• Title/Summary/Keyword: fuzzification

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Optimization of FCM-based Radial Basis Function Neural Network Using Particle Swarm Optimization (PSO를 이용한 FCM 기반 RBF 뉴럴 네트워크의 최적화)

  • Choi, Jeoung-Nae;Kim, Hyun-Ki;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.57 no.11
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    • pp.2108-2116
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    • 2008
  • The paper concerns Fuzzy C-Means clustering based Radial Basis Function neural networks (FCM-RBFNN) and the optimization of the network is carried out by means of Particle Swarm Optimization(PSO). FCM-RBFNN is the extended architecture of Radial Basis Function Neural Network(RBFNN). In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values directly rely on the computation of the relevant distance between data points by means of FCM. Also, as the consequent part of fuzzy rules extracted by the FCM - RBFNN model, the order of four types of polynomials can be considered such as constant, linear, quadratic and modified quadratic. Weighted Least Square Estimator(WLSE) are used to estimates the coefficients of polynomial. Since the performance of FCM-RBFNN is affected by some parameters of FCM-RBFNN such as a specific subset of input variables, fuzzification coefficient of FCM, the number of rules and the order of polynomials of consequent part of fuzzy rule, we need the structural as well as parametric optimization of the network. In this study, the PSO is exploited to carry out the structural as well as parametric optimization of FCM-RBFNN. Moreover The proposed model is demonstrated with the use of numerical example and gas furnace data set.

RBFNNs-based Recognition System of Vehicle License Plate Using Distortion Correction and Local Binarization (왜곡 보정과 지역 이진화를 이용한 RBFNNs 기반 차량 번호판 인식 시스템)

  • Kim, Sun-Hwan;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.65 no.9
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    • pp.1531-1540
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    • 2016
  • In this paper, we propose vehicle license plate recognition system based on Radial Basis Function Neural Networks (RBFNNs) with the use of local binarization functions and canny edge algorithm. In order to detect the area of license plate and also recognize license plate numbers, binary images are generated by using local binarization methods, which consider local brightness, and canny edge detection. The generated binary images provide information related to the size and the position of license plate. Additionally, image warping is used to compensate the distortion of images obtained from the side. After extracting license plate numbers, the dimensionality of number images is reduced through Principal Component Analysis (PCA) and is used as input variables to RBFNNs. Particle Swarm Optimization (PSO) algorithm is used to optimize a number of essential parameters needed to improve the accuracy of RBFNNs. Those optimized parameters include the number of clusters and the fuzzification coefficient used in the FCM algorithm, and the orders of polynomial of networks. Image data sets are obtained by changing the distance between stationary vehicle and camera and then used to evaluate the performance of the proposed system.

A Fuzzy System Representation of Functions of Two Variables and its Application to Gray Scale Images

  • Moon, Byung-soo;Kim, Young-taek;Kim, Jang-yeol
    • Journal of the Korean Institute of Intelligent Systems
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    • v.11 no.7
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    • pp.569-573
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    • 2001
  • An approximate representation of discrete functions {f$_{i,j}\mid$|i, j=-1, 0, 1, …, N+1}in two variables by a fuzzy system is described. We use the cubic B-splines as fuzzy sets for the input fuzzification and spike functions as the output fuzzy sets. The ordinal number of f$_{i,j}$ in the sorted list is taken to be the out put fuzzy set number in the (i, j) th entry of the fuzzy rule table. We show that the fuzzy system is an exact representation of the cubic spline function s(x, y)=$\sum_{N+1}^{{i,j}=-1}f_{i,j}B_i(x)B_j(y)$ and that the approximation error S(x, y)-f(x, y) is surprisingly O($h^2$) when f(x, y) is three times continuously differentiable. We prove that when f(x, y) is a gray scale image, then the fuzzy system is a smoothed representation of the image and the original image can be recovered exactly from its fuzzy system representation when it is a digitized image.e.

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A Fuzzy Control of Autonomous Mobile Robot for Obstacle Avoidance (장애물 회피를 위한 자율이동로봇의 퍼지제어)

  • Chae Moon-Seok;Jung Tae-Young;Kang Suk-Bum;Yang Tae-Kyu
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.10 no.9
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    • pp.1718-1726
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    • 2006
  • In this paper, we proposed a fuzzy controller and algorithm for efficiently obstacle avoidance in unknown space. The ultrasonic sensor is used for position and distance recognition of obstacle, and fuzzy controller is used for left and right wheels angular velocity control. The fuzzification is used singleton method and the control rule is each wheel forty-nine. The fuzzy inference is used simplified Mamdani's reasoning and defuzzification is used SCOG(Simplified Center Of Gravity). The computer simulation based on mobile robot modelling was performed for the capacity of fuzzy controller and the really applicable possibility revaluation of the proposed avoidance algorithm and fuzzy controller. As a result, mobile robot was exactly reached in target and it avoided obstacle efficiently.

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.

