Neural network based optimal control of a biosynthesis process
International Journal of Knowledge-based and Intelligent Engineering Systems - Advanced Intelligent Techniques in Engineering Applications
Neural network prediction of the roll motion of a ship for intelligent course control
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
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Very often the input variables for neural-network predictions contain measurement errors. In particular, this may happen because the original input variables are often not available at the time of prediction and have to be replaced by predicted values themselves. This issue is usually ignored and results in non-optimal predictions. This paper shows that under some general conditions, the optimal prediction using noisy input variables can be represented by a neural network with the same structure and the same weights as the optimal prediction using exact input variables. Only the activation functions have to be adjusted. Therefore, we can achieve optimal prediction without costly retraining of the neural network. We explicitly provide an exact formula for adjusting the activation functions in a logistic network with Gaussian measurement errors in input variables. This approach is illustrated by an application to short-term load forecasting