Design of a parallelly cascaded two-layered perceptron consisting of hard-limited neurons

Chia-Lun John Hu

doi:10.1117/12.235904

22 March 1996 Design of a parallelly cascaded two-layered perceptron consisting of hard-limited neurons

Chia-Lun John Hu

Proceedings Volume 2760, Applications and Science of Artificial Neural Networks II; (1996) https://doi.org/10.1117/12.235904
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

When the given mapping in the supervised learning of a one-layered perceptron (OLP) satisfies the positive-linear-independency (or PLI) condition, the connection matrix of this OLP can be solved in a noniterative manner. On the other hand, if the given mapping violates this PLI condition, then no connection matrix exists for this OLP no matter what learning rule we use. This latter mapping is called an illegal mapping for the OLP. For an illegal mapping, it is found that a parallelly-cascaded, two-layered perceptron (PCTLP) consisting of hard- limited neurons will generally fulfill the learning duty and provide robust recognitions. The design of this PCTLP system is derived from the PLI condition. This PCTLP system is generally a much faster learning system than the conventional 3-layered perceptron system which is cascaded in series. The reason that it is much faster is not only that it is cascaded in parallel, but also that the learning is done noniteratively in a few algebraic steps.

Citation Download Citation

Chia-Lun John Hu "Design of a parallelly cascaded two-layered perceptron consisting of hard-limited neurons", Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); https://doi.org/10.1117/12.235904

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