• The Transactions of The Korean Institute of Electrical Engineers
• /
• 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.

• Journal of the Korean Institute of Intelligent Systems
• /
• 제20권1호
• /
• pp.101-108
• /
• 2010

In this paper, we proposed a new lazy classifier with fuzzy k-nearest neighbors approach and feature selection which is based on reconstruction error. Reconstruction error is the performance index for locally linear reconstruction. When a new query point is given, fuzzy k-nearest neighbors approach defines the local area where the local classifier is available and assigns the weighting values to the data patterns which are involved within the local area. After defining the local area and assigning the weighting value, the feature selection is carried out to reduce the dimension of the feature space. When some features are selected in terms of the reconstruction error, the local classifier which is a sort of polynomial is developed using weighted least square estimation. In addition, the experimental application covers a comparative analysis including several previously commonly encountered methods such as standard neural networks, support vector machine, linear discriminant analysis, and C4.5 trees.

• Journal of Institute of Control, Robotics and Systems
• /
• 제16권12호
• /
• pp.1150-1158
• /
• 2010

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as 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 problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. 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 three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of 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 the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.

• Journal of the Earthquake Engineering Society of Korea
• /
• 제3권3호
• /
• pp.63-74
• /
• 1999

A sliding mode fuzzy control (SMFC) algorithm is presented for vibration of large structures. Rule-base of the fuzzy inference engine is constructed based on the sliding mode control, which is one of the nonlinear control algorithms. Fuzziness of the controller makes the control system robust against the uncertainties in the system parameters and the input excitation. Non-linearity of the control rule makes the controller more effective than linear controllers. Design procedure based on the present fuzzy control is more convenient than those of the conventional algorithms based on complex mathematical analysis, such as linear quadratic regulator and sliding mode control(SMC). Robustness of presented controller is illustrated by examining the loop transfer function. For verification of the present algorithm, a numerical study is carried out on the benchmark problem initiated by the ASCE Committee on Structural Control. To achieve a high level of realism, various aspects are considered such as actuator-structure interaction, modeling error, sensor noise, actuator time delay, precision of the A/D and D/A converters, magnitude of control force, and order of control model. Performance of the SMFC is examined in comparison with those of other control algorithms such as $H_{mixed 2/{\infty}}$ optimal polynomial control, neural networks control, and SMC, which were reported by other researchers. The results indicate that the present SMFC is an efficient and attractive control method, since the vibration responses of the structure can be reduced very effectively and the design procedure is simple and convenient.

• Journal of the Korean Institute of Intelligent Systems
• /
• 제25권1호
• /
• pp.91-96
• /
• 2015

In this study, we introduce robust face recognition system with illumination variation realized with the aid of CT preprocessing method. As preprocessing algorithm, Census Transform(CT) algorithm is used to extract locally facial features under unilluminated condition. The dimension reduction of the preprocessed data is carried out by using $(2D)^2$PCA which is the extended type of PCA. Feature data extracted through dimension algorithm is used as the inputs of proposed radial basis function neural networks. The hidden layer of the radial basis function neural networks(RBFNN) is built up by fuzzy c-means(FCM) clustering algorithm and the connection weights of the networks are described as the coefficients of linear polynomial function. The essential design parameters (including the number of inputs and fuzzification coefficient) of the proposed networks are optimized by means of artificial bee colony(ABC) algorithm. This study is experimented with both Yale Face database B and CMU PIE database to evaluate the performance of the proposed system.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• 제65권6호
• /
• pp.1060-1069
• /
• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 한국퍼지및지능시스템학회 2005년도 추계학술대회 학술발표 논문집 제15권 제2호
• /
• pp.367-370
• /
• 2005

본 논문에서는 정보 입자화와 유전자 알고리즘을 기반으로 최적 퍼지 다항식 뉴럴네트워크를 제안하고, 유전자 알고리즘을 사용하여 종합적인 설계방법을 개발한다. 제안된 모델은 기존의 진화론적 퍼지 다항식 뉴럴네트워크의 구조를 정보입자화를 통해 좀 더 빠르게 최적의 해공간에 접근시키는데 그 목적이 있다. 퍼지 관계기반 다항식 뉴럴네트워크는 퍼지 다항식 뉴론이 기초가 되어 가능한 구조적이고 요소적으로 모델의 성능을 향상 시켜준다. 퍼지 다항식 뉴런의 최적 구조를 위해 유전자 알고리즘을 이용하여 입력변수의 수와 후반부 다항식의 차수 입력변수 수에 따른 입력변수 그리고 멤버쉽 함수의 수를 동조한다. 여기서, 클러스터링의 하나의 방법인 HCM에 의해 퍼지 규칙 각각의 전반부와 후반부에 데이터 중심값을 이용하여 다항식함수의 파라미터값을 결정한다. 제안된 유전론적 퍼지 관계 다항식 뉴럴네트워크의 성능평가는 기존 퍼지 모델링에서 이용된 표준 데이터를 활용하여 평가한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 한국퍼지및지능시스템학회 2004년도 춘계학술대회 학술발표 논문집 제14권 제1호
• /
• pp.303-306
• /
• 2004

