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Journal articles on the topic 'Baum-Welch algorithm'

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Published: 4 June 2021
Last updated: 12 June 2025

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1

Assunção, Joaquim, Paulo Fernandes, and Jean-Marc Vincent. "Piecewise Aggregation for HMM Fitting: A Pre-Fitting Model for Seamless Integration with Time-Series Data." International Journal of Software Engineering and Knowledge Engineering 29, no. 11n12 (2019): 1835–50. http://dx.doi.org/10.1142/s0218194019400242.

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We propose a simple, fast, deterministic pre-fitting approach which derives the Baum–Welch algorithm initial values directly from the input data. Such pre-fitting has the purpose of improving the fitting time for a given Hidden Markov Model (HMM) while maintaining the original Baum–Welch algorithm as the fitting one. The fitting time is improved by avoiding the Baum–Welch algorithm sensitiveness through the generation of parameters closer to the global maximum likelihood. Furthermore, by keeping the original Baum–Welch algorithm as the fitting one, we guarantee that all related methods will continue to work properly. On the other hand, the pre-fitting generates the HMM parameters directly derived from time-series data, without any data transformation, using an [Formula: see text] operation.
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Zhang, Xu, Ting Wu, Qiuhua Zheng, et al. "Multi-Step Attack Detection Based on Pre-Trained Hidden Markov Models." Sensors 22, no. 8 (2022): 2874. http://dx.doi.org/10.3390/s22082874.

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Currently, hidden Markov-based multi-step attack detection models are mainly trained using the unsupervised Baum–Welch algorithm. The Baum–Welch algorithm is sensitive to the initial values of model parameters. However, its training uses random or average parameter initialization methods, which frequently results in the model training into a local optimum, thus, making the model unable to fit the alert logs well and thereby reducing the detection effectiveness of the model. To solve this issue, we propose a pre-training method for multi-step attack detection models based on the high semantic similarity of alerts in the same attack phase. The method first clusters the alerts based on their semantic information and pre-classifies the attack phase to which each alert belongs. Then, the distance of the alert vector to each attack stage is converted into the probability of generating alerts in each attack stage, replacing the initial value of Baum–Welch. The effectiveness of the proposed method is evaluated using the DARPA 2000 dataset, DEFCON21 CTF dataset, and ISCXIDS 2012 dataset. The experimental results show that the hidden Markov multi-step attack detection method based on pre-training of the proposed model parameters had higher detection accuracy than the Baum–Welch-based, K-means-based, and transfer learning differential evolution-based hidden Markov multi-step attack detection methods.
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Zhang, Jingwen, Fanggang Wang, Zhangdui Zhong, and Shilian Wang. "Continuous Phase Modulation Classification via Baum-Welch Algorithm." IEEE Communications Letters 22, no. 7 (2018): 1390–93. http://dx.doi.org/10.1109/lcomm.2018.2821171.

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Steeneck, Daniel, and Fredrik Eng-Larsson. "The Baum–Welch algorithm with limiting distribution constraints." Operations Research Letters 46, no. 6 (2018): 563–67. http://dx.doi.org/10.1016/j.orl.2018.08.008.

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5

GOLOD, DANIIL, and DANIEL G. BROWN. "A TUTORIAL OF TECHNIQUES FOR IMPROVING STANDARD HIDDEN MARKOV MODEL ALGORITHMS." Journal of Bioinformatics and Computational Biology 07, no. 04 (2009): 737–54. http://dx.doi.org/10.1142/s0219720009004242.

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In this tutorial, we discuss two main algorithms for Hidden Markov Models or HMMs: the Viterbi algorithm and the expectation phase of the Baum–Welch algorithm, and we describe ways to improve their naïve implementations. For the Baum–Welch algorithm we first present an implementation of the expectation computations using constant space. We then discuss the classical implementation of this calculation and describe ways to reduce its space usage to logarithmic and [Formula: see text], with their respective CPU costs. We also note where each respective algorithm can be parallelized. For the Viterbi algorithm, we describe [Formula: see text] and logarithmic space algorithms which increase CPU use by a factor of two and by a logarithmic factor respectively. We also present two recent heuristics for decreasing space use, which in practice lead to logarithmic space use. Classical version of Viterbi cannot be parallelized by splitting sequence in several subsequences, but we show a parallelization that works if we are willing to pay a significant extra CPU cost. Finally we show a very simple parallelization trick which enables full usage of multiple CPUs/cores under the condition that they share memory.
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Xu, Hui Hong, and Su Chun Gao. "Speaker Recognition Study Based on Optimized Baum-Welch Algorithm." Applied Mechanics and Materials 543-547 (March 2014): 2471–73. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2471.

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The speaker recognition is a sort of biological recognition technology according to person's sound to identify .The article based on vc platform implement speaker recognitions function using VQ and HMM technology. using genetic algorithm to improve the Baum-Welch algorithm.Trough experiment verificate that improved-arithmetic enhance recognition effect.
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7

Zhang, Yanxue, Dongmei Zhao, and Jinxing Liu. "The Application of Baum-Welch Algorithm in Multistep Attack." Scientific World Journal 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/374260.

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The biggest difficulty of hidden Markov model applied to multistep attack is the determination of observations. Now the research of the determination of observations is still lacking, and it shows a certain degree of subjectivity. In this regard, we integrate the attack intentions and hidden Markov model (HMM) and support a method to forecasting multistep attack based on hidden Markov model. Firstly, we train the existing hidden Markov model(s) by the Baum-Welch algorithm of HMM. Then we recognize the alert belonging to attack scenarios with the Forward algorithm of HMM. Finally, we forecast the next possible attack sequence with the Viterbi algorithm of HMM. The results of simulation experiments show that the hidden Markov models which have been trained are better than the untrained in recognition and prediction.
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8

Jensen, Jens Ledet. "A Note on the Linear Memory Baum-Welch Algorithm." Journal of Computational Biology 16, no. 9 (2009): 1209–10. http://dx.doi.org/10.1089/cmb.2008.0178.

