Salazar, Jaime, authorAzimi-Sadjadi, Mahmood R., authorRobinson, Marc, authorIEEE, publisher2007-01-032007-01-032005Robinson, Marc, Mahmood R. Azimi-Sadjadi, and Jaime Salazar, Multi-Aspect Target Discrimination Using Hidden Markov Models and Neural Networks, IEEE Transactions on Neural Networks 16, no. 2 (March 2005): 447-459.http://hdl.handle.net/10217/923This paper presents a new multi-aspect pattern classification method using hidden Markov models (HMMs). Models are defined for each class, with the probability found by each model determining class membership. Each HMM model is enhanced by the use of a multilayer perception (MLP) network to generate emission probabilities. This hybrid system uses the MLP to find the probability of a state for an unknown pattern and the HMM to model the process underlying the state transitions. A new batch gradient descent-based method is introduced for optimal estimation of the transition and emission probabilities. A prediction method in conjunction with HMM model is also presented that attempts to improve the computation of transition probabilities by using the previous states to predict the next state. This method exploits the correlation information between consecutive aspects. These algorithms are then implemented and benchmarked on a multi-aspect underwater target classification problem using a realistic sonar data set collected in different bottom conditions.born digitalarticleseng©2005 IEEE.Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.neural networksmulti-aspect pattern classificationhidden Markov models (HMMs)predictionunderwater target classificationText