Robinson Marc, Azimi-Sadjadi Mahmood R, Salazar Jaime
Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, USA.
IEEE Trans Neural Netw. 2005 Mar;16(2):447-59. doi: 10.1109/TNN.2004.841805.
This 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.
本文提出了一种使用隐马尔可夫模型（HMM）的新型多方面模式分类方法。为每个类别定义模型，每个模型找到的概率决定类别归属。通过使用多层感知器（MLP）网络来生成发射概率，从而增强每个HMM模型。这个混合系统使用MLP来找到未知模式的状态概率，并使用HMM对状态转换背后的过程进行建模。引入了一种基于批梯度下降的新方法，用于对转移概率和发射概率进行最优估计。还提出了一种结合HMM模型的预测方法，该方法尝试通过使用先前状态预测下一个状态来改进转移概率的计算。此方法利用了连续方面之间的相关信息。然后，使用在不同海底条件下收集的真实声纳数据集，在多方面水下目标分类问题上实现并测试了这些算法。
