Outlier identification in high dimensions
Computational Statistics & Data Analysis
Engineering Applications of Artificial Intelligence
Soft Sensors for Monitoring and Control of Industrial Processes
Soft Sensors for Monitoring and Control of Industrial Processes
Robust error measure for supervised neural network learning with outliers
IEEE Transactions on Neural Networks
The annealing robust backpropagation (ARBP) learning algorithm
IEEE Transactions on Neural Networks
A robust backpropagation learning algorithm for function approximation
IEEE Transactions on Neural Networks
Engineering Applications of Artificial Intelligence
| Hi-index | 0.00 |
Environmental monitoring is nowadays an important task in many industrial operations. In order to comply with strong environmental laws, they have implemented monitoring systems based on a network of air quality and meteorological stations providing real-time measurements of key variables associated to the distribution of pollutants in surrounding areas. These measurements can be contaminated by outliers, which must be discarded in order to have a consistent set of data. This work presents a nonlinear procedure for outliers detection based on residual analysis of regression with Partial Least Squares and Artificial Neural Networks. In order to minimize the negative effect of outliers in the training dataset a learning algorithm with regularization is proposed. This algorithm is based on a Quasi-Newton optimization method and it was tested on a simulated nonlinear process, on real data from environmental monitoring contaminated with synthetic outliers, and finally applied to a real environmental monitoring data obtained from a monitoring station and having natural outliers. The results are encouraging and further developments are foreseen for including information from neighboring stations and emission source operation.