@article{(Open Science Index):https://publications.waset.org/pdf/4871,
	  title     = {Validity Domains of Beams Behavioural Models: Efficiency and Reduction with Artificial Neural Networks},
	  author    = {Keny Ordaz-Hernandez and  Xavier Fischer and  Fouad Bennis},
	  country	= {},
	  institution	= {},
	  abstract     = {In a particular case of behavioural model reduction by ANNs, a validity domain shortening has been found. In mechanics, as in other domains, the notion of validity domain allows the engineer to choose a valid model for a particular analysis or simulation. In the study of mechanical behaviour for a cantilever beam (using linear and non-linear models), Multi-Layer Perceptron (MLP) Backpropagation (BP) networks have been applied as model reduction technique. This reduced model is constructed to be more efficient than the non-reduced model. Within a less extended domain, the ANN reduced model estimates correctly the non-linear response, with a lower computational cost. It has been found that the neural network model is not able to approximate the linear behaviour while it does approximate the non-linear behaviour very well. The details of the case are provided with an example of the cantilever beam behaviour modelling.
},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {2},
	  number    = {6},
	  year      = {2008},
	  pages     = {2080 - 2087},
	  ee        = {https://publications.waset.org/pdf/4871},
	  url   	= {https://publications.waset.org/vol/18},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 18, 2008},
	}