기존의 퍼지 규칙에 기반을 둔 퍼지 다항식 뉴론(FPN)들로 구성된 SOFPNN은 데이터 수가 적고 비선형 요소가 많은 시스템에 대한 체계적이고 효율적인 최적 모델 을 구축할 수 있었으며 각 층 노드의 선택 입력을 변화시킴으로써 네트워크 구조 전체의 적응능력을 향상 시켰다. 유전자 알고리즘을 이용하여 자기구성 퍼지 다항식 뉴럴 네트워크의 입력변수의 수와 이에 해당되는 입력변수 그리고 규칙 후반부 다항식의 차수를 탐색하여 최적 의 자기구성 퍼지 다항식 뉴럴 네트워크를 구축한다. 그러나, SOFPNN의 기본 뉴론인 퍼지 규칙 기반 다항식 뉴론의 경우 입력변수가 많아질수록 규칙수가 기하급수적으로 증가한다는 단점을 가지고 있으나 본 노문에서 제안한 퍼지 집합 기반 다항식 뉴론(FSPN)의 규칙수는 입력 변수들이 서로 독립적이므로 규칙의 증가가 퍼지 규칙 기반 다항식 뉴런보다는 적다는 장점을 가지고 있다. 이러한 특성을 기반으로 기존의 SOFPNN의 노드에 퍼지 규칙 기반 다항식 뉴런 대신에 퍼지 집합 기반 다항식 뉴런을 적용한 SOFPNN을 제안하여 기존의 SOFPNN과 성능을 비교하였다. 최적의 자기 구성 퍼지 집합기반 다항식 뉴럴 네트워크를 구축하기 위하여 SOFPNN에서처럼 유전자 알고리즘을 이용하여 네트워크의 입력변수의 수와 이에 해당되는 입력변수 그리고 규칙 후반부 다항식의 차수를 탐색하였다.

• Journal of the Institute of Electronics and Information Engineers
• /
• 제50권1호
• /
• pp.225-231
• /
• 2013

In this study, we propose the design of optimized pRBFNNs-based night vision face recognition system using PCA algorithm. It is difficalt to obtain images using CCD camera due to low brightness under surround condition without lighting. The quality of the images distorted by low illuminance is improved by using night vision camera and histogram equalization. Ada-Boost algorithm also is used for the detection of face image between face and non-face image area. The dimension of the obtained image data is reduced to low dimension using PCA method. Also we introduce the pRBFNNs as recognition module. The proposed pRBFNNs consists of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned by using Fuzzy C-Means clustering. In the conclusion part of rules, the connection weights of pRBFNNs is represented as three kinds of polynomials such as linear, quadratic, and modified quadratic. The essential design parameters of the networks are optimized by means of Differential Evolution.

• Proceedings of the KIEE Conference
• /
• 대한전기학회 2011년도 제42회 하계학술대회
• /
• pp.1952-1953
• /
• 2011

본 논문에서는 다항식 기반 Radial Basis Function(RBF)신경회로망(Polynomial based Radial Basis function Neural Network)을 설계하고 이를 n-클래스 패턴 분류 문제에 적용한다. 제안된 다항식기반 RBF 신경회로망은 입력층, 은닉층, 출력층으로 이루어진다. 입력층은 입력 벡터의 값들을 은닉층으로 전달하는 기능을 수행하고 은닉층과 출력층사이의 연결가중치는 상수, 선형식 또는 이차식으로 이루어지며 경사 하강법에 의해 학습된다. Networks의 최종 출력은 연결가중치와 은닉층 출력의 곱에 의해 퍼지추론의 결과로서 얻어진다. 패턴분류기의 최적화는 PSO(Particle Swarm Optimization)알고리즘을 통해 이루어진다. 그리고 제안된 패턴분류기는 실제 얼굴인식 시스템으로 응용하여 직접 CCD 카메라로부터 입력받은 데이터를 영상 보정, 얼굴 검출, 특징 추출 등과 같은 처리 과정을 포함하여 서로 다른 등록인물의 n-클래스 분류 문제에 적용 및 평가되어 분류기로써의 성능을 분석해본다.