• Title, Summary, Keyword: Recursive function

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Radial basis function network design for chaotic time series prediction (혼돈 시계열의 예측을 위한 Radial Basis 함수 회로망 설계)

  • 신창용;김택수;최윤호;박상희
    • The Transactions of the Korean Institute of Electrical Engineers
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    • v.45 no.4
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    • pp.602-611
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    • 1996
  • In this paper, radial basis function networks with two hidden layers, which employ the K-means clustering method and the hierarchical training, are proposed for improving the short-term predictability of chaotic time series. Furthermore the recursive training method of radial basis function network using the recursive modified Gram-Schmidt algorithm is proposed for the purpose. In addition, the radial basis function networks trained by the proposed training methods are compared with the X.D. He A Lapedes's model and the radial basis function network by nonrecursive training method. Through this comparison, an improved radial basis function network for predicting chaotic time series is presented. (author). 17 refs., 8 figs., 3 tabs.

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Design of Incremental FCM-based Recursive RBF Neural Networks Pattern Classifier for Big Data Processing (빅 데이터 처리를 위한 증분형 FCM 기반 순환 RBF Neural Networks 패턴 분류기 설계)

  • Lee, Seung-Cheol;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.65 no.6
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    • pp.1070-1079
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    • 2016
  • In this paper, the design of recursive radial basis function neural networks based on incremental fuzzy c-means is introduced for processing the big data. Radial basis function neural networks consist of condition, conclusion and inference phase. Gaussian function is generally used as the activation function of the condition phase, but in this study, incremental fuzzy clustering is considered for the activation function of radial basis function neural networks, which could effectively do big data processing. In the conclusion phase, the connection weights of networks are given as the linear function. And then the connection weights are calculated by recursive least square estimation. In the inference phase, a final output is obtained by fuzzy inference method. Machine Learning datasets are employed to demonstrate the superiority of the proposed classifier, and their results are described from the viewpoint of the algorithm complexity and performance index.

High Performance CNC Control Using a New Discrete-Time Variable Structure Control Method (새로운 이산시간 가변구조 제어방법을 이용한 CNC의 고성능 제어)

  • Oh, Seung-Hyun;Kim, Jung-ho;Cho, Dong-il
    • Journal of Institute of Control, Robotics and Systems
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    • v.6 no.12
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    • pp.1053-1060
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    • 2000
  • In this paper, a discrete-time variable structure control method using recursively defined switching function and a decoupled variable structure disturbance compensator is used to achieve high performance circular motion control of a CNC machining center. The discrete-time variable structure control with the decoupled disturbance compensator method developed in this paper uses a recursive switching function defined as the sum of the current tracking error vector and the previous value of the switching function multiplied by a positive constant less than one. This recursive switching function provides much improved performance compared to the method that uses a switching function defined only as a linear combination of the current tracking error. Enhancements in tracking performance are demonstrated in the circular motion control using a CNC milling machine.

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Recursive Parameter estimation algorithm of the Probability (확률밀도함수의 축차모수추정 방법)

  • 한영열;박진수
    • Proceedings of the Korean Institute of Communication Sciences Conference
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    • pp.42-45
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    • 1984
  • we propose a new parameter estimation algorithm that converge with probability one and in mean square, If the mean is the function of parameter of the probability density function. This recursive algorithm is applicable also ever the parameters we estimate are multiparameter case. And the results are shown by the computer simulation.

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THE RECURSIVE ALGOFITHM FOR OPTIMAL REGULATOR OF NONSTANCARD SINGULARLY PERTURVED SYSTEMS

  • Mukaidani, Hiroaki;Xu, Hau;Mizukami, Koichi
    • 제어로봇시스템학회:학술대회논문집
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    • pp.10-13
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    • 1995
  • This paper considers the linear-quadratic optimal regulator problem for nonstandard singularly perturbed systems making use of the recursive technique. We first derive a generalized Riccati differential equation by the Hamilton-Jacobi equation. In order to obtain the feedback gain, we must solve the generalized algebraic Riccati equation. Using the recursive technique, we show that the solution of the generalized algebraic Riccati equation converges with the rate of convergence of O(.epsilon.). The existence of a bounded solution of error term can be proved by the implicit function theorem. It is enough to show that the corresponding Jacobian matrix is nonsingular at .epsilon. = 0. As a result, the solution of optimal regulator problem for nonstandard singularly perturbed systems can be obtained with an accuracy of O(.epsilon.$^{k}$ ). The proposed technique represents a significant improvement since the existing method for the standard singularly perturbed systems can not be applied to the nonstandard singularly perturbed systems.