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9

El annas, Monir, Mohamed Ouzineb, and Badreddine Benyacoub. "Hidden Markov Models Training Using Hybrid Baum Welch - Variable Neighborhood Search Algorithm." Statistics, Optimization & Information Computing 10, no. 1 (2022): 160–70. http://dx.doi.org/10.19139/soic-2310-5070-1213.

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Hidden Markov Models (HMM) are used in a wide range of artifificial intelligence applications including speech recognition, computer vision, computational biology and fifinance. Estimating an HMM parameters is often addressed via the Baum-Welch algorithm (BWA), but this algorithm tends to convergence to local optimum of the model parameters. Therefore, optimizing HMM parameters remains a crucial and challenging work. In this paper, a Variable Neighborhood Search (VNS) combined with Baum-Welch algorithm (VNS-BWA) is proposed. The idea is to use VNS to escape from local minima, enable greater exploration of the search space, and enhance the learning capability of HMMs models. The proposed algorithm has entire advantage of combination of the search mechanism in VNS algorithm for training with no gradient information, and the BWA algorithm that utilizes this kind of knowledge. The performance of the proposed method is validated on a real dataset. The results show that the VNS-BWA has better performance fifinding the optimal parameters of HMM models, enhancing its learning capability and classifification performance.
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10

Anandhalekshmi, A. V., V. Srinivasa Rao, and G. R. Kanagachidambaresan. "Hybrid approach of baum-welch algorithm and SVM for sensor fault diagnosis in healthcare monitoring system." Journal of Intelligent & Fuzzy Systems 42, no. 4 (2022): 2979–88. http://dx.doi.org/10.3233/jifs-210615.

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Internet of Things (IoT) based healthcare monitoring system is becoming the present and the future of the medical field around the world. Here the monitoring system acquires the regular health details of hospital discharged patients like elderly patients, patients out of critical operations, and patients from remote areas, etc., and transmits it to the doctors. But the system is highly susceptible to sensor faults. Hence a data-driven hybrid approach of Hidden Markov Model (HMM) based on baum-welch algorithm with Support Vector Machine (SVM) is proposed to predict the abnormality caused by the medical sensors. The proposed work first perform the abnormality detection on the sensor data using the HMM based on baum-welch algorithm in which the normal data is separated from abnormal data followed by classifying the abnormal data as critical patient data or sensor fault data using the SVM. Here the proposed work efficiently performs fault diagnosis with an overall accuracy of 99.94% which is 0.59% better than the existing SVM model. And also a comparison is made between the hybrid approach and the existing ML algorithms in terms of recall and F1-score where the proposed approach outperforms the other algorithms with a recall value of 100% and F1-score of 99.7%.
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11

Shiping Du, Houhui Huang, and Yuming Wei. "The Baum-Welch Algorithm of Mixture Coupled Hidden Markov Model." Journal of Convergence Information Technology 8, no. 1 (2013): 620–27. http://dx.doi.org/10.4156/jcit.vol8.issue1.76.

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12

MURAKAMI, Jin'ichi. "Step by Step the Baum-Welch Algorithm and its Application." IEICE ESS FUNDAMENTALS REVIEW 4, no. 1 (2010): 48–56. http://dx.doi.org/10.1587/essfr.4.48.

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13

Rezaei, Vahid, Hamid Pezeshk, and Horacio Pérez-Sa'nchez. "Generalized Baum-Welch Algorithm Based on the Similarity between Sequences." PLoS ONE 8, no. 12 (2013): e80565. http://dx.doi.org/10.1371/journal.pone.0080565.

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14

Holmes, Ian. "Using evolutionary Expectation Maximization to estimate indel rates." Bioinformatics 21, no. 10 (2005): 2294–300. http://dx.doi.org/10.1093/bioinformatics/bti177.

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Abstract Motivation The Expectation Maximization (EM) algorithm, in the form of the Baum–Welch algorithm (for hidden Markov models) or the Inside-Outside algorithm (for stochastic context-free grammars), is a powerful way to estimate the parameters of stochastic grammars for biological sequence analysis. To use this algorithm for multiple-sequence evolutionary modelling, it would be useful to apply the EM algorithm to estimate not only the probability parameters of the stochastic grammar, but also the instantaneous mutation rates of the underlying evolutionary model (to facilitate the development of stochastic grammars based on phylogenetic trees, also known as Statistical Alignment). Recently, we showed how to do this for the point substitution component of the evolutionary process; here, we extend these results to the indel process. Results We present an algorithm for maximum-likelihood estimation of insertion and deletion rates from multiple sequence alignments, using EM, under the single-residue indel model owing to Thorne, Kishino and Felsenstein (the ‘TKF91’ model). The algorithm converges extremely rapidly, gives accurate results on simulated data that are an improvement over parsimonious estimates (which are shown to underestimate the true indel rate), and gives plausible results on experimental data (coronavirus envelope domains). Owing to the algorithm's close similarity to the Baum–Welch algorithm for training hidden Markov models, it can be used in an ‘unsupervised’ fashion to estimate rates for unaligned sequences, or estimate several sets of rates for sequences with heterogenous rates. Availability Software implementing the algorithm and the benchmark is available under GPL from http://www.biowiki.org/ Contact ihh@berkeley.edu
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15

Liu, Hong Zhi, and Li Gao. "Quality Control of Information Engineering Surveillance Based on Baum-Welch Algorithm." Applied Mechanics and Materials 63-64 (June 2011): 178–81. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.178.

