Online kernel density estimation using fuzzy logic


During this paper, a fuzzy methodology is proposed to estimate kernel density function on-line. To achieve this goal, Gaussian mixture model is generated by the fuzzy algorithm. Defuzzifier operator is changed to create it suitable for this application. Suggests that and variances of the model are adapted using observed data in every new sample. Then, rule weights are tuned by minimising the expected L2 risk function of the estimated and true PDFs. In contrast to the present approaches, our approach does not require fine-tuning parameters for a specific application, specific sorts of the target distributions aren't assumed, and temporal constraints are not thought-about on the observed data. The algorithm is simple and straightforward to use. Simulation results show the capability of the proposed algorithm in online and accurate estimation of kernel density function.

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