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Pattern recognition using finite-iteration cellular systems (2005).
| Content Provider | CiteSeerX |
|---|---|
| Author | Ogorzalek, Maciej Merkwirth, Christian |
| Abstract | Cellular Systems are defined by cells that have an internal state and local interactions between cells that govern the dynamics of the system. We propose to use a special kind of Cellular Neural Networks (CNNs) which operates in finite iteration discrete-time mode and mimics the processing of visual perception in biological systems for digit recognition. We propose also a solution to another type of pattern recognition problem using a non-standard cellular neural networks called Molecular Graph Networks (MGNs) which offer direct mapping from compound to property of interest such as Physico-Chemical, Toxicity, logP, Inhibitory Activity MGNs translate molecular topology to network topology. We show how to design/train by backpropagation CNNs and MGNs in their discrete-time and finite-iteration versions to perform classification on real-world data sets. |
| File Format | |
| Publisher Date | 2005-01-01 |
| Access Restriction | Open |
| Subject Keyword | Internal State Direct Mapping Real-world Data Set Non-standard Cellular Neural Network Backpropagation Cnns Cellular System Molecular Graph Network Finite Iteration Discrete-time Mode Biological System Special Kind Local Interaction Cellular Neural Network Digit Recognition Pattern Recognition Problem Finite-iteration Version Visual Perception Network Topology |
| Content Type | Text |
