The Recognition Method of MQAM Signals Based on BP Neural Network and Bird Swarm Algorithm

Zhang, Chengchang; Sa, Yu; Li, Guojun; Yu, Xu

doi:10.1109/access.2021.3061585

Cited by 16 publications

(9 citation statements)

References 20 publications

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“…However, although such a translation method can obtain high-quality translation results, it has a long translation cycle and requires a lot of human and material resources. It cannot be frequently applied to people's daily lives and widely meet various translation needs [ 2 ]. Chen and Huang said that people began to focus on the use of computers to automatically translate different languages and texts, so as to achieve the goal of efficiently solving cross-language communication difficulties.…”

Section: Related Workmentioning

confidence: 99%

Machine English Translation Evaluation System Based on BP Neural Network Algorithm

Han¹,

Shaoxiu

2022

Computational Intelligence and Neuroscience

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In order to solve the problems of machine translation efficiency and translation quality, this paper proposes an English translation evaluation system based on the BP neural network algorithm. This method provides users with a more intelligent machine translation service experience. With the help of the BP neural network algorithm, taking English online translation as the research object, Google’s translation quality is the best, with an error frequency of only 167, while Baidu translation and iFLYTEK translation in China have a high error rate of 266 and 301, respectively, which is much higher than Google translation. A model of machine translation evaluation based on the neural network algorithm is proposed to better solve the disadvantages of traditional English machine translation. The results show that the machine translation system based on the neural network algorithm can further optimize the problems existing in machine translation, such as insufficient use of information and large scale of model parameters, and further improve the performance of neural network machine translation.

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Section: Related Workmentioning

confidence: 99%

Machine English Translation Evaluation System Based on BP Neural Network Algorithm

Han¹,

Shaoxiu

2022

Computational Intelligence and Neuroscience

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“…To evaluate the recognition quality of each model more comprehensively, some indicators are introduced: recall rate (TPR), Matthews correlation coefficient (MCC), precision rate (Pr), F-measure (F1) and recognition accuracy (ACC). Formulas ( 16), ( 17), ( 18), (19) and (20) show the calculation methods adopted for each indicator. The comparison of the performance indicators for different algorithms is shown in TABLE 3.…”

Section: ) Shock Recognitionmentioning

confidence: 99%

“…Though SVM can handle datasets with large sample sizes, it is sensitive to data and prone to overfitting. As a typical artificial neural network, BPNN [20]- [24] has been extensively used for mechanical fault diagnosis because of its superior nonlinear mapping ability. Nevertheless, typically, training parameters for the BPNN model are randomly selected.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Key Information in Non-Stationary Signals Based on Wavelet Threshold Denoising and Back Propagation Neural Network Optimized by Manta Ray Foraging Optimization Algorithm

Jia

Jing

2022

IEEE Access

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The identification of key information hidden in non-stationary signals is challenging in various fields such as logistics and transportation, biomedicine, and fault diagnosis. To facilitate this identification, we propose a back propagation neural network (BPNN) classification and recognition algorithm based on wavelet threshold denoising (WTD) and manta ray foraging optimization (MRFO) algorithm for the first time.The algorithm first performs WTD on the original signals to obtain denoised signals. Subsequently, the MRFO algorithm is utilized to optimize the initial weights and thresholds of the BPNN. On the base of this, the optimization model is finally obtained to classify and recognize the key information in the non-stationary signals. The comparative experimental results indicate that the proposed WTD-MRFO-BPNN algorithm can be utilized to availably recognize the key information hidden in non-stationary signals. The recognition accuracy reaches 97.25%.

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“…We also use other optimization algorithms to optimize BPNN further verify the effectiveness of the proposed algorithm, including the PSO-BPNN approach [21], GA-BPNN approach [22], BAS-BPNN approach [23], and EHO-BPNN approach. Fig.…”

Section: Performance Comparisons Of Qeha-bpnn With Other Optimal Algorithmmentioning

confidence: 99%

Evolutionary Neural Network based on Quantum Elephant Herding Algorithm for Modulation Recognition in Impulse Noise

2021

KSII TIIS

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In this paper, we proposed a novel modulation recognition method based on quantum elephant herding algorithm (QEHA) evolving neural network under impulse noise environment. We use the adaptive weight myriad filter to preprocess the received digital modulation signals which passing through the impulsive noise channel, and then the instantaneous characteristics and high order cumulant features of digital modulation signals are extracted as classification feature set, finally, the BP neural network (BPNN) model as a classifier for automatic digital modulation recognition. Besides, based on the elephant herding optimization (EHO) algorithm and quantum computing mechanism, we design a quantum elephant herding algorithm (QEHA) to optimize the initial thresholds and weights of the BPNN, which solves the problem that traditional BPNN is easy into local minimum values and poor robustness. The experimental results prove that the adaptive weight myriad filter we used can remove the impulsive noise effectively, and the proposed QEHA-BPNN classifier has better recognition performance than other conventional pattern recognition classifiers. Compared with other global optimization algorithms, the QEHA designed in this paper has a faster convergence speed and higher convergence accuracy. Furthermore, the effect of symbol shape has been considered, which can satisfy the need for engineering.

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The Recognition Method of MQAM Signals Based on BP Neural Network and Bird Swarm Algorithm

Cited by 16 publications

References 20 publications

Machine English Translation Evaluation System Based on BP Neural Network Algorithm

Machine English Translation Evaluation System Based on BP Neural Network Algorithm

Recognition of Key Information in Non-Stationary Signals Based on Wavelet Threshold Denoising and Back Propagation Neural Network Optimized by Manta Ray Foraging Optimization Algorithm

Evolutionary Neural Network based on Quantum Elephant Herding Algorithm for Modulation Recognition in Impulse Noise

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