Multilayer feedforward networks are universal approximators
Neural Networks
Advances in neural information processing systems 2
Neural Computation
Exact calculation of the Hessian matrix for the multilayer perceptron
Neural Computation
Neural network design
Descriptive sampling: an improvement over Latin hypercube sampling
Proceedings of the 29th conference on Winter simulation
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Nonlinear parameter estimation via the genetic algorithm
IEEE Transactions on Signal Processing
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
An introduction to simulated evolutionary optimization
IEEE Transactions on Neural Networks
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Abstract: The paper proposes a general procedure based on Bayesian neural networks for parameter identification of numerical models. In this context, the Bayesian neural networks are extended to multiple outputs with a full covariance matrix to describe the correlation between the noise of output parameters. This extension is especially useful for inverse problems such as a parameter identification procedure, since it allows for the quantification of correlations between output parameters. Based on numerically obtained forward calculations, the Bayesian neural network is trained to solve the inverse parameter identification problem. The main advantage of the method is the ability to verify the accuracy of the identified parameters and their correlation. The methodology further allows to detect, whether a certain set of experiments is sufficient to determine an individual model parameter. As a result, a general scheme for the design of experiments to identify model parameters is developed and illustrated for two examples.