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A new method of Quality Control for Information Engineering Surveillance based on Hidden Markov Model (HMM) has been proposed and the related model been built by us. The process of information engineering quality surveillance can be seen as a two-layered random process. The five elements of HMM correspond with the process of quality surveillance through abstracting the characteristics of the surveillance process. Software quality can be estimated under the model. In this paper, we divided the five elements. Therefore, the model was improved from single dimension to multi-dimension, trained by Baum-Welch algorithm. Experimental results show that the proposed model proves to be feasible and real-time when it is used for quality control.
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16

Baldi, Pierre, and Yves Chauvin. "Smooth On-Line Learning Algorithms for Hidden Markov Models." Neural Computation 6, no. 2 (1994): 307–18. http://dx.doi.org/10.1162/neco.1994.6.2.307.

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A simple learning algorithm for Hidden Markov Models (HMMs) is presented together with a number of variations. Unlike other classical algorithms such as the Baum-Welch algorithm, the algorithms described are smooth and can be used on-line (after each example presentation) or in batch mode, with or without the usual Viterbi most likely path approximation. The algorithms have simple expressions that result from using a normalized-exponential representation for the HMM parameters. All the algorithms presented are proved to be exact or approximate gradient optimization algorithms with respect to likelihood, log-likelihood, or cross-entropy functions, and as such are usually convergent. These algorithms can also be casted in the more general EM (Expectation-Maximization) framework where they can be viewed as exact or approximate GEM (Generalized Expectation-Maximization) algorithms. The mathematical properties of the algorithms are derived in the appendix.
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17

Wu, Li Ming, Qi Li, and Si Cheng Chen. "Research of Multi-Touch Projection Camera Interaction Techniques Based on SOPC." Applied Mechanics and Materials 543-547 (March 2014): 784–87. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.784.

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Projection-camera interaction is a human-computer interaction technology which uses projector-camera as input-output devices, and combined with human movement as interactive mode. Introducting radial distortion and tangential distortion of lens to projection-geometric calibrate the camera system, the platform is used the Xilinx main processor Virtex-4 FX experimental box to building a projection-camera interactive implementation platform, and used the Baum-Welch algorithm which based on hidden Markov model for training gesture.
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18

Long, Hai Xia, Li Hua Wu, and Yu Zhang. "Multiple Sequence Alignment Based on Profile Hidden Markov Model and Quantum-Behaved Particle Swarm Optimization with Selection Method." Advanced Materials Research 282-283 (July 2011): 7–12. http://dx.doi.org/10.4028/www.scientific.net/amr.282-283.7.

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Multiple sequence alignment (MSA) is an NP-complete and important problem in bioinformatics. Currently, profile hidden Markov model (HMM) is widely used for multiple sequence alignment. In this paper, Quantum-behaved Particle Swarm Optimization with selection operation (SQPSO) is presented, which is used to train profile HMM. Furthermore, an integration algorithm based on the profile HMM and SQPSO for the MSA is constructed. The approach is examined by using multiple nucleotides and protein sequences and compared with other algorithms. The results of the comparisons show that the HMM trained with SQPSO and QPSO yield better alignments than other most commonly used HMM training methods such as Baum–Welch and PSO.
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19

Kaleh, G. K. "The Baum-Welch algorithm for the detection of time-unsynchronized rectangular PAM signals." IEEE Transactions on Communications 42, no. 2/3/4 (1994): 260–62. http://dx.doi.org/10.1109/tcomm.1994.577027.

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20

Baggenstoss, P. M. "A modified Baum-Welch algorithm for hidden Markov models with multiple observation spaces." IEEE Transactions on Speech and Audio Processing 9, no. 4 (2001): 411–16. http://dx.doi.org/10.1109/89.917686.

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21

Li, Jia, and Lin Lin. "Baum-Welch algorithm on directed acyclic graph for mixtures with latent Bayesian networks." Stat 6, no. 1 (2017): 303–14. http://dx.doi.org/10.1002/sta4.158.

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Zhang, Yingjun, Wen Liu, Xuefeng Yang, and Shengwei Xing. "Hidden Markov Model-based Pedestrian Navigation System using MEMS Inertial Sensors." Measurement Science Review 15, no. 1 (2015): 35–43. http://dx.doi.org/10.1515/msr-2015-0006.

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Abstract In this paper, a foot-mounted pedestrian navigation system using MEMS inertial sensors is implemented, where the zero-velocity detection is abstracted into a hidden Markov model with 4 states and 15 observations. Moreover, an observations extraction algorithm has been developed to extract observations from sensor outputs; sample sets are used to train and optimize the model parameters by the Baum-Welch algorithm. Finally, a navigation system is developed, and the performance of the pedestrian navigation system is evaluated using indoor and outdoor field tests, and the results show that position error is less than 3% of total distance travelled.
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23

Benmachiche, A., A. Makhlouf, and T. Bouhadada. "Optimization learning of hidden Markov model using the bacterial foraging optimization algorithm for speech recognition." International Journal of Knowledge-based and Intelligent Engineering Systems 24, no. 3 (2020): 171–81. http://dx.doi.org/10.3233/kes-200039.

