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학술지 퍼지추론 기반 다항식 RBF 뉴럴 네트워크의 설계 및 최적화
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백진열, 박병준, 오성권
전기학회논문지 P, v.58 no.2, pp.399-406
대한전기학회 (KIEE)
08MC3600, Pro-active Idle-Stop을 위한 가상센서기반 Situation-Aware 기술개발, 손명희
In this study, Polynomial Radial Basis Function Neural Network(pRBFNN) based on Fuzzy Inference System is designed and its parameters such as learning rate, momentum coefficient, and distributed weight (width of RBF) are optimized by means of Particle Swarm Optimization. The proposed model can be expressed as three functional module that consists of condition part, conclusion part, and inference part in the viewpoint of fuzzy rule formed in 'If-then'. In the condition part of pRBFNN as a fuzzy rule, input space is partitioned by defining kernel functions (RBFs). Here, the structure of kernel functions, namely, RBF is generated from HCM clustering algorithm. We use Gaussian type and Inverse multiquadratic type as a RBF. Besides these types of RBF, Conic RBF is also proposed and used as a kernel function. Also, in order to reflect the characteristic of dataset when partitioning input space, we consider the width of RBF defined by standard deviation of dataset. In the conclusion part, the connection weights of pRBFNN are represented as a polynomial which is the extended structure of the general RBF neural network with constant as a connection weights. Finally, the output of model is decided by the fuzzy inference of the inference part of pRBFNN. In order to evaluate the proposed model, nonlinear function with 2 inputs, waster water dataset and gas furnace time series dataset are used and the results of pRBFNN are compared with some previous models. Approximation as well as generalization abilities are discussed with these results.
KSP 제안 키워드
Clustering algorithm, Connection weights, Extended structure, Functional Modules, Fuzzy Inference System, HCM clustering, Kernel function, Learning rate, Proposed model, RBF neural network, Standard deviation(STD)