Neural Modeling of an Industrial Process with Noisy Data
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Improved CBP Neural Network Model with Applications in Time Series Prediction
Neural Processing Letters
Stochastic fuzzy neural network and its robust parameter learning algorithm
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
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Currently, most learning algorithms for neural-network modeling are based on the output error approach, using a least squares cost function. This method provides good results when the network is trained with noisy output data and known inputs. Special care must be taken, however, when training the network with noisy input data, or when both inputs and outputs contain noise. This paper proposes a novel cost function for learning NN with noisy inputs, based on the errors-in-variables stochastic framework. A learning scheme is presented and examples are given demonstrating the improved performance in neural-network curve fitting, at the cost of increased computation time