Direct Signal Calibration Using Learning Systems

摘要

In addition to proposing two novel ensemble learning methods, a novel method of using a learning paradigm for the calibration of nonlinear systems is also proposed in this paper. With this it addresses the non-existent use of learning systems to provide corrective measures in calibration. In this method the learned system provides corrections directly to the nonlinear device to linearize the system output without the need for an additional calculation to produce the corrections. In many calibration tasks a model of the nonlinear system is created and with its help corrections are calculated to linearize the system output. The proposed method makes these two steps transparent by learning the corrective step instead. Therefore the learned system is able to then directly linearize the nonlinear system output. By taking into consideration both the training and pruning aspects of ensemble neural network predictors, two dynamic ensemble methods have been proposed in this paper, one involving pruning and the other a hybrid approach. To enhance diversity the pruning or selection of predictors, and the training of predictors are performed in succession for every pattern in the training set. By ordering the predictors based on their performance on a training pattern, the first method trains only the most divers predictors, while the second method splits the ensemble into two sub-ensembles and applies the hybrid method of training the first sub-ensemble using Negative Correlation Learning (NCL) while the second sub-ensemble independently. During the test phase of these methods a subset of the trained predictors are chosen differently depending on their performance on the test sample. Therefore the ensemble selection is dynamic during predicting the output, which improves the prediction accuracy.