Multilayer feedforward networks are universal approximators
Neural Networks
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Universal approximation using radial-basis-function networks
Neural Computation
Ill-conditioning in neural network training problems
SIAM Journal on Scientific Computing
Training with noise is equivalent to Tikhonov regularization
Neural Computation
Regularized neural networks: some convergence rate results
Neural Computation
Some new results on neural network approximation
Neural Networks
Solution of nonlinear ordinary differential equations by feedforward neural networks
Mathematical and Computer Modelling: An International Journal
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
Curvature-driven smoothing: a learning algorithm for feedforward networks
IEEE Transactions on Neural Networks
Functional approximation by feed-forward networks: a least-squares approach to generalization
IEEE Transactions on Neural Networks
Existence and uniqueness results for neural network approximations
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Stochastic choice of basis functions in adaptive function approximation and the functional-link net
IEEE Transactions on Neural Networks
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The emulation of mechanical systems is a popular application of artificial neural networks in engineering. This paper examines general principles of modelling mechanical systems by feedforward artificial neural networks (FFANNs). The slow convergence issue associated with the highly parallel and redundant structure of FFANN systems is addressed by formulating criteria for constraining network parameters so that FFANNs may be reliably applied to mechanics problems. The existence of the FFANN mechanical model and its stability during construction, with respect to the error in the data, are analyzed. Also, a class of differential equations is analyzed for use with Tikhonov regularization. It is shown that the use of Tikhonov regularization can aid in FFANN data-driven construction with a priori mathematical models of varying degrees of physical fidelity. Criteria to ensure successful FFANN application from an engineering perspective are established.