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Entropy 2013, 15(10), 4188-4198; doi:10.3390/e15104188

The Fractional Differential Polynomial Neural Network for Approximation of Functions

Institute of Mathematical Sciences, University Malaya, Kuala Lumpur 50603, Malaysia
Received: 26 August 2013 / Revised: 5 September 2013 / Accepted: 24 September 2013 / Published: 30 September 2013
(This article belongs to the Special Issue Dynamical Systems)
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Abstract

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.
Keywords: fractional calculus; fractional differential equations; fractional polynomial neural network fractional calculus; fractional differential equations; fractional polynomial neural network
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Ibrahim, R.W. The Fractional Differential Polynomial Neural Network for Approximation of Functions. Entropy 2013, 15, 4188-4198.

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