• Title, Summary, Keyword: RBF Network

Search Result 233, Processing Time 0.045 seconds

Simplified RBF Multiuser Receivers of Synchronous DS-CDMA Systems (Synchronous DS-CDMA 시스템에서의 간략화된 RBF 다중사용자 수신기)

  • 고균병;이충용;강창언;홍대식
    • The Journal of Korean Institute of Communications and Information Sciences
    • /
    • v.28 no.5C
    • /
    • pp.555-560
    • /
    • 2003
  • For synchronous direct sequence-code division multiple access (DS-CDMA) systems, the authors propose an adaptive radial basis function (RBF) receiver with suboptimal structure that reduces not only the complexity with regard to the number of centers but also the quantity of instructions required per one bit reception. The proposed receiver is constructed with parallel RBF networks. Each RBF network has the same procedure as the conventional RBF receiver. The performance of each RBF network is affected by interferences which are assigned to the other RBF networks because neither RBF network uses the full user set. To combat these interferences, the partial IC technique is employed. Monte Carlo simulations over additive white Gaussian noise (AWGN) channels confirm that the proposed receiver with its reduced complexity is able to obtain near-optimum performance. Moreover, the proposed receiver is able to properly cope with a various environment.

Learning Performance Improvement of Fuzzy RBF Network (퍼지 RBF 네트워크의 학습 성능 개선)

  • Kim Kwang-Baek
    • Journal of Korea Multimedia Society
    • /
    • v.9 no.3
    • /
    • pp.369-376
    • /
    • 2006
  • In this paper, we propose an improved fuzzy RBF network which dynamically adjusts the rate of learning by applying the Delta-bar-Delta algorithm in order to improve the learning performance of fuzzy RBF networks. The proposed learning algorithm, which combines the fuzzy C-Means algorithm with the generalized delta learning method, improves its learning performance by dynamically adjusting the rate of learning. The adjustment of the learning rate is achieved by self-generating middle-layered nodes and by applying the Delta-bar-Delta algorithm to the generalized delta learning method for the learning of middle and output layers. To evaluate the learning performance of the proposed RBF network, we used 40 identifiers extracted from a container image as the training data. Our experimental results show that the proposed method consumes less training time and improves the convergence of teaming, compared to the conventional ART2-based RBF network and fuzzy RBF network.

  • PDF

A study on nonlinear channel equalization using RBF network (RBF 네트워크를 이용한 비선형 채널 등화에 관한 연구)

  • 전선도;위진우;강철호
    • The Journal of Korean Institute of Communications and Information Sciences
    • /
    • v.22 no.1
    • /
    • pp.64-71
    • /
    • 1997
  • Digital communication channels are imparied by linear effects such as dispersion, ISI(intersymbol Interference), fading phenomenon etc. But, the practical channel equalization system is required to design for compensating the nonlinear distortion caused by harmonic distortion etc. This paper is a study on the performance of nonlinear channel equalization using RBF(Radial Basis Funclion) network, which has the equivalent structure to the optimal Basian filter. Expecially, the variance of RBF network is modifiedby nonlinear polynomial filters to compare the convergence characteristic of nonlinear channel equalization. Experimental results show that the modified RBF network achieves the faster convergence property than conventional RBF network. Moreover, the RBF network ofhigher order variance modified represents the better performance than that of lower order variance in the bandpass channels and second/third order polynomial channels.

  • PDF

MIMO Receiver Using RBF Network Over Rich-Scattering fading channels (Rich-Scattering 페이딩 채널에서 RBF Network를 이용한 MIMO 수신기)

  • 고균병;강창언;홍대식
    • Journal of the Institute of Electronics Engineers of Korea TC
    • /
    • v.40 no.8
    • /
    • pp.301-306
    • /
    • 2003
  • This paper proposes a novel detection scheme using a radial basis function (RBF) network in a multi-input multi-output (MIMO) environment. In order to evaluate the performance of the proposed MIMO-RBF receiver, simulations are performed over the rich-scattering fading channel. Simulation results confirm that the proposed scheme shows the similar bit-error rate (BER) performance of a maximum likelihood detection (MLD) and outperforms Vertical-Bell Laboratories Layered Space-Time using minimum-mean-square-error nulling (VBLAST-MMSE) as well as VBLAST using zero-forcing nulling (VBLAST-ZF). Moreover, we investigate the effect on the performance of the number of RBF center with two modulation formats (BPSK and QPSK) and different number of transmit and receive antennas. The performance of the proposed detector is verified with respect to an initialization-rate of RBF centers.

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
    • /
    • /
    • pp.1183-1188
    • /
    • 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.

  • PDF

Self Organizing RBF Neural Network Equalizer (자력(自力) RBF 신경망 등화기)

  • Kim, Jeong-Su;Jeong, Jeong-Hwa
    • Journal of the Institute of Electronics Engineers of Korea CI
    • /
    • v.39 no.1
    • /
    • pp.35-47
    • /
    • 2002
  • This paper proposes a self organizing RBF neural network equalizer for the equalization of digital communications. It is the most important for the equalizer using the RBF neural network to estimate the RBF centers correctly and quickly, which are the desired channel states. However, the previous RBF equalizers are not used in the actual communication system because of some drawbacks that the number of channel states has to be known in advance and many centers are necessary. Self organizing neural network equalizer proposed in this paper can implement the equalization without prior information regarding the number of channel states because it selects RBF centers among the signals that are transmitted to the equalizer by the new addition and removal criteria. Furthermore, the proposed equalizer has a merit that is able to make a equalization with fewer centers than those of prior one by the course of the training using LMS and clustering algorithm. In the linear, nonlinear and standard telephone channel, the proposed equalizer is compared with the optimal Bayesian equalizer for the BER performance, the symbol decision boundary and the number of centers. As a result of the comparison, we can confirm that the proposed equalizer has almost similar performance with the Bavesian enualizer.

