A hybrid neural network for spatio-temporal pattern recognition
Yifeng Chen, Yuanda Cao · 2002
In this paper a hybrid network is presented for spatio-temporal pattern recognition (STPR) which is called TS-LM-SOFM. The top layer of TS-LM-SOFM is a single layer temporal sequence recognizer which is called TS (temporal sequence). TS can transform temporal sparse pattern sequence into abstract spatial feature representations. The bottom layer of TS-LM-SOFM is a modified SOFM (self-organizing feature map) used as a spatial feature detector. LM (learning matrix) is introduced as a middle layer. In the experiment, some mobile robot's sonar sensor data are used for training. Experiments show that the hybrid network can well capture the spatio-temporal features of input signals.