Pattern recognition by an optical thin-film multilayer model

This paper describes a computational learning model inspired by the technology of optical thin-film multilayers from the field of optics. With the thicknesses of thin-film layers serving as adjustable "weights" for the computation, the optical thin-film multilayer (OTFM) model is capable of approximating virtually any kind of nonlinear mapping. This paper describes the architecture of the model and how it can be used as a computational learning model. Some sample simulation calculations that are typical of connectionist models, including a pattern recognition of alphabetic characters, iris plant classification, and time series modelling of a gas furnace process, are given to demonstrate the model's learning capability.

• Type: Journal
• Year: 1999
• Where: AMAI
• Authors: Xiaodong Li, Martin K. Purvis
• Keywords: AMAI 1999, Artificial Intelligence, Computational Learning Model, Optical Thin-film Multilayer, Thin-film Layers