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Nowadays, the speech recognition applications can be found in several activities, and their existence as a field of study and research lasts for a long time. Although, many studies deal with different problems, in security-related areas, biometric identification, access to the Smartphone… Etc. In automatic speech recognition (ASR) systems, hidden Markov models (HMMs) have widely used for modeling the temporal speech signal. In order to optimize HMM parameters (i.e., observation and transition probabilities), iterative algorithms commonly used such as Forward-Backward or Baum-Welch. In this article, we propose to use the bacterial foraging optimization algorithm (BFOA) to enhance HMM with Gaussian mixture densities. As a global optimization algorithm of current interest, BFOA has proven itself for distributed optimization and control. Our experimental results show that the proposed approach yields a significant improvement of the transcription accuracy at signal/noise ratios greater than 15 dB.
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Beaulac, Cédric, and Fabrice Larribe. "Narrow Artificial Intelligence with Machine Learning for Real-Time Estimation of a Mobile Agent’s Location Using Hidden Markov Models." International Journal of Computer Games Technology 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/4939261.

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We propose to use a supervised machine learning technique to track the location of a mobile agent in real time. Hidden Markov Models are used to build artificial intelligence that estimates the unknown position of a mobile target moving in a defined environment. This narrow artificial intelligence performs two distinct tasks. First, it provides real-time estimation of the mobile agent’s position using the forward algorithm. Second, it uses the Baum–Welch algorithm as a statistical learning tool to gain knowledge of the mobile target. Finally, an experimental environment is proposed, namely, a video game that we use to test our artificial intelligence. We present statistical and graphical results to illustrate the efficiency of our method.
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Ocaña, M., L. M. Bergasa, M. A. Sotelo, R. Flores, D. F. Llorca, and D. Schleicher. "Automatic training method applied to a WiFi+ultrasound POMDP navigation system." Robotica 27, no. 7 (2009): 1049–61. http://dx.doi.org/10.1017/s0263574709005463.

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SUMMARYThis paper presents an automatic training method based on the Baum–Welch algorithm (also known as EM algorithm) and a robust low-level controller. The method has been applied to the indoor autonomous navigation of a surveillance robot that utilizes a WiFi+Ultrasound Partially Observable Markov Decision Process (POMDP). This method uses a robust local navigation system to automatically provide some WiFi+Ultrasound maps. These maps could be employed within probabilistic global robot localization systems. These systems use a priori probabilistic map in order to estimate the global robot position. The method has been tested in a real environment using two commercial Pioneer 2AT robotic platforms in the premises of the Department of Electronics at the University of Alcalá. Some experimental results and conclusions are presented.
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Darby, John, Baihua Li, and Nick Costen. "Activity Classification for Interactive Game Interfaces." International Journal of Computer Games Technology 2008 (2008): 1–7. http://dx.doi.org/10.1155/2008/751268.

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We present a technique for modeling and recognising human activity from moving light displays using hidden Markov models. We extract a small number of joint angles at each frame to form a feature vector. Continuous hidden Markov models are then trained with the resulting time series, one for each of a variety of human activity, using the Baum-Welch algorithm. Motion classification is then attempted by evaluation of the forward variable for each model using previously unseen test data. Experimental results based on real-world human motion capture data demonstrate the performance of the algorithm and some degree of robustness to data noise and human motion irregularity. This technique has potential applications in activity classification for gesture-based game interfaces and character animation.
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Sassi, Imad, Samir Anter, and Abdelkrim Bekkhoucha. "A New Improved Baum-Welch Algorithm for Unsupervised Learning for Continuous-Time HMM Using Spark." International Journal of Intelligent Engineering and Systems 13, no. 1 (2020): 214–26. http://dx.doi.org/10.22266/ijies2020.0229.20.

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28

Puengnim, Anchalee, Nathalie Thomas, Jean-Yves Tourneret, and Josep Vidal. "Classification of linear and non-linear modulations using the Baum–Welch algorithm and MCMC methods." Signal Processing 90, no. 12 (2010): 3242–55. http://dx.doi.org/10.1016/j.sigpro.2010.05.030.

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29

Qi, Yue. "Research on badminton action feature recognition based on improved HMM model." Journal of Intelligent & Fuzzy Systems 39, no. 4 (2020): 5571–82. http://dx.doi.org/10.3233/jifs-189038.

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The badminton movement speed is fast, and the movement is complicated. Therefore, it is difficult to effectively recognize the athlete’s movement through the monitoring level in the competition and training, which makes it difficult for the athlete to effectively improve his skill. In order to effectively improve the training effect and the quality of the athletes, this study uses badminton as the research object, analyzes the sports characteristics research algorithm through literature review, and finds the shortcomings of traditional algorithms. At the same time, this paper combines the actual situation to improve the algorithm and combines GMM and HMM to builds the GMM-HMM model. In addition, this paper uses the Baum-Welch unsupervised learning algorithm for data processing, and based on the learning machine training, the recognition results are obtained. Finally, in order to verify the validity of the model, this study uses the mobile phone badminton action as the data foundation and performs training recognition in the model to summarize the recognition results. The research shows that the algorithm has good performance and can meet the actual needs and can be used as a reference for the subsequent related research corporal punishment theory.
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Sato, T., and Y. Kameya. "Parameter Learning of Logic Programs for Symbolic-Statistical Modeling." Journal of Artificial Intelligence Research 15 (December 1, 2001): 391–454. http://dx.doi.org/10.1613/jair.912.

