• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.562-568
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• 2014

There exist precipitation echo and non-precipitation echo in the meteorological radar. It is difficult to effectively issue the right weather forecast because of a difficulty in determining these ambiguous point. In this study, Data is extracted from UF data of meteorological radar used. Input and output data for designing two classifier were built up through the analysis of the characteristics of precipitation and non-precipitation. Selected input variables are considered for better performance and echo classifier is designed using fuzzy relation-based nueral network. Comparative studies on the performance of echo classifier are carried out by considering both echo judgement module 1 and module 2.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.4
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• pp.534-540
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• 2014

In this paper, we propose a fuzzy combined Polynomial Neural Network(PNN) for pattern classification. The fuzzy combined PNN comes from the generic TSK fuzzy model with several linear polynomial as the consequent part and is the expanded version of the fuzzy model. The proposed pattern classifier has the polynomial neural networks as the consequent part, instead of the general linear polynomial. PNNs are implemented by stacking the simple polynomials dynamically. To implement one layer of PNNs, the various types of simple polynomials are used so that PNNs have flexibility and versatility. Although the structural complexity of the implemented PNNs is high, the PNNs become a high order-multi input polynomial finally. To estimate the coefficients of a polynomial neuron, The weighted linear discriminant analysis. The output of fuzzy rule system with PNNs as the consequent part is the linear combination of the output of several PNNs. To evaluate the classification ability of the proposed pattern classifier, we make some experiments with several machine learning data sets.