Performance Evaluation of Various Back-propagation Training Algorithms in the Neural Network Based Classification of Respiratory Signals
نویسندگان
چکیده
In this work, we describe a method for the classification of respiratory states based on four significant features using Artificial neural network (ANN). These features are extracted from the respiratory signals using modified threshold algorithm were fed as input parameters to the ANN for classification. A gradient based search algorithms are usually being used in ANN to find a set of suitable parameters for the given classification task. We analyze the performance of five back propagation training algorithms, namely, Levenberg-Marquardt, Scaled Conjugate Gradient, Quasi Newton BFGS Algorithm, One Step Secant and Powell-Beale Restarts algorithm for classification of the respiratory states. The Levenberg-Marquardt algorithm was observed to be correct in approximately 99% of the test cases.
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