Selective Dynamic Principal Component Analysis using Recurrent Neural Networks
Paper i proceeding, 2008
In the last decades, considerable attention has been focused on development of bio-inspired systems. This paper employs the principals of information processing in the Basal Ganglia (BG) to develop a new method for selectively extracting dynamic principal components (DPCs) of multidimensional datasets. The DPCs are extracted by are current structure of auto-associative neural network and selectivity is achieved by means of a reinforcement-like signal which modifies the desired outputs and the learning coefficient of the network. Performance of the model is evaluated through two experiments; at first, the DPCs of a stock price database are extracted and then, speech compression capability of the method is checked which illustrates the efficiency of the proposed approach.
Recurrent neural nets
Principal component analysis
Learning (artificial intelligence)