) is used. We can obtain optimized parameters such as both the number of clusters and the polynomial order of weights connection through structural optimization as well as the widths of receptive fields through parametric optimization. To evaluate the performance of proposed model, NXT equipment offered by National Instrument(NI) is exploited. The situation awareness system-related intelligent model was built up by the experimental dataset of distance information measured between object and diverse sensor such as sound sensor, light sensor, and ultrasonic sensor of NXT equipment.