PSO Optimized Hidden Markov Model Performance Analysis for IEEE 802.16/WiMAX Standard

Kordnoori, Shaghayegh; Mostafaei, Hamidreza; Behzadi, Mohammad Hassan

doi:10.1007/s11277-019-06533-5

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…In order to avoid the local optimal problem caused by random initialization of HMM parameters, cuckoo algorithm is used to optimize the initial value of HMM model (Kordnoori et al, 2019). The optimal solution found by the CS algorithm is the initial parameter of the HMM.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, Zang et al used the genetic algorithm (GA) to obtain parameters of HMM (Ranjan & Mitra, 2017; Zang, ShangGuan, Wang, & Cai, 2016), and the experiment results showed that HMM based on the GA was more efficient in calculation and could quickly achieve the steady‐state training. Kordnoori, Mostafaei, and Behzadi (2019) optimized the HMM by the particle swarm optimization algorithm (PSO), and experimental results proved that HMM based on the PSO had better optimization ability and faster convergence rate than HMM based on the Baum–Welch algorithm. However, Sivanandam and Deepa (2008) found that GAs can only find acceptable good solution, but cannot guarantee to find the global optimal solution.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic risk assessment of food safety based on an improved hidden Markov model integrating cuckoo search algorithm: A sterilized milk study

Lin

Han

et al. 2021

J Food Process Engineering

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With the growth in the living standard of people, food safety and quality risk assessment has gradually become the focus. Therefore, this article realizes the dynamic risk assessment of food safety based on an improved hidden Markov model (HMM) integrating the cuckoo search algorithm (CS) (CS‐HMM). The CS is used to conduct global search to obtain the initial value of HMM, and then the Baum–Welch algorithm is used for modifying the initial value to obtain a trained risk assessment model. Finally, in terms of several benchmark functions, the accuracy of the CS algorithm is better than the PSO and the GA algorithms for seeking global optimal solution. Moreover, the presented method is applied to evaluate the safety risk of the sterilized milk production. Combined with the characteristics of time and temperature, the deterioration reasons of sterilized milk are analyzed. Moreover, the proposed method has certain theoretical significance and practical value for improving the ability of food quality risk assessment and formulating control measures in advance. Practical applications The presented method is applied to evaluate the safety risk of the sterilized milk production, which are supplied by the food inspection agency of Guizhou province in China. Combined with the characteristics of time and temperature, the deterioration reasons of sterilized milk are analyzed. Furthermore, the improved model breaks out of the local optimal limit, so as to better realize the risk prediction of food and obtain better evaluation results. Based on the analysis of dynamic risk assessment result with the temperature characteristics, the future risk trend can be predicted. Meanwhile, the causes affecting the deterioration of the sterilized milk and the direction to improve the quality of the sterilized milk can be obtained. In this way, risk control measures can be taken more precisely and risks can be reduced more effectively, which is helpful to the production and storage of food products.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic risk assessment of food safety based on an improved hidden Markov model integrating cuckoo search algorithm: A sterilized milk study

Lin

Han

et al. 2021

J Food Process Engineering

View full text Add to dashboard Cite

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“…The received symbol can be observed, but the state in which an error happens is not observable. HMMs were used for modeling the statistics of burst errors in the communication channel [24][25][26][27][28][29][30] . The advantage of HMM compared to the standard wireless channel simulation is its high advance in simulation time.…”

Section: Figurementioning

confidence: 99%

Evaluating the CDMA System Using Hidden Markov and Semi Hidden Markov Models

Kordnoori

Mostafaei

Kordnoori

et al. 2021

JTS

Self Cite

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CDMA is an important and basic part of today's communications technologies. This technology can be analyzed efficiently by reducing the time, computation burden, and cost by characterizing the physical layer with a Markov Model. Waveform level simulation is generally used for simulating different parts of a digital communication system. In this paper, we introduce two different mathematical methods to model digital communication channels. Hidden Markov and Semi Hidden Markov models' applications have been investigated for evaluating the DS-CDMA link performance with different parameters. Hidden Markov Models have been a powerful mathematical tool that can be applied as models of discrete-time series in many fields successfully. A semi-hidden Markov model as a stochastic process is a modification of hidden Markov models with states that are no longer unobservable and less hidden. A principal characteristic of this mathematical model is statistical inertia, which admits the generation, and analysis of observation symbol contains frequent runs.

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“…Attempts to estimate HMMs parameters were carried out by several authors [9,10,11,12,13], but the dominant approach to HMM parameter estimation problem is the Baum-Welch algorithm [8], despite its use in practice, the Baum-Welch algorithm can get trapped in local optima of the model parameters. Thus there is need for an algorithm that can escape from the local optimum and then probe the solution space to reach the global optimum of the model parameters.…”

Section: Introductionmentioning

confidence: 99%

Hidden Markov Models Training Using Hybrid Baum Welch - Variable Neighborhood Search Algorithm

Annas

Ouzineb²,

Benyacoub³

2022

Stat., optim. inf. comput.

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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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PSO Optimized Hidden Markov Model Performance Analysis for IEEE 802.16/WiMAX Standard

Cited by 5 publications

References 15 publications

Dynamic risk assessment of food safety based on an improved hidden Markov model integrating cuckoo search algorithm: A sterilized milk study

Dynamic risk assessment of food safety based on an improved hidden Markov model integrating cuckoo search algorithm: A sterilized milk study

Evaluating the CDMA System Using Hidden Markov and Semi Hidden Markov Models

Hidden Markov Models Training Using Hybrid Baum Welch - Variable Neighborhood Search Algorithm

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