Expressing uncertainty in neural networks for production systems

Uncertainty of Artificial Neural Networks for Daily Evaporation Prediction (Case Study: Rasht and Manjil Stations)

This research uses the multilayer perceptron (MLP) model to predict daily evaporation at two synoptic stations located in Rasht and Manjil, Guilan province, in north-west of Iran. Initially the most important combinations of climatic parameters for both of the stations were identified using the gamma test; and daily evaporation were modeled based on the obtained optimal combination. The results...

Uncertainty in Bayesian Neural Networks

Weight Uncertainty in Neural Networks

We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop. It regularises the weights by minimising a compression cost, known as the variational free energy or the expected lower bound on the marginal likelihood. We show that this principled kind of regularisation yields...

Distribution Systems Reconfiguration Using Pattern Recognizer Neural Networks

A novel intelligent neural optimizer with two objective functions is designed for electrical distribution systems. The presented method is faster than alternative optimization methods and is comparable with the most powerful and precise ones. This optimizer is much smaller than similar neural systems. In this work, two intelligent estimators are designed, a load flow program is coded, and a spe...

Dynamic Sliding Mode Control of Nonlinear Systems Using Neural Networks

Dynamic sliding mode control (DSMC) of nonlinear systems using neural networks is proposed. In DSMC the chattering is removed due to the integrator which is placed before the input control signal of the plant. However, in DSMC the augmented system is one dimension bigger than the actual system i.e. the states number of augmented system is more than the actual system and then to control of such ...