Evaluation of proximity points for a class of neuron's network functions based on local fractional calculus

Rabha W. Ibrahim, Maslina Darus

Research output: Contribution to journalArticle

Abstract

In mathematical modeling of Artificial Neural Networks (ANN), a neuron's network function formulates as an arrangement of other functions. It represents a composition of other functions by using arrows between variables. In this paper, we introduce a new class of activation functions based on the concept of the univex function. This type of functions satisfies the convexity property. This property converges to the outcome of ANN. Moreover, we study the activation function by using the concept of approximation theory. Finally, we lay new connections of the nonlinear weighted sum depending on the fractional power. The simulation is introduced to maximize the utility function in a fractional cloud computing system.

Original languageEnglish
Pages (from-to)285-299
Number of pages15
JournalInternational Journal of Mathematics and Computer Science
Volume14
Issue number1
Publication statusPublished - 1 Jan 2019

Fingerprint

Fractional Calculus
Proximity
Neurons
Neuron
Evaluation
Activation Function
Artificial Neural Network
Fractional Powers
Approximation Theory
Weighted Sums
Cloud Computing
Utility Function
Mathematical Modeling
Chemical activation
Convexity
Arrangement
Neural networks
Approximation theory
Fractional
Maximise

Keywords

  • Activation function
  • ANN
  • Fractional calculus
  • Fractional difference problem
  • System operators

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Algebra and Number Theory
  • Statistics and Probability
  • Numerical Analysis
  • Modelling and Simulation
  • Discrete Mathematics and Combinatorics
  • Computational Mathematics
  • Applied Mathematics

Cite this

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