Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Estimating the Pen Trajectories of Static Signatures Using Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
The forward-backward search algorithm
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
Detecting faults in heterogeneous and dynamic systems using DSP and an agent-based architecture
Engineering Applications of Artificial Intelligence
PM2.5 concentration prediction using hidden semi-Markov model-based times series data mining
Expert Systems with Applications: An International Journal
On the memory complexity of the forward-backward algorithm
Pattern Recognition Letters
Brief paper: Geometric properties of partial least squares for process monitoring
Automatica (Journal of IFAC)
Approximate forward-backward algorithm for a switching linear Gaussian model
Computational Statistics & Data Analysis
Improved hidden Markov models in the wavelet-domain
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
On receiver structures for channels having memory
IEEE Transactions on Information Theory
Compact encoding of stationary Markov sources
IEEE Transactions on Information Theory
Decision making in Markov chains applied to the problem of pattern recognition
IEEE Transactions on Information Theory
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Review: Cloud computing service composition: A systematic literature review
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Hidden Markov models (HMMs) perform parameter estimation based on the forward-backward (FB) procedure and the Baum-Welch (BW) algorithm. The two algorithms together may increase the computational complexity and the difficulty to understand the algorithm structure of HMMs clearly. In this study, an increasing mapping based hidden Markov model (IMHMM) is proposed. Between the observation sequence and possible state sequence an increasing mapping is established. The re-estimation formulas for the model parameters are derived straightforwardly based on these mappings instead of FB variables. The IMHMM has simpler algorithm structure and lower storage requirement than the HMM. Based on IMHMM, an expandable process monitoring and fault diagnosis framework for large-scale dynamical process is developed. To characterize the dynamic process, a novel index considering serial correlation is used to evaluate process state. The presented methodology is carried out in Tennessee Eastman process (TEP). The results show improvement over HMM in terms of memory complexity and training time of the model. Also, the power of IMHMM can be observed compared with principal component analysis (PCA) based methods.