• Title/Summary/Keyword: radial basis function neural network

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A Study on Speaker Recognition Algorithm Through Wire/Wireless Telephone (유무선 전화를 통한 화자인식 알고리즘에 관한 연구)

  • 김정호;정희석;강철호;김선희
    • The Journal of the Acoustical Society of Korea
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    • v.22 no.3
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    • pp.182-187
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    • 2003
  • In this thesis, we propose the algorithm to improve the performance of speaker verification that is mapping feature parameters by using RBF neural network. There is a big difference between wire vector region and wireless one which comes from the same speaker. For wire/wireless speakers model production, speaker verification system should distinguish the wire/wireless channel that based on speech recognition system. And the feature vector of untrained channel models is mapped to the feature vector(LPC Cepstrum) of trained channel model by using RBF neural network. As a simulation result, the proposed algorithm makes 0.6%∼10.5% performance improvement compared to conventional method such as cepstral mean subtraction.

Design of RBF Neural Networks Based on Recursive Weighted Least Square Estimation for Processing Massive Meteorological Radar Data and Its Application (방대한 기상 레이더 데이터의 원할한 처리를 위한 순환 가중최소자승법 기반 RBF 뉴럴 네트워크 설계 및 응용)

  • Kang, Jeon-Seong;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.64 no.1
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    • pp.99-106
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    • 2015
  • In this study, we propose Radial basis function Neural Network(RBFNN) using Recursive Weighted Least Square Estimation(RWLSE) to effectively deal with big data class meteorological radar data. In the condition part of the RBFNN, Fuzzy C-Means(FCM) clustering is used to obtain fitness values taking into account characteristics of input data, and connection weights are defined as linear polynomial function in the conclusion part. The coefficients of the polynomial function are estimated by using RWLSE in order to cope with big data. As recursive learning technique, RWLSE which is based on WLSE is carried out to efficiently process big data. This study is experimented with both widely used some Machine Learning (ML) dataset and big data obtained from meteorological radar to evaluate the performance of the proposed classifier. The meteorological radar data as big data consists of precipitation echo and non-precipitation echo, and the proposed classifier is used to efficiently classify these echoes.

Decentralized Robust Adaptive Neural Network Control for Electrically Driven Robot Manipulators with Bounded Input Voltages (제한된 입력 전압을 갖는 전기 구동 로봇 매니퓰레이터에 대한 분산 강인 적응 신경망 제어)

  • Shin, Jin-Ho;Kim, Won-Ho
    • Transactions of the Korean Society for Noise and Vibration Engineering
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    • v.25 no.11
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    • pp.753-763
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    • 2015
  • This paper proposes a decentralized robust adaptive neural network control scheme using multiple radial basis function neural networks for electrically driven robot manipulators with bounded input voltages in the presence of uncertainties. The proposed controller considers both robot link dynamics and actuator dynamics. Practically, the controller gain coefficients applied at each joint may be nonlinear time-varying and the input voltage at each joint is saturated. The proposed robot controller overcomes the various uncertainties and the input voltage saturation problem. The proposed controller does not require any robot and actuator parameters. The adaptation laws of the proposed controller are derived by using the Lyapunov stability analysis and the stability of the closed-loop control system is guaranteed. The validity and robustness of the proposed control scheme are verified through simulation results.

Design of Fingerprints Identification Based on RBFNN Using Image Processing Techniques (영상처리 기법을 통한 RBFNN 패턴 분류기 기반 개선된 지문인식 시스템 설계)

  • Bae, Jong-Soo;Oh, Sung-Kwun;Kim, Hyun-Ki
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.65 no.6
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    • pp.1060-1069
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    • 2016
  • In this paper, we introduce the fingerprint recognition system based on Radial Basis Function Neural Network(RBFNN). Fingerprints are classified as four types(Whole, Arch, Right roof, Left roof). The preprocessing methods such as fast fourier transform, normalization, calculation of ridge's direction, filtering with gabor filter, binarization and rotation algorithm, are used in order to extract the features on fingerprint images and then those features are considered as the inputs of the network. RBFNN uses Fuzzy C-Means(FCM) clustering in the hidden layer and polynomial functions such as linear, quadratic, and modified quadratic are defined as connection weights of the network. Particle Swarm Optimization (PSO) algorithm optimizes a number of essential parameters needed to improve the accuracy of RBFNN. Those optimized parameters include the number of clusters and the fuzzification coefficient used in the FCM algorithm, and the orders of polynomial of networks. The performance evaluation of the proposed fingerprint recognition system is illustrated with the use of fingerprint data sets that are collected through Anguli program.

Design & Implementation of Pedestrian Detection System Using HOG-PCA Based pRBFNNs Pattern Classifier (HOG-PCA기반 pRBFNNs 패턴분류기를 이용한 보행자 검출 시스템의 설계 및 구현)

  • Kim, Jin-Yul;Park, Chan-Jun;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.64 no.7
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    • pp.1064-1073
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    • 2015
  • In this study, we introduce the pedestrian detection system by using the feature of HOG-PCA and RBFNNs pattern classifier. HOG(Histogram of Oriented Gradient) feature is extracted from input image to identify and recognize a object. And a dimension is reduced for improving performance as well as processing speed by using PCA which is a typical dimensional reduction algorithm. So, the feature of HOG-PCA through the dimensional reduction by using PCA leads to the improvement of the detection rate. FCM clustering algorithm is used instead of gaussian function to apply the characteristic of input data as well and connection weight is used by polynomial expression such as constant, linear, quadratic and modified quadratic. Finally, INRIA person database known as one of the benchmark dataset used for pedestrian detection is applied for the performance evaluation of the proposed classifier. The experimental result of the proposed classifier are compared with those studied by Dalal.

