A perception evolution network for unsupervised fast incremental learning

A perception evolution network (PEN) is proposed for unsupervised fast incremental (or on-line) learning in this paper. The network has two layers: the perception layer receives the external data from the reality environment and the knowledge layer learns and records the knowledge contained in the data from the perception layer in incremental and self-organizing way. In PEN model, new input channels can be added to the perception layer freely during learning. When the perception layer gets some new input channels, the network will create corresponding data transmission channels to the knowledge layer. The prior learned knowledge stored in the knowledge layer will be expanded to a higher-dimensional space which contains the attributes of the new input channels. For incremental (or on-line) learning, PEN can automatically obtain suitable prototypes and find the topology structure of the learning data without any priori knowledge. The noise processing mechanism guarantees PEN can work in the complex real-world (noisy) environment. The experiments for both artificial data set and real-world data set show that the proposed method is effective.