A Study on Static Situation Awareness System with the Aid of Optimized Polynomial Radial Basis Function Neural Networks

최적화된 pRBF 뉴럴 네트워크에 의한 정적 상황 인지 시스템에 관한 연구

  • 오성권 (수원대 공대 전기공학과) ;
  • 나현석 (수원대 공대 전기공학과) ;
  • 김욱동 (수원대 공대 전기공학과)
  • Received : 2011.09.19
  • Accepted : 2011.10.25
  • Published : 2011.12.01


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.



Grant : U-city 보안감시 기술협력센터

Supported by : 한국연구재단, 경기도


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