A neural network approach to robust shape classification
Pattern Recognition
Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
Introduction to artificial neural systems
Introduction to artificial neural systems
Improving the convergence of the back-propagation algorithm
Neural Networks
Neural Networks and Speech Processing
Neural Networks and Speech Processing
A fast new algorithm for training feedforward neural networks
IEEE Transactions on Signal Processing
Multiscale corner detection by using wavelet transform
IEEE Transactions on Image Processing
Survey of neural network technology for automatic target recognition
IEEE Transactions on Neural Networks
A simple method to derive bounds on the size and to train multilayer neural networks
IEEE Transactions on Neural Networks
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Neural network classifiers have been widely used in classification due to its adaptive and parallel processing ability. This paper concerns classification of underwater passive sonar signals radiated by ships using neural networks. Classification process can be divided into two stages: one is the signal preprocessing and feature extraction, the other is the recognition process. In the preprocessing and feature extraction stage, the wavelet transform (WT) is used to extract tonal features from the average power spectral density (APSD) of the input data. In the classification stage, two kinds of neural network classifiers are used to evaluate the classification results, inclusive of the hyperplane-based classifier-Multilayer Perceptron (MLP)-and the kernel-based classifier-Adaptive Kernel Classifier (AKC). The experimental results obtained from MLP with different configurations and algorithms show that the bipolar continuous function possesses a wider range and a higher value of the learning rate than the unipolar continuous function. Besides, AKC with fixed radius (modified AKC) sometimes gives better performance than AKC, but the former takes more training time in selecting the width of the receptive field. More important, networks trained with tonal features extracted by WT has 96% or 94% correction rate, but the training with original APSDs only have 80% correction rate.