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We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. definite clause programs containing probabilistic facts with a parameterized distribution. It extends the traditional least Herbrand model semantics in logic programming to distribution semantics, possible world semantics with a probability distribution which is unconditionally applicable to arbitrary logic programs including ones for HMMs, PCFGs and Bayesian networks. We also propose a new EM algorithm, the graphical EM algorithm, that runs for a class of parameterized logic programs representing sequential decision processes where each decision is exclusive and independent. It runs on a new data structure called support graphs describing the logical relationship between observations and their explanations, and learns parameters by computing inside and outside probability generalized for logic programs. The complexity analysis shows that when combined with OLDT search for all explanations for observations, the graphical EM algorithm, despite its generality, has the same time complexity as existing EM algorithms, i.e. the Baum-Welch algorithm for HMMs, the Inside-Outside algorithm for PCFGs, and the one for singly connected Bayesian networks that have been developed independently in each research field. Learning experiments with PCFGs using two corpora of moderate size indicate that the graphical EM algorithm can significantly outperform the Inside-Outside algorithm.
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He, Lei, Hai Ou Xiang, Dong Xue Chen, and Chang Fu Zong. "Emergency Obstacle Avoidance Control Method Based on Driver Steering Intention Recognition for Steer-by-Wire Vehicle." Advanced Materials Research 694-697 (May 2013): 2738–41. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.2738.

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This paper proposes an emergency obstacle avoidance control method based on driver steering intention recognition for steer-by-wire vehicle in order to solve the problem that the response rate and stability time are unsatisfactory. The paper focuses on the method to recognize driver steering intention, and builds a driver steering behavior model by using the multidimensional Gauss HMM theory, optimizes the model by using the Baum-Welch algorithm and conducts real-time verification on steering intention recognition by means of LabVIEW and driving simulator. The results indicate that the driver steering intention recognition method has higher recognition accuracy and can help to realize emergency obstacle avoidance control effectively for steer-by-wire vehicle.
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32

Lindberg, David Volent, and Henning Omre. "Inference of the Transition Matrix in Convolved Hidden Markov Models and the Generalized Baum–Welch Algorithm." IEEE Transactions on Geoscience and Remote Sensing 53, no. 12 (2015): 6443–56. http://dx.doi.org/10.1109/tgrs.2015.2440415.

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33

Du, Shi Ping, Jian Wang, and Yu Ming Wei. "The Learning Algorithms of Coupled Discrete Hidden Markov Models." Applied Mechanics and Materials 411-414 (September 2013): 2106–10. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.2106.

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A hidden Markov model (HMM) encompasses a large class of stochastic process models and has been successfully applied to a number of scientific and engineering problems, including speech and other pattern recognition problems, and biological sequence analysis. A major restriction is found, however, in conventional HMM, i.e., it is ill-suited to capture the interactions among different models. A variety of coupled hidden Markov models (CHMMs) have recently been proposed as extensions of HMM to better characterize multiple interdependent sequences. The resulting models have multiple state variables that are temporally coupled via matrices of conditional probabilities. This paper study is focused on the coupled discrete HMM, there are two state variables in the network. By generalizing forward-backward algorithm, Viterbi algorithm and Baum-Welch algorithm commonly used in conventional HMM to accommodate two state variables, several new formulae solving the 2-chain coupled discrete HMM probability evaluation, decoding and training problem are theoretically derived.
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34

Sujatha, R., and T. M. Rajalaxmi. "Hierarchical Fuzzy Hidden Markov Chain for Web Applications." International Journal of Information Technology & Decision Making 15, no. 01 (2016): 83–118. http://dx.doi.org/10.1142/s0219622015500376.

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Fuzzy sets, a scheme for handling nonstatistical vague concepts, provide a natural basis for the theory of possibility space. In this paper, on possibility space, a hierarchical generalization of the fuzzy hidden Markov chain (HFHMC) which is named as FHMC is proposed. For the proposed model, three problems which naturally arise in any kind of hidden Markov models (HMMs) are discussed. To solve these problems, generalized Baum–Welch and generalized Viterbi algorithms are formulated; further it is observed that the generalized Viterbi algorithm itself solves the first two problems namely the likelihood of a given observation sequence and finding the most likelihood state sequence, which exhibits that the time complexity involved in the computation of two problems reduces to a single problem. In order to ensure the ease of models use, the proposed model is applied to our institution website and simulation is performed to analyze the accessibility of the website among the users.
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35

Baggenstoss, Paul M. "System and method for training a class-specific hidden markov model using a modified baum-welch algorithm." Journal of the Acoustical Society of America 113, no. 4 (2003): 1793. http://dx.doi.org/10.1121/1.1572369.

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36

Bengio, Y., and P. Frasconi. "Diffusion of Context and Credit Information in Markovian Models." Journal of Artificial Intelligence Research 3 (October 1, 1995): 249–70. http://dx.doi.org/10.1613/jair.233.

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This paper studies the problem of ergodicity of transition probability matrices in Markovian models, such as hidden Markov models (HMMs), and how it makes very difficult the task of learning to represent long-term context for sequential data. This phenomenon hurts the forward propagation of long-term context information, as well as learning a hidden state representation to represent long-term context, which depends on propagating credit information backwards in time. Using results from Markov chain theory, we show that this problem of diffusion of context and credit is reduced when the transition probabilities approach 0 or 1, i.e., the transition probability matrices are sparse and the model essentially deterministic. The results found in this paper apply to learning approaches based on continuous optimization, such as gradient descent and the Baum-Welch algorithm.
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37

Li, Jingdong, Zhangang Wang, Liankun Sun, and Wanru Wang. "Modeling and Analysis of Network Control System Based on Hierarchical Coloured Petri Net and Markov Chain." Discrete Dynamics in Nature and Society 2021 (June 8, 2021): 1–11. http://dx.doi.org/10.1155/2021/9948855.

