Growing and Evolving Neural Models for Analysis of Off-Line Data and Data Streams

抄録

The paper proposes three types of neural models, namely the fixed neural model (FNM), the growing neural model (GNM) and the evolving neural model (ENM) and their respective learning algorithms, based on different types of available data. The off-line learning algorithms of the FNM and GNM use batch-type (off-line) data sets, while the ENM learning can be performed in real time, by using ""endless"" data stream. It is shown in the paper, that the GNM is more flexible than the FNM and faster in off-line learning, while the ENM is a very convenient tool for analysis of real-time data, with its ability to analyse time-varying machine operations. Test examples as well as real data from a diesel engine of a hydraulic excavator are used in the paper to demonstrate te applicability and the features of the proposed neural models.