Pattern Recognition using Chaotic Transients

This paper proposes a novel nonlinear transient computation device described as the LTCM that uses the chaotic attractor provided by the Lorenz system of equations to perform pattern recognition. Previous work on nonlinear transient computation has demonstrated that such devices can process time varying input signals. This paper investigates the ability of the LTCM to correctly classify static, linearly inseperable data sets commonly used as benchmarks in the pattern recognition research community. The results from the LTCM are compared with those from support vector machines and multi-layer perceptrons on the same data sets.