Single-layer perceptron and dynamic neuron implementing linearly non-separable Boolean functions
✍ Scribed by Fangyue Chen; Wenhui Tang; Guanrong Chen
- Book ID
- 102128959
- Publisher
- John Wiley and Sons
- Year
- 2009
- Tongue
- English
- Weight
- 166 KB
- Volume
- 37
- Category
- Article
- ISSN
- 0098-9886
- DOI
- 10.1002/cta.481
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✦ Synopsis
Abstract
This paper presents a single‐layer perceptron (SLP) scheme with an impulse activation function (IAF) and a dynamic neuron (DN) with a trapezoidal activation function (TAF). Combining with some interesting properties of the offset levels, it is shown that many linearly non‐separable Boolean functions can be realized by using only one SLPwIAF or one DNwTAF. In the present work, a few appropriate IAF and TAF are adopted, and the inverse offset level method is used for the design of the SLPwIAF synaptic weights and the DNwTAF templates. The XOR and NXOR Boolean operations with two inputs and all 152 non‐separable Boolean functions with three inputs can be easily implemented by one SLPwIAF or one DNwTAF. Finally, the entire set of 152 DNwTAF templates associated with 152 non‐separable Boolean functions of three inputs is completely listed. Copyright © 2008 John Wiley & Sons, Ltd.