Neural networks with hybrid morphological/rank/linear nodes: a unifying framework with applications to handwritten character recognition

✍ Scribed by Lúcio F.C. Pessoa; Petros Maragos

• Publisher: Elsevier Science
• Year: 2000
• Tongue: English
• Weight: 506 KB
• Volume: 33
• Category: Article
• ISSN: 0031-3203
• DOI: 10.1016/s0031-3203(99)00157-0

✦ Synopsis

In this paper, the general class of morphological/rank/linear (MRL) multilayer feed-forward neural networks (NNs) is presented as a unifying signal processing tool that incorporates the properties of multilayer perceptrons (MLPs) and morphological/rank neural networks (MRNNs). The fundamental processing unit of MRL-NNs is the MRL-"lter, where the combination of inputs in every node is formed by hybrid linear and nonlinear (of the morphological/rank type) operations. For its design we formulate a methodology using ideas from the back-propagation algorithm and robust techniques to circumvent the non-di!erentiability of rank functions. Extensive experimental results are presented from the problem of handwritten character recognition, which suggest that MRL-NNs not only provide better or similar performance when compared to MLPs but also can be trained faster. The MRL-NNs are a broad interesting class of nonlinear systems with many promising applications in pattern recognition and signal/image processing.