Optimization of FCM-based Radial Basis Function Neural Network using PSO (PSO를 이용한 FCM 기반 RBF 뉴럴네트워크의 최적화)

  • Choi, Jeoung-Nae;Oh, Sung-Kwun;Kim, Hyun-Ki
    • Proceedings of the KIEE Conference
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    • pp.1857-1858
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    • 2008
  • 본 논문에서는 FCM 기반 RBF 뉴럴네트워크(FCM-RBFNN) 구조를 제안하고 PSO를 이용한 FCM-RBFNN의 구조 및 파라미터의 최적화 방법을 제시한다. 클러스터링 알고리즘은 퍼지 뉴럴 네트워크에서 멤버쉽함수의 중심점과 반경 등을 결정하는 학습에 일반적으로 사용된다. 제안된 FCM-RBFNN서는 방사기저함수로써 가우시안, 삼각형 타입 등의 정해진 형태를 사용하지 않고 데이터들 사이의 거리에 관계된 계산을 수행하는 FCM에 의해 결정된다. 기존의 RBFNN에서 후반부는 상수형태로써 방사기저함수의 선형결합으로써 표현되는 반면에 제안된 FCM-RBFNN의 후반부는 상수형, 선형, 2차식 등의 다양한 형태의 다항식으로 표현될 수 있으며 다항식의 계수는 WLSE를 이용하여 추정한다. FCM 기반 RBF 뉴럴 네트워크의 성능은 퍼지규칙의 수, 후반부 다항식의 차수 FCM의 퍼지화 계수에 의하여 결정기 때문에 FCM-RBFNN의 구조와 파라미터의 최적화가 요구된다. 본 논문에서는 PSO를 이용하여 FCM-RBFNN의 구조에 관련된 퍼지 규칙의 수, 후반부 다항식의 차수와 파라미터에 관련된 퍼지화 계수를 최적화한다. 또한 후반부 다항식의 계수는 WLSE를 사용하여 추정한다.

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Design of Pedestrian Detection System Based on Optimized pRBFNNs Pattern Classifier Using HOG Features and PCA (PCA와 HOG특징을 이용한 최적의 pRBFNNs 패턴분류기 기반 보행자 검출 시스템의 설계)

  • Lim, Myeoung-Ho;Park, Chan-Jun;Oh, Sung-Kwun;Kim, Jin-Yul
    • Proceedings of the KIEE Conference
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    • pp.1345-1346
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    • 2015
  • 본 논문에서는 보행자 및 배경 이미지로부터 HOG-PCA 특징을 추출하고 다항식 기반 RBFNNs(Radial Basis Function Neural Network) 패턴분류기과 최적화 알고리즘을 이용하여 보행자를 검출하는 시스템 설계를 제안한다. 입력 영상으로부터 보행자를 검출하기 위해 전처리 과정에서 HOG(Histogram of oriented gradient) 알고리즘을 통해 특징을 추출한다. 추출된 특징은 고차원이므로 패턴분류기 분류 시 많은 연산과 처리속도가 따른다. 이를 개선하고자 PCA (Principal Components Analysis)을 사용하여 저차원으로의 차원 축소한다. 본 논문에서 제안하는 분류기는 pRBFNNs 패턴분류기의 효율적인 학습을 위해 최적화 알고리즘인 PSO(Particle Swarm Optimization)을 사용하여 구조 및 파라미터를 최적화시켜 모델의 성능을 향상시킨다. 사용된 데이터로는 보행자 검출에 널리 사용되는 INRIA2005_person data set에서 보행자와 배경 영상을 각각 1200장을 학습 데이터, 검증 데이터로 구성하여 분류기를 설계하고 테스트 이미지를 설계된 최적의 분류기를 이용하여 보행자를 검출하고 검출률을 확인한다.

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

  • Kim, Jin-Yul;Kim, Jong-Bum;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.66 no.4
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    • pp.672-681
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    • 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.

The Adaptive Backstepping Controller of RBF Neural Network Which is Designed on the Basis of the Error (오차를 기반으로한 RBF 신경회로망 적응 백스테핑 제어기 설계)

  • Kim, Hyun Woo;Yoon, Yook Hyun;Jeong, Jin Han;Park, Jahng Hyon
    • Journal of the Korean Society for Precision Engineering
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    • v.34 no.2
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    • pp.125-131
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    • 2017
  • 2-Axis Pan and Tilt Motion Platform, a complex multivariate non-linear system, may incur any disturbance, thus requiring system controller with robustness against various disturbances. In this study, we designed an adaptive backstepping compensated controller by estimating the disturbance and error using the Radial Basis Function Neural Network (RBF NN). In this process, Uniformly Ultimately Bounded (UUB) was demonstrated via Lyapunov and stability was confirmed. By generating progressive disturbance to the irregular frequency and amplitude changes, it was verified for various environmental disturbances. In addition, by setting the RBF NN input vector to the minimum, the estimated disturbance compensation process was analyzed. Only two input vectors facilitated compensatory function of RBF NN via estimating the modeling and control error values as well as irregular disturbance; the application of the process resulted in improved backstepping controller performance that was confirmed through simulation.

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

  • Oh, Sung-Kwun;Na, Hyun-Suk;Kim, Wook-Dong
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.60 no.12
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    • pp.2352-2360
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    • 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.