Tracking Detection using Information Granulation-based Fuzzy Radial Basis Function Neural Networks (정보입자기반 퍼지 RBF 뉴럴 네트워크를 이용한 트랙킹 검출)

  • Choi, Jeoung-Nae;Kim, Young-Il;Oh, Sung-Kwun;Kim, Jeong-Tae
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.58 no.12
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    • pp.2520-2528
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    • 2009
  • In this paper, we proposed tracking detection methodology using information granulation-based fuzzy radial basis function neural networks (IG-FRBFNN). According to IEC 60112, tracking device is manufactured and utilized for experiment. We consider 12 features that can be used to decide whether tracking phenomenon happened or not. These features are considered by signal processing methods such as filtering, Fast Fourier Transform(FFT) and Wavelet. Such some effective features are used as the inputs of the IG-FRBFNN, the tracking phenomenon is confirmed by using the IG-FRBFNN. The learning of the premise and the consequent part of rules in the IG-FRBFNN is carried out by Fuzzy C-Means (FCM) clustering algorithm and weighted least squares method (WLSE), respectively. Also, Hierarchical Fair Competition-based Parallel Genetic Algorithm (HFC-PGA) is exploited to optimize the IG-FRBFNN. Effective features to be selected and the number of fuzzy rules, the order of polynomial of fuzzy rules, the fuzzification coefficient used in FCM are optimized by the HFC-PGA. Tracking inference engine is implemented by using the LabVIEW and loaded into embedded system. We show the superb performance and feasibility of the tracking detection system through some experiments.

Stability Analysis and Proposal of a Simple Form of a Fuzzy PID Controller

  • Lee, Byung-Kyul;Kim, In-Hwan;Kim, Jong-Hwa
    • Journal of Advanced Marine Engineering and Technology
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    • v.28 no.8
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    • pp.1299-1312
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    • 2004
  • This paper suggests the simple form of a fuzzy PID controller and describes the design principle, tracking performance, stability analysis and changes of parameters of a suggested fuzzy PID controller. A fuzzy PID controller is derived from the design procedure of fuzzy control. It is well known that a fuzzy PID controller has a simple structure of the conventional PID controller but posses its self-tuning control capability and the gains of a fuzzy PID controller become nonlinear functions of the inputs. Nonlinear calculation during fuzzification, defuzzification and the fuzzy inference require more time in computation. To increase the applicability of a fuzzy PID controller to digital computer, a simple form of a fuzzy PID controller is introduced by the backward difference mapping and the analysis of the fuzzy input space. To guarantee the BIBO stability of a suggested fuzzy PID controller, ‘small gain theorem’ which proves the BIBO stability of a fuzzy PI and a fuzzy PD controller is used. After a detailed stability analysis using ‘small gain theorem’, from which a simple and practical method to decide the parameters of a fuzzy PID controller is derived. Through the computer simulations for the linear and nonlinear plants, the performance of a suggested fuzzy PID controller will be assured and the variation of the gains of a fuzzy PID controller will be investigated.

Fuzzy Applications in a Multi-Machine Power System Stabilizer

  • Sambariya, D.K.;Gupta, Rajeev
    • Journal of Electrical Engineering and Technology
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    • v.5 no.3
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    • pp.503-510
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    • 2010
  • This paper proposes the use of fuzzy applications to a 4-machine and 10-bus system to check stability in open conditions. Fuzzy controllers and the excitation of a synchronous generator are added. Power system stabilizers (PSSs) are added to the excitation system to enhance damping during low frequency oscillations. A fuzzy logic power system stabilizer (PSS) for stability enhancement of a multi-machine power system is also presented. To attain stability enhancement, speed deviation ($\Delta\omega$) and acceleration ($\Delta\varpi$) of the Kota Thermal synchronous generator rotor are taken as inputs to the fuzzy logic controller. These variables have significant effects on the damping of generator shaft mechanical oscillations. The stabilizing signals are computed using fuzzy membership functions that are dependent on these variables. The performance of the fuzzy logic PSS is compared with the open power system, after which the simulations are tested under different operating conditions and changes in reference voltage. The simulation results are quite encouraging and satisfactory. Similarly, the system is tested for the different defuzzification methods, and based on the results, the centroid method elicits the best possible system response.

Double Talk Detection using the Fuzzy Inference (퍼지 추론을 이용한 동시통화 검출)

  • 류근택;배현덕
    • Journal of Broadcast Engineering
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    • v.5 no.1
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    • pp.123-129
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    • 2000
  • This paper addresses a new double detection algorithm which is based on the fuzzy control in the adaptive echo canceller of communication system. In this method, the two input of the fuzzy inference for detecting double talk condition are used. The one is the cross-correlation coefficient between the error signal and the primary signal which is the summed signal of the real echo signal and the near-end signal. The other is the cross-correlation coefficient between the estimation error signal and the primary signal. The fuzzy controller made a fuzzification for two inputs by the membership functions of trapezoid and them became the composition using inference rules. The composed result is defuzzificated by the center gravity method. The output is compared with two threshold values to detect double talk and echo path variation effectively. It is confirmed by computer simulation that this fuzzy double talk detector is able to track echo path variation accurately.

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A Tuning Method for the Membership Functions of a Fuzzy Controller (퍼지제어기의 멤버쉽함수의 튜닝 방법)

  • Lee, Ji-Hong;Chae, Seog;Oh, Young-Seok
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.30B no.4
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    • pp.138-147
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    • 1993
  • It is known that the performance of a fuzzy controller is related with fuzzification method, inference rules, defuzzification method, and linguistic variables. Among these, generally, the linguistic variables and control rules are transfered to control engineers from an expert or experts of the controlled system and other parts are designed by control engineers. However, there may be some missed infirmations or uncertainties in the transfered data. The purpose of the paper is to propose an algorithm to tune the membership functions of initially given fuzzy sets To do so, a simple shape of the membership fuction is assumed for the fuzzy sets, and the relations between the shapes of the fuzzy sets and the performance of the control system is derived. According to the relations, the shape of the membership functions are modified during operation of the whole system. The proposed algorithm will be applied to two emample plants, type 1 and type 0 systems.

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