• Title/Summary/Keyword: radial basis function networks

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The Study of Neural Networks Using Orthogonal function System in Hidden-Layer (직교함수를 은닉층에 지닌 신경회로망에 대한 연구)

  • 권성훈;최용준;이정훈;유석용;엄기환;손동설
    • Proceedings of the IEEK Conference
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    • 1999.06a
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    • pp.482-485
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    • 1999
  • In this paper we proposed a heterogeneous hidden layer consisting of both sigmoid functions and RBFs(Radial Basis Function) in multi-layered neural networks. Focusing on the orthogonal relationship between the sigmoid function and its derivative, a derived RBF that is a derivative of the sigmoid function is used as the RBF in the neural network. so the proposed neural network is called ONN(Orthogonal Neural Network). Identification results using a nonlinear function confirm both the ONN's feasibility and characteristics by comparing with those obtained using a conventional neural network which has sigmoid function or RBF in hidden layer

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The Study of Neural Networks Using Orthogonal Function System (직교함수를 사용한 신경회로망에 대한 연구)

  • 권성훈;최용준;이정훈;손동설;엄기환
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 1999.11a
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    • pp.214-217
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    • 1999
  • In this paper we proposed a heterogeneous hidden layer consisting of both sigmoid functions and RBFs(Radial Basis Function) in multi-layered neural networks. Focusing on the orthogonal relationship between the sigmoid function and its derivative, a derived RBF that is a derivative of the sigmoid function is used as the RBF in the neural network. so the proposed neural network is called ONN's feasibility Neural Network). Identification results using a nonlinear. function confirm both the ONN's feasibility and characteristics by comparing with those obtained using a conventional neural network which has sigmoid function or RBF in hidden layer.

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Design of Face Recognition algorithm Using PCA&LDA combined for Data Pre-Processing and Polynomial-based RBF Neural Networks (PCA와 LDA를 결합한 데이터 전 처리와 다항식 기반 RBFNNs을 이용한 얼굴 인식 알고리즘 설계)

  • Oh, Sung-Kwun;Yoo, Sung-Hoon
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.61 no.5
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    • pp.744-752
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    • 2012
  • In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as an one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problems. In data preprocessing part, Principal Component Analysis(PCA) which is generally used in face recognition, which is useful to express some classes using reduction, since it is effective to maintain the rate of recognition and to reduce the amount of data at the same time. However, because of there of the whole face image, it can not guarantee the detection rate about the change of viewpoint and whole image. Thus, to compensate for the defects, Linear Discriminant Analysis(LDA) is used to enhance the separation of different classes. In this paper, we combine the PCA&LDA algorithm and design the optimized pRBFNNs for recognition module. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as two kinds of polynomials such as constant, and linear. The coefficients of connection weight identified with back-propagation using gradient descent method. The output of the pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to face image(ex Yale, AT&T) datasets and then demonstrated from the viewpoint of the output performance and recognition rate.

Visual servoing of robot manipulator by fuzzy membership function based neural network (퍼지 신경망에 의한 로보트의 시각구동)

  • 김태원;서일홍;조영조
    • 제어로봇시스템학회:학술대회논문집
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    • 1992.10a
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    • pp.874-879
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    • 1992
  • It is shown that there exists a nonlinear mappping which transforms features and their changes to the desired camera motion without measurement of the relative distance between the camera and the part, and the nonlinear mapping can eliminate several difficulties encountered when using the inverse of the feature Jacobian as in the usual feature-based visual feedback controls. And instead of analytically deriving the closed form of such a nonlinear mapping, a fuzzy membership function (FMF) based neural network is then proposed to approximate the nonlinear mapping, where the structure of proposed networks is similar to that of radial basis function neural network which is known to be very useful in function approximations. The proposed FMF network is trained to be capable of tracking moving parts in the whole work space along the line of sight. For the effective implementation of proposed IMF networks, an image feature selection processing is investigated, and required fuzzy membership functions are designed. Finally, several numerical examples are illustrated to show the validities of our proposed visual servoing method.

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Evolutionary Learning Algorithm fo r Projection Neural NEtworks (투영신경회로망의 훈련을 위한 진화학습기법)

  • 황민웅;최진영
    • Journal of the Korean Institute of Intelligent Systems
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    • v.7 no.4
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    • pp.74-81
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    • 1997
  • This paper proposes an evolutionary learning algorithm to discipline the projection neural nctworks (PNNs) with special type of hidden nodes which can activate radial basis functions as well as sigmoid functions. The proposed algorithm not only trains the parameters and the connection weights hut also c~ptimizes the network structure. Through the structure optimization, the number of hidden node:; necessary to represent a given target function is determined and the role of each hidden node is decided whether it activates a radial basis function or a sigmoid function. To apply the algorithm, PNN is realized by a self-organizing genotype representation with a linked list data structure. Simulations show that the algorithm can build the PNN with less hidden nodes than thc existing learning algorithm using error hack propagation(EE3P) and network growing strategy.