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This paper investigates a modified modeling of networked control systems (NCSs) with programmable logic controller (PLC). First, the controller-to-actuator and sensor-to-controller network-induced delays are investigated by a modeling tactics based on hierarchical coloured petri net (HCPN) in a structure-conserving way. Comparing with the recent result, the signal transmission delay is set in a random interval instead of a fixed mode; moreover, the data packet drop out and disorder are also taken into consideration. Second, delays captured form CPN tools are analyzed with a strategy based on Baum–Welch algorithm and statistics science. Besides, time delays are modeled as a Markov chain and the transition probabilities is calculated using the consequent from the previous operation. Finally, a comparison verification illustrates the equivalence property between proposed models.
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38

Tumilaar, Kezia, Yohanes Langi, and Altien Rindengan. "Hidden Markov Model." d'CARTESIAN 4, no. 1 (2015): 86. http://dx.doi.org/10.35799/dc.4.1.2015.8104.

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Hidden Markov Models (HMM) is a stochastic model and is essentially an extension of Markov Chain. In Hidden Markov Model (HMM) there are two types states: the observable states and the hidden states. The purpose of this research are to understand how hidden Markov model (HMM) and to understand how the solution of three basic problems on Hidden Markov Model (HMM) which consist of evaluation problem, decoding problem and learning problem. The result of the research is hidden Markov model can be defined as . The evaluation problem or to compute probability of the observation sequence given the model P(O|) can solved by Forward-Backward algorithm, the decoding problem or to choose hidden state sequence which is optimal can solved by Viterbi algorithm and learning problem or to estimate hidden Markov model parameter to maximize P(O|) can solved by Baum – Welch algorithm. From description above Hidden Markov Model with state 3 can describe behavior from the case studies. Key words: Decoding Problem, Evaluation Problem, Hidden Markov Model, Learning Problem
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Jatipaningrum, Maria Titah, Kris Suryowati, and Libertania Maria Melania Esti Un. "Prediksi Kurs Rupiah Terhadap Dolar Dengan FTS-Markov Chain Dan Hidden Markov Model." Jurnal Derivat: Jurnal Matematika dan Pendidikan Matematika 6, no. 1 (2019): 32–41. http://dx.doi.org/10.31316/j.derivat.v6i1.334.

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Hidden Markov model is a development of the Markov chain where the state cannot be observed directly (hidden), but can only be observed, a set of other observations and combination of fuzzy logic and Markov chain to predict Rupiah exchange rate against the Dollar. The exchange rate of purchasing and exchange rate of saling is divided into four states, namely down large, down small, small rise, and large rise are symbolized respectively S1, S2, S3, and S4. Probability of sequences of observation for 3 days later is computed by forwarding and Backward Algorithm, determine the hidden state sequence using the viterbi algorithm and estimate the HMM parameters using the Baum Welch algorithm. The MAPE result exchange rate of purchase of FTS-Markov Chain is 1,355% and the exchange rate of sale of FTS-Markov Chain is 1,317%. The sequences of observation which optimized within exchange rate of purchase is X* = {S3,S3,S3}, within exchange rate of sale is also X* = {S3,S3,S3}. Keywords: Exchange rate, FTS-Markov Chain, Hidden Markov Model
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Zhang, Chun Liang, and Li Ping Chen. "Real Time Monitoring of Cutting Chatter Based on Fuzzy Hidden Markov Models." Materials Science Forum 532-533 (December 2006): 1160–63. http://dx.doi.org/10.4028/www.scientific.net/msf.532-533.1160.

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The full automation of machine tools has gained substantial importance in manufacturing industries in recent years, as machining technology has progressed from manually operated production machines to highly advanced and sophisticated CNC machine tool. Whereas manufacturing technology has moved to the stage of automation, there is still an unsolved problem in metal cutting processes: cutting chatter. Due to its complexity, thus cutting chatter is still the primary problem in metal cutting processes. According to the characteristic of cutting chatter, a real time monitoring technique of cutting chatter based on fuzzy hidden Markov model (FHMM) was presented. Hidden Markov model (HMM) is a state-of-the-art technique for speech recognition because of its elegant mathematical structure and the availability of computer implementation of these models. In this paper, the fuzzy EM algorithm was used to the Baum-Welch algorithm in the HMM method, and the strategy of time frequency feature extraction to non-stability signal was described. The experimental results show that the proposed method is feasible and effective for the monitoring of cutting chatter in the metal cutting processes.
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Liu, Tingting, and Jan Lemeire. "Efficient and Effective Learning of HMMs Based on Identification of Hidden States." Mathematical Problems in Engineering 2017 (2017): 1–26. http://dx.doi.org/10.1155/2017/7318940.

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The predominant learning algorithm for Hidden Markov Models (HMMs) is local search heuristics, of which the Baum-Welch (BW) algorithm is mostly used. It is an iterative learning procedure starting with a predefined size of state spaces and randomly chosen initial parameters. However, wrongly chosen initial parameters may cause the risk of falling into a local optimum and a low convergence speed. To overcome these drawbacks, we propose to use a more suitable model initialization approach, a Segmentation-Clustering and Transient analysis (SCT) framework, to estimate the number of states and model parameters directly from the input data. Based on an analysis of the information flow through HMMs, we demystify the structure of models and show that high-impact states are directly identifiable from the properties of observation sequences. States having a high impact on the log-likelihood make HMMs highly specific. Experimental results show that even though the identification accuracy drops to 87.9% when random models are considered, the SCT method is around 50 to 260 times faster than the BW algorithm with 100% correct identification for highly specific models whose specificity is greater than 0.06.
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Chang, Liu, Yacine Ouzrout, Antoine Nongaillard, Abdelaziz Bouras, and Zhou Jiliu. "The Reputation Evaluation Based on Optimized Hidden Markov Model in E-Commerce." Mathematical Problems in Engineering 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/391720.

