Trigonometric Coordinate Transformation Blind Equalization Algorithm Based on Bi-Direction Long and Short-Term Memory Neural Networks

Liu, Na; Wang, Zuoxun; Wei, Haiwen

doi:10.1109/access.2024.3368857

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…Figure 3 is the BPNN topology structure with multiple hidden layers. Although the nonlinear generalization ability of BPNN is strong, it takes a long time to search for the optimal solution by multiple iterative training, the convergence is poor, it is not easy to escape from the local extremum, and the prediction error is relatively large [35,36]. As such, the traditional BPNN prediction has great limitations in some fields.…”

Section: Back Propogation Neural Networkmentioning

confidence: 99%

An Improved Back Propagation Neural Network Based on Differential Evolution and Grey Wolf Optimizer and Its Application in the Height Prediction of Water-Conducting Fracture Zone

Wang,

Zhu,

2024

Applied Sciences

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Given that the conventional back propagation neural network (BPNN) easily falls into the local optimal solutions, resulting in poor prediction accuracy, an improved BPNN based on the differential evolution and grey wolf optimizer (DEGWO) is proposed, the so-called DEGWO-BPNN. The prediction of the water-conducting fracture zone (WCFZ) height is significant for mine safety operations. A total of 104 sample data are trained and 25 sample data are tested to identify the optimal prediction model. Five evaluation indexes are selected to assess the prediction performance of the models quantitatively. Finally, the DEGWO-BPNN model is applied to a specific engineering case. The main conclusions are as follows: (1) Mining height, mining depth, coal seam dip, panel width, and ratio of hard rock as the main factors affecting the WCFZ height are selected. The topology structure of the model is defined as ‘5-12-1’; (2) the bias between the predicted value and the actual value of the training samples is smaller with an average error of 2.39. Test samples further validate the prediction precision through evaluation indexes. The values of MAE, RMSE, MAPE, and R2 are 2.3952, 3.4674, 5.3148%, and 0.99077, respectively. The prediction accuracy is 94.6852%; (3) ‘Mining Code’, MLR, BPNN, and GWO-BPNN models are treated as the comparison groups. The comparative analysis shows that the prediction performance of ‘Mining Code’ is the worst, while that of DEGWO-BPNN is the best, and it outperforms other algorithms and statistical approaches; (4) the prediction of WCFZ height in the 11601 panel is in line with the actual value. The prediction error of the DEGWO-BPNN model is lower than that of the comparison models. As such, the DEGWO-BPNN model can be well applied to the prediction of WCFZ height and is suitable for coal mines with different regional geological conditions. It can provide a valuable reference for mine safety operations.

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Section: Back Propogation Neural Networkmentioning

confidence: 99%

An Improved Back Propagation Neural Network Based on Differential Evolution and Grey Wolf Optimizer and Its Application in the Height Prediction of Water-Conducting Fracture Zone

Wang,

Zhu,

2024

Applied Sciences

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Lithium Thionyl Chloride Battery RP Estimation Mechanism Based on Wavelet Framework and Neural Networks

Li,

Yi,

Xie

et al. 2024

2024 10th International Conference on Big Data and Information Analytics (BigDIA)

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Cluster channel equalization using adaptive sensing and reinforcement learning for UAV communication

Liu,

Zhao,

Liang

et al. 2024

PeerJ Computer Science

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Aiming to address the need for dynamic sensing and channel equalization in UAV cluster communication environments, this article introduces an equalization algorithm based on a U-Net model and fuzzy reinforcement Q-learning (U-FRQL-EA). This algorithm is designed to enhance the channel sensing and equalization capabilities of UAV communication systems. Initially, we develop a U-Net-based signal processing algorithm that effectively reduces acoustic noise in UAV communication channels and enables real-time, accurate perception of channel states by automatically learning channel features. Subsequently, we enhance fuzzy reinforcement Q-learning by incorporating a fuzzy neural network to approximate the Q-values and integrating this approach with the allocation strategy of wireless sensing nodes. This enhancement not only improves the accuracy of Q-value approximation but also increases the algorithm’s adaptability and decision-making ability in complex channel environments. Finally, we construct the U-FRQL-EA equalization algorithm by combining the improved U-Net model with fuzzy reinforcement Q-learning. This algorithm leverages the U-Net model to sense channel states in real time and intelligently adjusts data forwarding strategies based on fuzzy values generated by the fuzzy reinforcement Q-learning. Simulation results demonstrate that the U-FRQL-EA algorithm effectively reduces the system’s bit error rate, enhances communication quality, and optimizes network resource utilization, offering a novel solution for improving the performance of uncrewed aerial vehicle communication systems.

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Trigonometric Coordinate Transformation Blind Equalization Algorithm Based on Bi-Direction Long and Short-Term Memory Neural Networks

Cited by 3 publications

References 19 publications

An Improved Back Propagation Neural Network Based on Differential Evolution and Grey Wolf Optimizer and Its Application in the Height Prediction of Water-Conducting Fracture Zone

An Improved Back Propagation Neural Network Based on Differential Evolution and Grey Wolf Optimizer and Its Application in the Height Prediction of Water-Conducting Fracture Zone

Lithium Thionyl Chloride Battery RP Estimation Mechanism Based on Wavelet Framework and Neural Networks

Cluster channel equalization using adaptive sensing and reinforcement learning for UAV communication

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