Support Vector Machines and Artificial Neural Networks for Identification of Residence Time Distribution Signals

Authors : H. Kasban, H. Arafa , S. M. S. Elaraby  , O. Zahran, M. El-Kordy and F. E. Abd El-Samie


Abstract

This paper presents a practical comparison between the Support Vector Machines (SVMs) and the Artificial Neural Networks (ANNs) as identifiers for the Residence Time Distribution (RTD) signal identification. In these identifiers, the cepstral features are extracted from the signal or from its power density spectrum (PDS) estimated using eigenvector method, or from the Discrete Cosine Transform (DCT), then the extracted features feed the identifiers. Both identifiers have been tested using the same RTD signals. The performance of these identifiers is evaluated in the presence of different types of noise. The simulation results proved that, the ANNs based identifier is more reliable in RTD signal identification, but it takes more time with respect to the SVMs based identifier.

Index Terms

Support Vector Machines, Artificial Neural Networks, Residence Time Distribution

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Support Vector Machines and Artificial Neural Networks for Identification of Residence Time Distribution Signals