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Design of Incremental K-means Clustering-based Radial Basis Function Neural Networks Model (증분형 K-means 클러스터링 기반 방사형 기저함수 신경회로망 모델 설계)

  • Park, Sang-Beom;Lee, Seung-Cheol;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.66 no.5
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    • pp.833-842
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    • 2017
  • In this study, the design methodology of radial basis function neural networks based on incremental K-means clustering is introduced for learning and processing the big data. If there is a lot of dataset to be trained, general clustering may not learn dataset due to the lack of memory capacity. However, the on-line processing of big data could be effectively realized through the parameters operation of recursive least square estimation as well as the sequential operation of incremental clustering algorithm. Radial basis function neural networks consist of condition part, conclusion part and aggregation part. In the condition part, incremental K-means clustering algorithms is used tweights of the conclusion part are given as linear function and parameters are calculated using recursive least squareo get the center points of data and find the fitness using gaussian function as the activation function. Connection s estimation. In the aggregation part, a final output is obtained by center of gravity method. Using machine learning data, performance index are shown and compared with other models. Also, the performance of the incremental K-means clustering based-RBFNNs is carried out by using PSO. This study demonstrates that the proposed model shows the superiority of algorithmic design from the viewpoint of on-line processing for big data.

Windowed Quaternion Estimator For Gyroless Spacecraft Attitude Determination

  • Kim, Injung
    • 제어로봇시스템학회:학술대회논문집
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    • pp.167.5-167
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    • 2001
  • Single point attitude determination method provides an optimal attitude minimizing the Wahba loss function. However, for the insufficient number of measurement vectors, the conventional single point methods has no unique solution. Thus, we introduce the sequential method to and an optimal attitude minimizing the windowed loss function. In this paper, this function is de ned as the sum of square errors for all measurement vectors within the axed sliding window. For simple implementation, the proposed algorithm is rewritten as a recursive form. Moreover, the covariance matrix is derived and expressed as a recursive form. Finally, we apply this algorithm to the attitude determination system with three LOS measurement sensors.

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Discount Survival Models

  • Shim, Joo-Y.;Sohn, Joong-K.
    • Journal of the Korean Data and Information Science Society
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    • v.7 no.2
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    • pp.227-234
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    • 1996
  • The discount survival model is proposed for the application of the Cox model on the analysis of survival data with time-varying effects of covariates. Algorithms for the recursive estimation of the parameter vector and the retrospective estimation of the survival function are suggested. Also the algorithm of forecasting of the survival function of individuals of specific covariates in the next time interval based on the information gathered until the end of a certain time interval is suggested.

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MATHEMATICAL UNDERSTANDING OF CONSCIOUSNESS AND UNCONCIOUSNESS

  • LEE, NAMI;KIM, EUN YOUNG;SHIN, CHANGSOO
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • v.21 no.2
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    • pp.75-79
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    • 2017
  • This paper approaches the subject of consciousness and unconsciousness from a mathematical point of view. It sets up a hypothesis that when unconscious state becomes conscious state, high density energy is released. We argue that the process of transformation of unconsciousness into consciousness can be expressed using the infinite recursive Heaviside step function. We claim that differentiation of the potential of unconsciousness with respect to time is the process of being conscious in a world where only time exists, since the thinking process never have any concrete space. We try to attribute our unconsciousness to a special solution of the multi-dimensional advection partial differential equation which can be represented by the finite recursive Heaviside step function. Mathematical language explains how the infinitive neural process is perceived and understood by consciousness in a definitive time.

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.