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Inverse Estimation of Surface Temperature Using the RBF Network (RBF Network 를 이용한 표면온도 역추정에 관한 연구)

  • Jung, Bup-Sung;Lee, Woo-Il
    • Proceedings of the KSME Conference
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    • 2004.04a
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    • pp.1183-1188
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    • 2004
  • The inverse heat conduction problem (IHCP) is a problem of estimating boundary condition from temperature measurement at one or more interior points. Neural networks are general information processing systems inspired by the connectionist theory of human brain. By properly training the network by the learning rule, the neural network method can handle many non-linear or other complex problems. In this work, neural network is applied to complicated inverse heat conduction problems. Efficiency of the procedure is enhanced by incorporating the radial basis functions (RBF). The RBF is trained faster than other neural network and can find smooth solution. In order to demonstrate the effectiveness of the current scheme, a typical one-dimensional IHCP is considered. At one surface, the temperature as well as the heat flux is known. The unknown temperature of interest is estimated on the other side of the slab. The results from the proposed method based on RBF neural network are compared with the conventional method.

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The Design of Optimized Type-2 Fuzzy Neural Networks and Its Application (최적 Type-2 퍼지신경회로망 설계와 응용)

  • Kim, Gil-Sung;Ahn, Ihn-Seok;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.58 no.8
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    • pp.1615-1623
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    • 2009
  • In order to develop reliable on-site partial discharge (PD) pattern recognition algorithm, we introduce Type-2 Fuzzy Neural Networks (T2FNNs) optimized by means of Particle Swarm Optimization(PSO). T2FNNs exploit Type-2 fuzzy sets which have a characteristic of robustness in the diverse area of intelligence systems. Considering the on-site situation where it is not easy to obtain voltage phases to be used for PRPDA (Phase Resolved Partial Discharge Analysis), the PD data sets measured in the laboratory were artificially changed into data sets with shifted voltage phases and added noise in order to test the proposed algorithm. Also, the results obtained by the proposed algorithm were compared with that of conventional Neural Networks(NNs) as well as the existing Radial Basis Function Neural Networks (RBFNNs). The T2FNNs proposed in this study were appeared to have better performance when compared to conventional NNs and RBFNNs.

Feature Vector Extraction and Automatic Classification for Transient SONAR Signals using Wavelet Theory and Neural Networks (Wavelet 이론과 신경회로망을 이용한 천이 수중 신호의 특징벡타 추출 및 자동 식별)

  • Yang, Seung-Chul;Nam, Sang-Won;Jung, Yong-Min;Cho, Yong-Soo;Oh, Won-Tcheon
    • The Journal of the Acoustical Society of Korea
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    • v.14 no.3
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    • pp.71-81
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    • 1995
  • In this paper, feature vector extraction methods and classification algorithms for the automatic classification of transient signals in underwater are discussed. A feature vector extraction method using wavelet transform, which shows good performance with small number of coefficients, is proposed and compared with the existing classical methods. For the automatic classification, artificial neural networks such as multilayer perceptron (MLP), radial basis function (RBF), and MLP-Class are utilized, where those neural networks as well as extracted feature vectors are combined to improve the performance and reliability of the proposed algorithm. It is confirmed by computer simulation with Traco's standard transient data set I and simulated data that the proposed feature vector extraction method and classification algorithm perform well, assuming that the energy of a given transient signal is sufficiently larger than that of a ambient noise, that there are the finite number of noise sources, and that there does not exist noise sources more than two simultaneously.

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An On-line Construction of Generalized RBF Networks for System Modeling (시스템 모델링을 위한 일반화된 RBF 신경회로망의 온라인 구성)

  • Kwon, Oh-Shin;Kim, Hyong-Suk;Choi, Jong-Soo
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.37 no.1
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    • pp.32-42
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    • 2000
  • This paper presents an on-line learning algorithm for sequential construction of generalized radial basis function networks (GRBFNs) to model nonlinear systems from empirical data. The GRBFN, an extended from of standard radial basis function (RBF) networks with constant weights, is an architecture capable of representing nonlinear systems by smoothly integrating local linear models. The proposed learning algorithm has a two-stage learning scheme that performs both structure learning and parameter learning. The structure learning stage constructs the GRBFN model using two construction criteria, based on both training error criterion and Mahalanobis distance criterion, to assign new hidden units and the linear local models for given empirical training data. In the parameter learning stage the network parameters are updated using the gradient descent rule. To evaluate the modeling performance of the proposed algorithm, simulations and their results applied to two well-known benchmarks are discussed.

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Design of Fuzzy Clustering-based Neural Networks Classifier for Sorting Black Plastics with the Aid of Raman Spectroscopy (라만분광법에 의한 흑색 플라스틱 선별을 위한 퍼지 클러스터링기반 신경회로망 분류기 설계)

  • Kim, Eun-Hu;Bae, Jong-Soo;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.66 no.7
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    • pp.1131-1140
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    • 2017
  • This study is concerned with a design methodology of optimized fuzzy clustering-based neural network classifier for classifying black plastic. Since the amount of waste plastic is increased every year, the technique for recycling waste plastic is getting more attention. The proposed classifier is on a basis of architecture of radial basis function neural network. The hidden layer of the proposed classifier is composed to FCM clustering instead of activation functions, while connection weights are formed as the linear functions and their coefficients are estimated by the local least squares estimator (LLSE)-based learning. Because the raw dataset collected from Raman spectroscopy include high-dimensional variables over about three thousands, principal component analysis(PCA) is applied for the dimensional reduction. In addition, artificial bee colony(ABC), which is one of the evolutionary algorithm, is used in order to identify the architecture and parameters of the proposed network. In experiment, the proposed classifier sorts the three kinds of plastics which is the most largely discharged in the real world. The effectiveness of the proposed classifier is proved through a comparison of performance between dataset obtained from chemical analysis and entire dataset extracted directly from Raman spectroscopy.