DocumentCode :
1395916
Title :
An integrated hybrid neural network and hidden Markov model classifier for sonar signals
Author :
Kundu, Amlan ; Chen, George C.
Author_Institution :
U.S. West Adv. Technol., Boulder, CO, USA
Volume :
45
Issue :
10
fYear :
1997
fDate :
10/1/1997 12:00:00 AM
Firstpage :
2566
Lastpage :
2570
Abstract :
We present here an integrated hybrid hidden Markov model and neural network (HMM/NN) classifier that combines the time normalization property of the HMM classifier with the superior discriminative ability of the neural net (NN). In the proposed classifier, a left-to-right HMM module is used first to segment the observation sequence of every exemplar into a fixed number of states. Subsequently, all the frames belonging to the same state are replaced by one average frame. Thus, every exemplar, irrespective of its time-state variation, is transformed into a fixed number of frames, i.e., a static pattern. The multilayer perceptron (MLP) neural net is then used as the classifier for these time-normalized exemplars. Some experimental results using sonar biologic signals are presented to demonstrate the superiority of the hybrid integrated classifier
Keywords :
hidden Markov models; multilayer perceptrons; pattern classification; sequences; sonar signal processing; HMM/NN classifier; discriminative ability; frames; hidden Markov model classifier; hybrid integrated classifier; integrated hybrid neural network; multilayer perceptron; observation sequence; sonar biologic signals; sonar signals; static pattern; time normalization property; time-normalized exemplars; Frequency; Hidden Markov models; Multi-layer neural network; Multilayer perceptrons; Neural networks; Sonar; Space technology; Transient analysis; Viterbi algorithm; Wavelet transforms;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/78.640720
Filename :
640720
Link To Document :
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