RBF Network Structure for Prediction of Non-linear, Non-stationary Time Series (비선형, 비정상 시계열 예측을 위한 RBF(Radial Basis Function) 회로망 구조)

  • Kim, Sang-Hwan;Lee, Jong-Ho
    • The Transactions of the Korean Institute of Electrical Engineers A
    • /
    • v.48 no.2
    • /
    • pp.168-175
    • /
    • 1999
  • In this paper, a modified RBF(Radial Basis Function) network structure is suggested for the prediction of a time-series with non-linear, non-stationary characteristics. Coventional RBF network predicting time series by using past outputs sense the trajectory of the time series and react when there exists strong relation between input and hidden activation function's RBF center. But this response is highly sensitive to level and trend of time serieses. In order to overcome such dependencies, hidden activation functions are modified to react to the increments of input variable and multiplied by increment(or dectement) for prediction. When the suggested structure is applied to prediction of Macyey-Glass chaotic time series, Lorenz equation, and Rossler equation, improved performances are obtained.

  • PDF

The Fault Detection of an Air-Conditioning System by Using a Residual Input RBF Neural Network (잔차입력 RBF 신경망을 사용한 냉방기 고장검출 알고리즘)

  • Han, Do-Young;Ryoo, Byoung-Jin
    • Korean Journal of Air-Conditioning and Refrigeration Engineering
    • /
    • v.17 no.8
    • /
    • pp.780-788
    • /
    • 2005
  • Two different types of algorithms were developed and applied to detect the partial faults of a multi-type air conditioning system. Partial faults include the compressor valve leakage, the refrigerant pipe partial blockage, the condenser fouling, and the evaporator fouling. The first algorithm was developed by using mathematical models and parity relations, and the second algorithm was developed by using mathematical models and a RBF neural network. Test results showed that the second algorithm was better than the first algorithm in detecting various partial faults of the system. Therefore, the algorithm developed by using mathematical models and a RBF neural network may be used for the detection of partial faults of an air-conditioning system.

Design of RBF-based Polynomial Neural Network (방사형 기저 함수 기반 다항식 뉴럴네트워크 설계)

  • Kim, Ki-Sang;Jin, Yong-Ha;Oh, Sung-Kwun;Kim, Hyun-Ki
    • Proceedings of the IEEK Conference
    • /
    • /
    • pp.261-263
    • /
    • 2009
  • 본 연구에서는 복잡한 비선형 모델링 방법인 RBF 뉴럴 네트워크(Radial Basis Function Neural Network)와 PNN(Polynomial Neural Network)을 접목한 새로운 형태의 Radial Basis Function Polynomial Neural Network(RPNN)를 제안한다. RBF 뉴럴 네트워크는 빠른 학습 시간, 일반화 그리고 단순화의 특징으로 비선형 시스템 모델링 등에 적용되고 있으며, PNN은 생성된 노드들 중에서 우수한 결과값을 가진 노드들을 선택함으로써 모델의 근사화 및 일반화에 탁월한 효과를 가진 비선형 모델링 방법이다. 제안된 RPNN모델의 기본적인 구조는 PNN의 형태를 이루고 있으며, 각각의 노드는 RBF 뉴럴 네트워크로 구성하였다. 사용된 RBF 뉴럴 네트워크에서의 커널 함수로는 FCM 클러스터링을 사용하였으며, 각 노드의 후반부는 다항식 구조로 표현하였다. 또한 각 노드의 후반부 파라미터들은 최소자승법을 이용하여 최적화 하였다. 제안한 모델의 적용 및 유용성을 비교 평가하기 위하여 비선형 데이터를 이용하여 그 우수성을 보인다.

  • PDF

A Performance Improvement for Tracking Controller of a Mobile Robot Using Neural Networks (신경망을 이용한 이동로봇 궤적제어기 성능개선)

  • Park Jae-Hwae;Lee Man-Hyung;Lee JangMyung
    • Journal of Institute of Control, Robotics and Systems
    • /
    • v.10 no.12
    • /
    • pp.1249-1255
    • /
    • 2004
  • A new parameter adaptation scheme for RBF Neural Network (NN) has been developed in this paper. Even though the RBF Neural Network (NN) based controllers are robust against both un-modeled dynamics and external disturbances, the performance is not satisfactory for a fast and precise mobile robot. To improve the tracking performance as well as robustness, all the parameters of RBF NN are updated in real time. The stability of this control law is rigorously proved by following the Lyapunov stability theory and shown by the experimental simulations. The fact that all of the weighting factors, width and center of RBF NN have been updated implies that this scheme utilizes all the possibilities in RBF NN to make the controller robust and precise while the mobile robot is following un-known trajectories. The performance of this new algorithm has been compared to the conventional RBF NN controller where some of the parameters are adjusted for robustness.