A novel machine learning algorithm for interval systems approximation based on artificial neural network

Zerrougui, Raouf; Adamou‐Mitiche, Amel Baha Houda; Mitiche, Lahcène

doi:10.1007/s10845-021-01874-0

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…At present, machine learning algorithms mainly include decision tree algorithm, support vector machine algorithm, artificial neural network algorithm, deep learning algorithm, etc. [30][31][32][33][34][35]. Researchers have applied the machine learning method to the field of flexible electronics by combining the characteristics of the algorithm and have achieved good results.…”

Section: Introductionmentioning

confidence: 99%

A Temperature Prediction Model for Flexible Electronic Devices Based on GA-BP Neural Network and Experimental Verification

Nan,

Chen,

et al. 2024

Micromachines

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The problem that the thermal safety of flexible electronic devices is difficult to evaluate in real time is addressed in this study by establishing a BP neural network (GA-BPNN) temperature prediction model based on genetic algorithm optimisation. The model uses a BP neural network to fit the functional relationship between the input condition and the steady-state temperature of the equipment and uses a genetic algorithm to optimise the parameter initialisation problem of the BP neural network. To overcome the challenge of the high cost of obtaining experimental data, finite element analysis software is used to simulate the temperature results of the equipment under different working conditions. The prediction variance of the GA-BPNN model does not exceed 0.57 °C and has good robustness, as the model is trained according to the simulation data. The study conducted thermal validation experiments on the temperature prediction model for this flexible electronic device. The device reached steady state after 1200 s of operation at rated power. The error between the predicted and experimental results was less than 0.9 °C, verifying the validity of the model’s predictions. Compared with traditional thermal simulation and experimental methods, this model can quickly predict the temperature with a certain accuracy and has outstanding advantages in computational efficiency and integrated application of hardware and software.

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