Connectionist learning procedures
Artificial Intelligence
A Rosetta stone for connectionism
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Complex Systems and Cognitive Processes
Complex Systems and Cognitive Processes
Computer-Aided Techniques for the Design of Multilayer Filters
Computer-Aided Techniques for the Design of Multilayer Filters
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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.