Optimizing feedforward artificial neural network architecture

Optimal Training Parameters and Hidden Layer Neuron Number of Two-Layer Perceptron for Generalised Scaled Object Classification Problem

“…This integer might be determined by R and B . But bonds between these three integers are implicit [17], [20], [21].…”

Shear Stress Prediction Using FEA-ANN Hybrid Modeling Of Eicher 11.10 Chassis Frame

“…Saltan et al (2007) introduced a new concept of integrating artificial neural networks (ANN) and finite element method (FEM) in modelling the unbound material properties of the sub -base layer in flexible pavements. Benardos et al (2007) have been adopted the multitude of different approaches in order to deal with this problem which has investigated all aspects of the ANN modelling procedure, from training data collection and pre/post-processing to elaborate training schemes and algorithms. Cardozo et al (2011) presented the formulation and implementation of a computational code to optimize manufactured complex laminated structures with a relatively low computational cost by combining the Finite Element Method (FEM) for structural analysis, Genetic Algorithms (GA) for structural optimization and ANN to approximate the finite element solutions.…”

Assessing rainfall erosivity indices through synthetic precipitation series and artificial neural networks

“…The low number of neurons on the developed ANNs (Table II) indicates a lower complexity, better ability to generalization and estimation (Bernardos and Vosniakos 2007) and had a lower probability of "memorizing" answers (Sinha and Wang 2007). ANN with increased numbers of neurons were only those for the spatial interpolation of EI 30 for the months of October, November and December (KE computed by WS equation) and for February and October (KE computed by WM equation).…”