• 한국지능시스템학회논문지
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• 제10권3호
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• pp.188-193
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• 2000

본 논문에서는 플랜트를 위한 규칙수가 줄어든 뉴로-퍼지 모델을 얻기 위한 접근을 제안한다. 뉴로-퍼지 네트워크는 가우시안 소속함수를 가진 RBF(Radial Basis Function) 네트워크들로 구성되고 오차 역전파 학습 알고리듬에 의해 학습된다. 러프 집합 이론에서 의존도는 규칙들으 줄이는데 사용된다. 모델에서 각 규칙이 조건 소속함수 값과 플랜트의 출력 값 사이의 의온도는 플랜트를 동정하기 위하여 규칙이 얼마나 많은 공헌을 하는가를 알 수 있도록 한다. 줄어든 모델은 원래의 것으로써 동일한 성능을 유지하는 동안 선택 알고리듬은 복잡성과 구조의 잉여성을 최소화할 수 있다.

• Computers and Concrete
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• 제16권6호
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• pp.919-932
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• 2015

This paper presents a new method for assessing the three-point-bending (3PB) strength of mortar beams in a non-destructive manner, based on neural network (NN) models. The models are based on the radial basis function (RBF) architecture and the fuzzy means algorithm is employed for training, in order to boost the prediction accuracy. Data for training the models were collected based on a series of experiments, where the cement mortar beams were subjected to various bending mechanical loads and the resulting pressure stimulated currents (PSCs) were recorded. The input variables to the NN models were then calculated by describing the PSC relaxation process through a generalization of Boltzmannn-Gibbs statistical physics, known as non-extensive statistical physics (NESP). The NN predictions were evaluated using k-fold cross-validation and new data that were kept independent from training; it can be seen that the proposed method can successfully form the basis of a non-destructive tool for assessing the bending strength. A comparison with a different NN architecture confirms the superiority of the proposed approach.

• 전기학회논문지
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• 제60권12호
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• pp.2352-2360
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• 2011

In this paper, we introduce a comprehensive design methodology of Radial Basis Function Neural Networks (RBFNN) that is based on mechanism of clustering and optimization algorithm. We can divide some clusters based on similarity of input dataset by using clustering algorithm. As a result, the number of clusters is equal to the number of nodes in the hidden layer. Moreover, the centers of each cluster are used into the centers of each receptive field in the hidden layer. In this study, we have applied Fuzzy-C Means(FCM) and K-Means(KM) clustering algorithm, respectively and compared between them. The weight connections of model are expanded into the type of polynomial functions such as linear and quadratic. In this reason, the output of model consists of relation between input and output. In order to get the optimal structure and better performance, Particle Swarm Optimization(PSO) is used. We can obtain optimized parameters such as both the number of clusters and the polynomial order of weights connection through structural optimization as well as the widths of receptive fields through parametric optimization. To evaluate the performance of proposed model, NXT equipment offered by National Instrument(NI) is exploited. The situation awareness system-related intelligent model was built up by the experimental dataset of distance information measured between object and diverse sensor such as sound sensor, light sensor, and ultrasonic sensor of NXT equipment.