Multilayer feedforward networks are universal approximators
Neural Networks
Approximation capabilities of multilayer feedforward networks
Neural Networks
Universal approximation using radial-basis-function networks
Neural Computation
Approximation theory and feedforward networks
Neural Networks
Observability and Observers for Nonlinear Systems
SIAM Journal on Control and Optimization
Rational function neural network
Neural Computation
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
An orthogonal neural network for function approximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A study of uncertain state estimation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
ScaleNet-multiscale neural-network architecture for time series prediction
IEEE Transactions on Neural Networks
Function approximation with spiked random networks
IEEE Transactions on Neural Networks
Estimations of error bounds for neural-network function approximators
IEEE Transactions on Neural Networks
Neural-network prediction with noisy predictors
IEEE Transactions on Neural Networks
Bayesian approach to neural-network modeling with input uncertainty
IEEE Transactions on Neural Networks
Approximation of nonlinear systems with radial basis function neural networks
IEEE Transactions on Neural Networks
Prediction of noisy chaotic time series using an optimal radial basis function neural network
IEEE Transactions on Neural Networks
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Knowing information about the physiologic state of microorganisms, an optimal control for biosynthesis processes can be implemented. Information regarding the physiologic state of microorganisms is given by their mean age, which can be estimated based on the measurable state variables of the process. In this paper, a neural network based estimator for the microorganisms' mean age is developed and used for implementing an optimal control law for the biosynthesis process. The data taken from such biosynthesis processes are affected by considerable measurement noise. The estimator in this paper was developed using noisy real data, the whole control structure for the process shows good control performances.