Abstract

This work presents an architecture for the development of on-line prediction models. The archi- tecture defines an unied modular environment based on three concepts frome machine learning, these are: (i) ensemble methods; (ii) local learning; and (iii) meta learning. The three concepts are organised in a three layer hierarchy within the architecture. For the actual prediction any data-driven predictive method such as articial neural network, support vector machines, etc. can be implemented and plugged in. Additionally to the predictive methods, data pre-processing methods are also managed and can also be implemented as plug- ins. Models developed according to the architecture can be trained and operated in different modes. As for the training, the architecture supports the building of ini- tial models based on a batch of training data, but if this data is not available the models can also be trained in incremental mode. In a scenario where correct target values are (occasionally) available during the run-time, the architecture supports life-long learning by providing several adaptation mechanisms across the three hierar- chical levels. In order to demonstrate its practicality, we show how the issues of current soft sensor development and maintenance can be effectively dealt with by using the architecture as a construction plan for automated development and maintenance of the soft sensors.