Modified Backpropagation with Added White Gaussian Noise in Weighted Sum for Convergence Improvement

Sapkal, Ashwini; Kulkarni, Uday

doi:10.1016/j.procs.2018.10.401

Cited by 11 publications

(2 citation statements)

References 25 publications

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“…The most popular activation functions are the logical sigmoid function and the hyperbolic tangent function. 95 The logical sigmoid function can be defined using equation ( 2) and is shown in Figure 4(a). The hyperbolic tangent function can be defined by applying equation ( 3) and is shown in Figure 4(b).…”

Section: Neural Network and Training Algorithmsmentioning

confidence: 99%

“…The training of neural networks is most simply the correction of weight factors to the extent that appropriate output data are obtained. 95 Today, there are several algorithms and methods for training neural networks, and one of them is the method of backpropagation (backward propagation of errors). Backpropagation is a special case of automatic differentiation, that is, backward differentiation.…”

Section: Neural Network Training Algorithmsmentioning

confidence: 99%

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Application of neural networks in predictions of brake wear particulate matter emission

Vasiljević

Glišović

Stojanović

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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According to the World Health Organization, air pollution with PM10 and PM2.5 (PM-particulate matter) is a significant problem that can have serious consequences for human health. Vehicles, as one of the main sources of PM10 and PM2.5 emissions, pollute the air and the environment both by creating particles by burning fuel in the engine, and by wearing of various elements in some vehicle systems. In this paper, the authors conducted the prediction of the formation of PM10 and PM2.5 particles generated by the wear of the braking system using a neural network (Artificial Neural Networks (ANN)). In this case, the neural network model was created based on the generated particles that were measured experimentally, while the validity of the created neural network was checked by means of a comparative analysis of the experimentally measured amount of particles and the prediction results. The experimental results were obtained by testing on an inertial braking dynamometer, where braking was performed in several modes, that is under different braking parameters (simulated vehicle speed, brake system pressure, temperature, braking time, braking torque). During braking, the concentration of PM10 and PM2.5 particles was measured simultaneously. The total of 196 measurements were performed and these data were used for training, validation, and verification of the neural network. When it comes to simulation, a comparison of two types of neural networks was performed with one output and with two outputs. For each type, network training was conducted using three different algorithms of backpropagation methods. For each neural network, a comparison of the obtained experimental and simulation results was performed. More accurate prediction results were obtained by the single-output neural network for both particulate sizes, while the smallest error was found in the case of a trained neural network using the Levenberg-Marquardt backward propagation algorithm. The aim of creating such a prediction model is to prove that by using neural networks it is possible to predict the emission of particles generated by brake wear, which can be further used for modern traffic systems such as traffic control. In addition, this wear algorithm could be applied on other vehicle systems, such as a clutch or tires.

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Section: Neural Network and Training Algorithmsmentioning

confidence: 99%

Section: Neural Network Training Algorithmsmentioning

confidence: 99%