Nonlinear Spring-Mass-Damper Modeling and Parameter Estimation of Train Frontal Crash Using CLGAN Model

Algorithm 1

CLGAN algorithm: mini-batch stochastic gradient descent training of generative adversarial net.

	For number of training iterations do:
	For K steps do:
	Sample mini batch of m noise samples from noise prior (z)
	Sample mini batch of m examples from data applying LSTM net to generate distribution Pdata(x)
	Sample mini batch of m examples from data applying CNN as discriminator
	The cost function of the calculation discriminator:
	Update the parameters of the discriminator through RMSprop gradient drop algorithm:
	End for
	Sample mini batch of m noise samples from noise prior
	The cost function of the calculation generator:
	Update the parameters of the generator through Adam gradient drop algorithm:
	End for