Kernelized Radial Basis Probabilistic Neural Network for Classification of River Water Quality
نویسندگان
چکیده
Radial Basis Probabilistic Neural Network (RBPNN) demonstrates broader and much more generalized capabilities which have been successfully applied to different fields. In this paper, the RBPNN is extended by calculating the Euclidean distance of each data point based on a kernel-induced distance instead of the conventional sum-of squares distance. The kernel function is a generalization of the distance metric that measures the distance between two data points as the data points are mapped into a high dimensional space. Through comparing the four constructed classification models with Kernelized RBPNN, Radial Basis Function networks, RBPNN and Back-Propagation networks as intended, results showed that, model classification on River water quality of Langat river in Selangor, Malaysia by Kernelized RBPNN exhibited excellent performance in this regard.
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