HMM parameters estimation using hybrid Baum-Welch genetic algorithm

Oudelha, Mourad; Ainon, Raja Noor

doi:10.1109/itsim.2010.5561388

Cited by 25 publications

(8 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If Ack packet is received, r Ck k = 1; otherwise, r Ck k = 0; 7. Update weights based on equations (35) and 36; types of jammers are introduced in section ''System and adversary model.'' The static jammer is not considered in the simulations because it is too easy to be avoided by the SUs.…”

Section: Performance Evaluation and Discussionmentioning

confidence: 99%

“…To improve the convergence of HMM estimation, genetic algorithm can be employed for the global search. 34,35 In this article, we combine the Baum-Welch algorithm with genetic algorithm for the two-state HMM estimation. Genetic algorithm implements the global model matching at first, and the Baum-Welch algorithm will use the result of genetic algorithm as the initial value.…”

Section: O(t )mentioning

confidence: 99%

See 1 more Smart Citation

Jamming-resilient algorithm for underwater cognitive acoustic networks

Wang

Fan

Zhang

et al. 2017

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Due to the limit spectrum resource in the underwater acoustic networks, underwater cognitive acoustic communication is a promising technique. The channel sharing mechanism in cognitive networks can improve the communication capacity efficiently. Jamming attack is a common deny of service attack in cognitive networks. In the underwater cognitive acoustic networks, the anti-jamming problem is quite different from cognitive radio networks. It calls for an effective antijamming strategy in the cognitive acoustic channel access. In this article, we propose an online learning anti-jamming algorithm called multi-armed bandit-based acoustic channel access algorithm to achieve the jamming-resilient cognitive acoustic communication. The imperfect channel sensing and the constraints of underwater acoustic communication are considered in the anti-jamming game. Under different kinds of jamming attacks, the channel utilization can be improved with our jamming-resilient approach.

show abstract

Section: Performance Evaluation and Discussionmentioning

confidence: 99%

Section: O(t )mentioning

confidence: 99%

Jamming-resilient algorithm for underwater cognitive acoustic networks

Wang

Fan

Zhang

et al. 2017

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

“…The following research works are focused on the speech recognition using HMMs, as in Oudelha et al [26] combining the Baum-Welch algorithm (BW); in Cheshomi et al [27] also uses the BW algorithm. Bhuriyakorn et al [28] present approaches for HMMs topologies generation, in Yang et al [29] address the optimization problem combining a Tabu search and BW algorithm; Yang et al [30] uses Particle Swarm Optimization (PSO) and GA on recognition performance, and in Ogawa et al [31] determine the structure of a Partly HMM with GA. -There is a work of Won et al [2] that presents the use of GA for evolving HMMs, used on information prediction of secondary structure for protein sequences.…”

Section: Parameters Optimization Of An Hmmmentioning

confidence: 99%

Network anomaly detection by continuous hidden markov models: An evolutionary programming approach

Flores

Calderón

Antolino

et al. 2015

IDA

View full text Add to dashboard Cite

Information security is an important and growing need. The most common schemes used for detection systems include pattern-or signature-based and anomaly-based. Anomaly-based schemes use a set of metrics, which outline the normal system behavior and any significant deviation from the established profile will be treated as an anomaly. This paper contributes with an anomaly-based scheme that monitors the bandwidth consumption of a subnetwork, at the Universidad Michoacana, in Mexico. A normal behavior model is based on bandwidth consumption of the subnetwork. The presence of an anomaly indicates that something is misusing the network (viruses, worms, denial of service, or any other kind of attack). This work also presents a scheme for an automatic architecture design and parameters optimization of Hidden Markov Models (HMMs), based on Evolutionary Programming (EP). The variables to be used by the HMMs are: the bandwidth consumption of network (IN and OUT), and the associated time where the network activity occurs. The system was tested with univariate and bivariate observation sequences to analyze and detect anomaly behavior. The HMMs, designed and trained by EP, were compared against semi-random HMMs trained by the Baum-Welch algorithm. On a second experiment, the HMMs, designed and trained by EP, were compared against HMMs created by an expert user. The HMMs outperformed the other methods in all cases. Finally, we made the HMMs time-aware, by including time as another variable. This inclusion made the HMMs capable of detecting activity patterns that are normal during a period of time but anomalous at other times. For instance, a heavy load on the network may be completely normal during working times, but anomalous at nights or weekends.speech and gesture recognition, as well as applications to anomaly detection. In an HMM, the estimation of good model parameters affects the performance of the recognition or detection processes [3]. The HMM parameters can be determined during an iterative process called "training". The Baum-Welch algorithm [4] is one method applied in setting an HMM's parameters, but this method has a drawback: being a gradient-based method, it may converge to a local optimum.Global search techniques can be used to optimize an HMM's parameters. Genetic Algorithms (GA) and Evolutionary Programming (EP) are global optimization techniques that can be used to optimize an HMM's parameters [3]. Studies of using GA to train HMMs are relatively rare, particularly in comparison with the large literature on applying GA to neural network training [2].In the study presented in this paper we want to know (given certain events or behaviors of the study objects), what is expected or what can be anticipated as output. That is, we are interested in studying the behavior of a set of random variables over time, working with stochastic processes. In other words, given the behavior of a computer network, the problem is to automatically develop an HMM capable of discerning between normal and anomalous behavior. This tas...

show abstract

“…Thus, we propose an HMM-based detection requiring no separate training data. Since the detection results from HMM are significantly affected by random initial parameters of HMM, a genetic algorithm (GA) is utilized to reduce the sensitivity of the initial parameters to the detection [ 15 ]. Furthermore, multiple measurements from array are exploited in the HMM-based detection to enhance accuracy and stability in finding the SOIs within sonar data.…”

Section: Introductionmentioning

confidence: 99%

Underwater Acoustic Signal Detection Using Calibrated Hidden Markov Model with Multiple Measurements

You

Byun

Choo

2022

Sensors

View full text Add to dashboard Cite

It is important to find signals of interest (SOIs) when operating sonar systems. A threshold-based method is generally used for SOI detection. However, it induces a high false alarm rate at a low signal-to-noise ratio. On the other side, machine-learning-based detection is performed to obtain more reliable detection results using abundant training data, costing intensive time and labor. We propose a method with favorable detection performance by using a hidden Markov model (HMM) for sequential acoustic data, which requires no separate training data. Since the detection results from HMM are significantly affected by the random initial parameters of HMM, the genetic algorithm (GA) is adopted to reduce the sensitivity of the initial parameters. The tuned initial parameters from GA are used as a start point for the subsequent Baum–Welch algorithm updating the HMM parameters. Furthermore, multiple measurements from arrays are exploited both in determining the proper initial parameters with GA and updating the parameters with the Baum–Welch algorithm. In contrast to the standard random selection of the initial point with single measurement, a stable initial point setting by the GA ensures improved SOI detections with the Baum–Welch algorithm using the multiple measurements, which are demonstrated in passive and active acoustic data. Particularly, the proposed method shows the most confidential detection in finding weak elastic surface waves from target, compared to existing methods such as conventional HMM.

show abstract

HMM parameters estimation using hybrid Baum-Welch genetic algorithm

Cited by 25 publications

References 8 publications

Jamming-resilient algorithm for underwater cognitive acoustic networks

Jamming-resilient algorithm for underwater cognitive acoustic networks

Network anomaly detection by continuous hidden markov models: An evolutionary programming approach

Underwater Acoustic Signal Detection Using Calibrated Hidden Markov Model with Multiple Measurements

Contact Info

Product

Resources

About