A global neural network learning machine: coupled integer and fractional calculus operator with an adaptive learning scheme
DOI10.1016/j.neunet.2021.06.021zbMath1521.68173OpenAlexW3173510166MaRDI QIDQ6079137
Huaqing Zhang, Xuetao Xie, Tingwen Huang, Yi-Fei Pu, Bingran Zhang, Jian Wang
Publication date: 28 September 2023
Published in: Neural Networks (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.neunet.2021.06.021
Evolutionary algorithms, genetic algorithms (computational aspects) (68W50) Nonconvex programming, global optimization (90C26) Learning and adaptive systems in artificial intelligence (68T05) Fractional programming (90C32) Approximation methods and heuristics in mathematical programming (90C59)
Related Items (1)
Cites Work
- Surpassing the fractional derivative: concept of the memory-dependent derivative
- The fractional calculus. Theory and applications of differentiation and integration to arbitrary order
- A note on short memory principle of fractional calculus
- Multilayer feedforward networks are universal approximators
- Study on fractional order gradient methods
- A learning rule for very simple universal approximators consisting of a single layer of perceptrons
- A novel fractional gradient-based learning algorithm for recurrent neural networks
- Fractional-order gradient descent learning of BP neural networks with Caputo derivative
- Fully complex conjugate gradient-based neural networks using Wirtinger calculus framework: deterministic convergence and its application
- Steepest Descent Algorithms for Optimization Under Unitary Matrix Constraint
- Improved Quasi-Newton Adaptive-Filtering Algorithm
- Fractional Differential Mask: A Fractional Differential-Based Approach for Multiscale Texture Enhancement
This page was built for publication: A global neural network learning machine: coupled integer and fractional calculus operator with an adaptive learning scheme
