Journal
ENTROPY
Volume 15, Issue 10, Pages 4188-4198Publisher
MDPI
DOI: 10.3390/e15104188
Keywords
fractional calculus; fractional differential equations; fractional polynomial neural network
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Funding
- University of Malaya [RG208-11AFR]
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In this work, we introduce a generalization of the differential polynomial neural network utilizing fractional calculus. Fractional calculus is taken in the sense of the Caputo differential operator. It approximates a multi-parametric function with particular polynomials characterizing its functional output as a generalization of input patterns. This method can be employed on data to describe modelling of complex systems. Furthermore, the total information is calculated by using the fractional Poisson process.
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