This paper presents a generalization of a recognition algorithm that is able to classify non-deterministic signals generated by a set of Stochastic Processes (SPs), the number of which may be arbitrarily chosen. This generalized recognizer exploits the nondeterministic trajectories generated by the Karhunen-Loève Transform (KLT) with no additional constraints or explicit limitations, and without the probability density function (pdf) estimation. Several experimentations were performed on SPs generated as solutions of non-linear differential equations with parameters and initial conditions being random variables. The results show a recognition rate which is close to 100%, thus demonstrating the validity of the generalized algorithm.

Generalization of a recognition algorithm based on Karhunen-Loève transform

TURCHETTI, Claudio;CRIPPA, Paolo;
2007

Abstract

This paper presents a generalization of a recognition algorithm that is able to classify non-deterministic signals generated by a set of Stochastic Processes (SPs), the number of which may be arbitrarily chosen. This generalized recognizer exploits the nondeterministic trajectories generated by the Karhunen-Loève Transform (KLT) with no additional constraints or explicit limitations, and without the probability density function (pdf) estimation. Several experimentations were performed on SPs generated as solutions of non-linear differential equations with parameters and initial conditions being random variables. The results show a recognition rate which is close to 100%, thus demonstrating the validity of the generalized algorithm.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11566/42322
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