Development of a gray box system identification model to estimate the parameters affecting traffic accidents (original) (raw)
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Literature on linear and nonlinear dynamic system identification is reviewed. The main motivation is to document the state-of-the-art, allowing one to propose further advancements in this field. The main problem is to identify system properties when experimental/numerical input and output data are specified. Parametric as well as nonparametric approaches for system identification are reviewed. For linear systems, both the first order and second order forms of the equations of motion are discussed. The use of first order form is more general as it can treat nonproportional structural damping as well. For nonlinear systems, the second order form of the equations of motion is used. A conclusion from the study is that more work is needed to develop identification formulations for nonlinear dissipative dynamic systems, especially for multi-degree of freedom systems.
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A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the p...
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Khulna University studies, 2022
In today's world, particularly in developing nations, traffic safety is one of the most pressing challenges. Every single day, traffic accidents alone claim hundreds of lives across all nations and regions. Besides rapid development in the transportation sector, road accidents are also increasing day by day. Therefore, every government has regarded it as one of the crucial issues and as the first big issue that has to be resolved. To lessen the number of accidents on the roads and the harm they cause, necessary actions have been implemented. We discovered that it is impracticable and time-consuming to investigate the contributing elements behind vehicle accidents since vast amounts of statistical data are required. In this paper, we develop a dynamical model to describe the dynamics of the traffic accident. Accidents are mostly caused by negligent or inexperienced drivers and defective machinery. We have described the dynamics in terms of a system of four ordinary differential equations with four state variables and parameters. We have developed a mathematical model that can estimate the number of fatal traffic accidents. In order to validate the model, we looked at the positivity of the solutions, equilibrium points, stability of the equilibrium points, and other relevant analytical studies. In order to demonstrate the validity of our study, we ran a numerical simulation using parameter values that were verified from reliable sources.