Air-quality prediction based on the ARIMA-CNN-LSTM combination model optimized by dung beetle optimizer

Duan, Jing; Gong, Yubin; Luo, Jun; Zhao, Zhiyao

doi:10.1038/s41598-023-36620-4

Cited by 22 publications

(8 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thieving dung beetles forage or steal dung balls based on known optimal foraging areas and the locations of other companions. The optimization process of the DBO algorithm for fuzzy PID control mainly has six steps [30]: (1) initialize the dung beetle population and DBO algorithm parameters; (2) calculate the fitness values of all dung beetles' positions based on the objective function; (3) update the positions of all dung beetles; (4) check if any updated dung beetles have gone out of bounds; (5) update the current optimal solution and its fitness value; and (6) repeat the above steps until t reaches the maximum number of iterations, after which the global optimum value and the optimal solution are output.…”

Section: Dung Beetle Optimizementioning

confidence: 99%

See 1 more Smart Citation

Dung Beetle Optimized Fuzzy PID Algorithm Applied in Four-Bar Target Temperature Control System

Cao,

Liu,

Song

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

With the widespread application of infrared thermal imagers in various fields, the demand for thermal imagers and their performance parameter testing equipment has increased significantly. There are particularly high demands on the detection accuracy of minimum resolvable temperature difference (MRTD) testers. Traditional MRTD testers have an issue with the four-bar target temperatures being easily affected by the external environment, resulting in non-uniform temperatures and imprecise detection results. This paper proposes an improvement to the four-bar targets by making them temperature-controllable. Temperature is controlled by installing thermoelectric coolers (TECs) and thin-film platinum resistors at the center and periphery of the four-bar targets with different spatial frequencies. The dung beetle algorithm is used to optimize fuzzy PID parameters to regulate the TEC’s heating and cooling, improving the overall temperature uniformity of the four-bar targets. Temperature simulations of the four-bar targets were conducted on the COMSOL platform, with the control part simulated on the Simulink platform. The simulation results show that, compared to traditional PID, the fuzzy PID controller reduces overshoot by approximately 3.6%, although the system still exhibits mild oscillations. The fuzzy PID controller optimized by the dung beetle optimization (DBO) algorithm, in comparison to standard fuzzy PID, reduces the settling time by about 40 s and lowers overshoot by around 7%, with oscillations in the system nearly disappearing. Comparing the fuzzy PID optimized by the particle swarm optimization (PSO) algorithm with the fuzzy PID optimized by the DBO algorithm, the DBO-based controller shows shorter rise and settling times, further illustrating the superiority of the fuzzy PID control optimized by the dung beetle algorithm. This provides a theoretical foundation for improving the accuracy of MRTD detector measurements. Finally, experimental verification was carried out. The experimental results indicate that DBO (drosophila-based optimization) has significant advantages, and its optimized results are closer to the actual values.

show abstract

Section: Dung Beetle Optimizementioning

confidence: 99%

Section: Dung Beetle Optimizementioning

confidence: 99%

Dung Beetle Optimized Fuzzy PID Algorithm Applied in Four-Bar Target Temperature Control System

Cao,

Liu,

Song

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…In summary, the IWSO algorithm offers an effective and dependable approach for hyperparameter selection in CNN-LSTM models, mitigating issues such as time loss and model instability that may arise from manual hyperparameter adjustments. The output of the optimal combination of hyperparameters is feature_num= [27,93], LayerSizes = 47, Stride = 2.…”

Section: Experimental Validation and Analysismentioning

confidence: 99%

“…To address these challenges, Hochreiter et al introduced the Long Short-Term Memory (LSTM) network, an upgraded version of Recurrent Neural Networks (RNNs) speci cally designed to handle time-series data 26 . The LSTM excels at retaining long-term memories and learning dependencies, effectively addressing gradient vanishing and exploding issues, resulting in superior performance and outcomes compared to RNNs 27 .…”

Section: Introductionmentioning

confidence: 99%

Wear fault diagnosis in hydro-turbine via the incorporation of the IWSO algorithm optimized CNN-LSTM neural network

Dao,

Zeng,

Zou

et al. 2024

Preprint

View full text Add to dashboard Cite

Diagnosing hydro-turbine wear fault is crucial for the safe and stable operation of hydropower units. A hydro-turbine wear fault diagnosis method based on improved WT (wavelet threshold algorithm) preprocessing combined with IWSO (improved white shark optimizer) optimized CNN-LSTM (convolutional neural network-long-short term memory) is proposed. The improved WT algorithm is utilized for denoising the preprocessing of the original signals. The CNN-LSTM hydro-turbine wear fault diagnosis model is constructed. Aiming at the problem that the WSO algorithm quickly falls into local optimum and premature convergence, tent chaotic mapping is used to initialize the population and birds flock search behavior. The cosine elite variation strategy is introduced to improve convergence speed and accuracy. Hyperparameter tuning of CNN-LSTM model based on IWSO algorithm. The experimental results show that the accuracy of the proposed method reaches 96.2%, which is 8.9% higher than that of the IWSO-CNN-LSTM model without denoising. The study also found that the diagnostic accuracy of hydro-turbine wear faults increased with increasing sediment concentration in the water. This study can supplement the existing hydro-turbine condition monitoring and fault diagnosis system. Meanwhile, diagnosing wear faults in hydro-turbines can improve power generation efficiency and quality and minimize resource consumption.

show abstract

“…Compared to the grey wolf algorithm, whale algorithm, and sparrow algorithm, DBO holds advantages such as robust optimization ability, rapid convergence, high solution accuracy, and robustness. Scholars have used these advantages to solve complex parameter optimization problems [22,23]. Consequently, DBO is utilized for optimizing VMD's parameters.…”

Section: Introductionmentioning

confidence: 99%

Compound fault diagnosis of rolling bearings based on AVMD and IMOMEDA

Lu,

Yan,

Wang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

The intricate nature of compound fault diagnosis in rolling bearings during nonstationary operations poses a challenge. To address this, a novel technique combines adaptive variational mode decomposition (AVMD) with improved multipoint optimal minimum entropy deconvolution adjustment (IMOMEDA). The compound fault signal is isolated through AVMD, with internal parameters obtained via a new indicator termed integrated fault-impact measure index (IFIMI) guiding the improved dung beetle optimizer (IDBO). An adaptive selection method, using a weight factor, chooses the intrinsic mode function (IMF) containing principal fault data. IMOMEDA whose key parameters are determined by a novel combinatorial strategy is then employed to deconvolute selected fault components, enhancing periodic fault impulses by removing complex interferences and ambient noise. The deconvoluted signal undergoes enhanced envelope spectrum processing to extract fault frequencies and identify fault types. Numerical simulations and experimental data confirm the method’s effectiveness and feasibility for compound faults diagnosis of rolling bearings, showcasing its superiority over existing techniques.

show abstract

Air-quality prediction based on the ARIMA-CNN-LSTM combination model optimized by dung beetle optimizer

Cited by 22 publications

References 38 publications

Dung Beetle Optimized Fuzzy PID Algorithm Applied in Four-Bar Target Temperature Control System

Dung Beetle Optimized Fuzzy PID Algorithm Applied in Four-Bar Target Temperature Control System

Wear fault diagnosis in hydro-turbine via the incorporation of the IWSO algorithm optimized CNN-LSTM neural network

Compound fault diagnosis of rolling bearings based on AVMD and IMOMEDA

Contact Info

Product

Resources

About