Hierarchical perceptron (HiPer) networks for signal/image classifications
Sun‐Yuan Kung, J.S. Taur · 2003
A new class of decision-based neural networks (DBNNs) is introduced. These networks combine the perceptron-like learning rule with a hierarchical nonlinear network structure and are called HiPer nets. Two HiPer net structures are proposed: hidden-node and subcluster structures. The authors explore several variants of HiPer nets based on the different hierarchical structures and basis functions and then examine the relationships between HiPer nets and other DBNNs, e.g., perceptron and LVQ. Based on the simulation performance comparison, the HiPer nets appear to be very effective for many signal/image classification applications, including texture classification, OCR (optical character recognition), and ECG (electrocardiography).>