Universal 2-layered noniterative perceptron for recognizing closely related patterns

Chia-Lun John Hu

doi:10.1117/12.395079

4 August 2000 Universal 2-layered noniterative perceptron for recognizing closely related patterns

Chia-Lun John Hu

Proceedings Volume 4052, Signal Processing, Sensor Fusion, and Target Recognition IX; (2000) https://doi.org/10.1117/12.395079
Event: AeroSense 2000, 2000, Orlando, FL, United States

Abstract

As we published in the last eight years, when the analog-to- binary mapping of any M training CLASS patterns are not PLI, then a one-layered preceptron (OLP) just cannot learn this mapping at all no matter what learning rule we use, because the solution of the connection matrix does not exist. However, as we derived form this PLI condition, which is the most general separability condition for an OLP, a PCTLP system can still be used to separate these closely relate and 'inseparable' patterns according to the targeted outputs V_m. This paper repots the theory and the design of this NOVEL PCTLP system.

Citation Download Citation

Chia-Lun John Hu "Universal 2-layered noniterative perceptron for recognizing closely related patterns", Proc. SPIE 4052, Signal Processing, Sensor Fusion, and Target Recognition IX, (4 August 2000); https://doi.org/10.1117/12.395079

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