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 / Gianfelici, F; Turchetti, Claudio; Crippa, Paolo; Battistelli, V.. - 4692:(2007), pp. 463-470. [10.1007/978-3-540-74819-9_57]
Generalization of a recognition algorithm based on Karhunen-Loève transform
TURCHETTI, Claudio;CRIPPA, Paolo;
2007-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
