Development of two-phase neural network-genetic algorithm hybrid model in modeling damage evolution in roll forming of aluminum sheet

Park, Hae‐Sim; Anh, Tran-Viet

doi:10.1007/s12289-012-1096-5

Cited by 7 publications

(1 citation statement)

References 19 publications

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“…Finally, make the operation through genetic operators such as selection, crossover and mutation. The output is seen as the initial weights and thresholds, and then the GA-BPNN prediction model can be obtained [14,26,27]. Therefore, the key elements of GA-BPNN involves chromosome coding, fitness function, crossover operation and mutation operation.…”

Section: Ga-bp Neural Network (Ga-bpnn)mentioning

confidence: 99%

Sound quality prediction of vehicle interior noise and mathematical modeling using a back propagation neural network (BPNN) based on particle swarm optimization (PSO)

Zhang

Hou

Shen

et al. 2015

Meas. Sci. Technol.

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To better solve the complex non-linear problem between the subjective sound quality evaluation results and objective psychoacoustics parameters, a method for the prediction of the sound quality is put forward by using a back propagation neural network (BPNN) based on particle swarm optimization (PSO), which is optimizing the initial weights and thresholds of BP network neurons through the PSO. In order to verify the effectiveness and accuracy of this approach, the noise signals of the B-Class vehicles from the idle speed to 120 km h−1 measured by the artificial head, are taken as a target. In addition, this paper describes a subjective evaluation experiment on the sound quality annoyance inside the vehicles through a grade evaluation method, by which the annoyance of each sample is obtained. With the use of Artemis software, the main objective psychoacoustic parameters of each noise sample are calculated. These parameters include loudness, sharpness, roughness, fluctuation, tonality, articulation index (AI) and A-weighted sound pressure level. Furthermore, three evaluation models with the same artificial neural network (ANN) structure are built: the standard BPNN model, the genetic algorithm-back-propagation neural network (GA-BPNN) model and the PSO-back-propagation neural network (PSO-BPNN) model. After the network training and the evaluation prediction on the three models’ network based on experimental data, it proves that the PSO-BPNN method can achieve convergence more quickly and improve the prediction accuracy of sound quality, which can further lay a foundation for the control of the sound quality inside vehicles.

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Section: Ga-bp Neural Network (Ga-bpnn)mentioning

confidence: 99%