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Nowadays, a large number of reputation systems have been deployed in practical applications or investigated in the literature to protect buyers from deception and malicious behaviors in online transactions. As an efficient Bayesian analysis tool, Hidden Markov Model (HMM) has been used into e-commerce to describe the dynamic behavior of sellers. Traditional solutions adopt Baum-Welch algorithm to train model parameters which is unstable due to its inability to find a globally optimal solution. Consequently, this paper presents a reputation evaluation mechanism based on the optimized Hidden Markov Model, which is called PSOHMM. The algorithm takes full advantage of the search mechanism in Particle Swarm Optimization (PSO) algorithm to strengthen the learning ability of HMM and PSO has been modified to guarantee interval and normalization constraints in HMM. Furthermore, a simplified reputation evaluation framework based on HMM is developed and applied to analyze the specific behaviors of sellers. The simulation experiments demonstrate that the proposed PSOHMM has better performance to search optimal model parameters than BWHMM, has faster convergence speed, and is more stable than BWHMM. Compared with Average and Beta reputation evaluation mechanism, PSOHMM can reflect the behavior changes of sellers more quickly in e-commerce systems.
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43

Micchelli, Charles A., and Peder Olsen. "Penalized maximum-likelihood estimation, the Baum–Welch algorithm, diagonal balancing of symmetric matrices and applications to training acoustic data." Journal of Computational and Applied Mathematics 119, no. 1-2 (2000): 301–31. http://dx.doi.org/10.1016/s0377-0427(00)00385-x.

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44

Abdolhossein Harisi, Rashin, and Hamid Reza Kobravi. "A Hidden Markov Model Based Detecting Solution for Detecting the Situation of Balance During Unsupported Standing Using the Electromyography of Ankle Muscles." International Clinical Neuroscience Journal 9, no. 1 (2022): e3-e3. http://dx.doi.org/10.34172/icnj.2022.03.

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Background: In this study, three detecting approaches have been proposed and evaluated for online detection of balance situations during quiet standing. The applied methods were based on electromyography of the gastrocnemius muscles adopting the hidden Markov models. Methods: The levels of postural stability during quiet standing were regarded as the hidden states of the Markov models while the zones in which the center of pressure lies within determines the level of stability. The Markov models were trained by using the well-known Baum-Welch algorithm. The performance of a single hidden Markov model, the multiple hidden Markov model, and the multiple hidden Markov model alongside an adaptive neuro-fuzzy inference system (ANFIS), were compared as three different detecting methods. Results: The obtained results show the better and more promising performance of the method designed based on a combination of the hidden Markov models and optimized neuro-fuzzy system. Conclusion: According to the results, using the combined detecting method yielded promising results.
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45

Oyelade, Jelili, Itunuoluwa Isewon, Damilare Olaniyan, Solomon O. Rotimi, and Jumoke Soyemi. "Effectiveness of model-based clustering in analyzing Plasmodium falciparum RNA-seq time-course data." F1000Research 6 (September 19, 2017): 1706. http://dx.doi.org/10.12688/f1000research.12360.1.

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Background: The genomics and microarray technology played tremendous roles in the amount of biologically useful information on gene expression of thousands of genes to be simultaneously observed. This required various computational methods of analyzing these amounts of data in order to discover information about gene function and regulatory mechanisms. Methods: In this research, we investigated the usefulness of hidden markov models (HMM) as a method of clustering Plasmodium falciparum genes that show similar expression patterns. The Baum-Welch algorithm was used to train the dataset to determine the maximum likelihood estimate of the Model parameters. Cluster validation was conducted by performing a likelihood ratio test. Results: The fitted HMM was able to identify 3 clusters from the dataset and sixteen functional enrichment in the cluster set were found. This method efficiently clustered the genes based on their expression pattern while identifying erythrocyte membrane protein 1 as a prominent and diverse protein in P. falciparum. Conclusion: The ability of HMM to identify 3 clusters with sixteen functional enrichment from the 2000 genes makes this a useful method in functional cluster analysis for P. falciparum
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46

Oyelade, Jelili, Itunuoluwa Isewon, Damilare Olaniyan, Solomon O. Rotimi, and Jumoke Soyemi. "Effectiveness of model-based clustering in analyzing Plasmodium falciparum RNA-seq time-course data." F1000Research 6 (May 25, 2018): 1706. http://dx.doi.org/10.12688/f1000research.12360.2.

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Background: The genomics and microarray technology played tremendous roles in the amount of biologically useful information on gene expression of thousands of genes to be simultaneously observed. This required various computational methods of analyzing these amounts of data in order to discover information about gene function and regulatory mechanisms. Methods: In this research, we investigated the usefulness of hidden markov models (HMM) as a method of clustering Plasmodium falciparum genes that show similar expression patterns. The Baum-Welch algorithm was used to train the dataset to determine the maximum likelihood estimate of the Model parameters. Cluster validation was conducted by performing a likelihood ratio test. Results: The fitted HMM was able to identify 3 clusters from the dataset and sixteen functional enrichment in the cluster set were found. This method efficiently clustered the genes based on their expression pattern while identifying erythrocyte membrane protein 1 as a prominent and diverse protein in P. falciparum. Conclusion: The ability of HMM to identify 3 clusters with sixteen functional enrichment from the 2000 genes makes this a useful method in functional cluster analysis for P. falciparum
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47

Jiang, Zhiwei, Xiaoqing Ding, Liangrui Peng, and Changsong Liu. "Exploring More Representative States of Hidden Markov Model in Optical Character Recognition: A Clustering-Based Model Pre-Training Approach." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 03 (2015): 1550014. http://dx.doi.org/10.1142/s0218001415500147.

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Hidden Markov Model (HMM) is an effective method to describe sequential signals in many applications. As to model estimation issue, common training algorithm only focuses on the optimization of model parameters. However, model structure influences system performance as well. Although some structure optimization methods are proposed, they are usually implemented as an independent module before parameter optimization. In this paper, the clustering feature of states in HMM is discussed through comparing the mechanism of Quadratic Discriminant Function (QDF) classifier and HMM. Then, through the clustering effect of Viterbi training and Baum–Welch training, a novel clustering-based model pre-training approach is proposed. It can optimize model parameters and model structure by turns, until the representative states of all models are explored. Finally, the proposed approach is evaluated on two typical OCR applications, printed and handwritten Arabic text line recognition. And it is compared with some other optimization methods. The improvement of character recognition performance proves the proposed approach can make more precise state allocation. And the representative states are benefit to HMM decoding.
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Ramadhan, Rahmawati, and Dodi Devianto. "A Hidden Markov Model for Forecasting Rainfall Data Availability at the Weather Station in West Sumatra." Science and Technology Indonesia 5, no. 2 (2020): 34. http://dx.doi.org/10.26554/sti.2020.5.2.34-40.

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Indonesia is a maritime continent in Southeast Asian, laying between Indian Ocean and Pacific Ocean. This position intensely affects the level of rainfall in Indonesia, especially West Sumatra. The availability of rainfall data can form a Markov chain which its state is not able to be observed directly (hidden), is called the Hidden Markov Model (HMM). The purposes of this research are to predict the hidden state of the availability of rainfall data using decoding problems and to find the best state sequence (optimal) by using Viterbi Algorithm, and also to predict probability for the availability of rainfall data in the future by using the Baum Welch Algorithm in the Hidden Markov Model. This research uses secondary data with a period of one day from the availability of rainfall data at the Minangkabau Meteorological Station, Padang Pariaman Climatology Station, and Silaing Bawah Geophysics Station from January 2018 to July 2019. The results of the prediction show that the Hidden Markov Model can be used to predict the probability of rainfall data availability. The results for the availability of the highest rainfall data for one day ahead is at the Padang Pariaman Climatology Station, with a probability of 0.36, followed by Minangkabau Meteorological Station is 0.35, and Silaing Bawah Geophysics station is 0.29. The result has shown for the next one day period the probability of rainfall data available from the three stations will be available following the Viterbi algorithm.
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49

Dong, Wenjie, Sifeng Liu, Zhigeng Fang, Yingsai Cao, and Ye Ding. "A model based on hidden graphic evaluation and review technique network to evaluate reliability and lifetime of multi-state systems." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 233, no. 3 (2018): 369–78. http://dx.doi.org/10.1177/1748006x18788414.

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The essence of multi-state system performance degradation is a process of deteriorating state transition. On the basis of hidden Markov model and graphic evaluation and review technique network, this article proposes a new reliability assessment method called hidden graphic evaluation and review technique network model for multi-state system. Specifically, nodes in graphic evaluation and review technique network represent hidden states of a system at different deteriorating times, and they can be expanded through a series of observable sequences. Baum–Welch algorithm in hidden Markov model is introduced to train parameters and when logarithmic likelihood function of the output reaches convergent, we can estimate the most probable output state and obtain the state transition probability eventually. Suppose performance degradation amount between different nodes follows gamma distribution, a method of improved maximum likelihood function is introduced to estimate parameters. According to signal flow graph theory and Mason formula, equivalent transfer function from the initial node to any other nodes can be obtained, then expectation and variance of performance degradation amount can be presented. In the real case study, we compare the reliability assessment method proposed in this article with other two traditional methods, which show the rationality of hidden graphic evaluation and review technique network model.
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Rajendran, Mahalakshmi, Senthamarai Kannan Kaliyaperumal, and Balasubramaniam Ramakrishnan. "Hidden Markov Model of Evaluation of Break-Even Point of HIV patients: A Simulation Study." International Journal of Medical Sciences and Nursing Research 1, no. 2 (2021): 19–22. http://dx.doi.org/10.55349/ijmsnr.2021121922.

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Background: The HIV virus carries projection of significant global population with specific estimations of the mathematical results of evolutionary methods which was presented in Tree Hidden Markov model (HMM). Materials and Methods: Hidden Markov models used to model the progression of the disease among HIV infected people. The author predicts a Baum Welch Algorithm method through HMM that can assess an unknown state of transition. Results: The Tree HMM model predicts the break down point starts once patient is infected with the HIV virus as it affects the immune system. The immune system drops more quickly in the initial inter arrival time when compared with the later time interval. The HIV virus length in the nth state within regrouping is uncertain to occur in each state of the given model. A simulation study was done to assess the goodness of fit for the model. Conclusion: The HIV virus length in the nth state within regrouping is uncertain to occur in each state of the given model. The inter arrival censoring between each state is essential in each infected HIV patients. The outcome of this works states that health care expert can use this model for effective patient cares. Keywords: expectation, hidden markov model, human immunodeficiency virus, immune system, transition
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