For multilayer perceptron (MLP), the initial weights will significantly influence its
performance. Based on the enhanced fractional derivative extend from convex optimization …

In recent years, the research of artificial neural networks based on fractional calculus has
attracted much attention. In this paper, we proposed a fractional‐order deep …

Fractional calculus has been found to be a promising area of research for information
processing and modeling of some physical systems. In this paper, we propose a fractional …

Fractional calculus is an efficient tool, which has the potential to improve the performance of
gradient methods. However, when the first order gradient direction is generalized by …

The application of fractional calculus to signal processing and adaptive learning is an
emerging area of research. A novel fractional adaptive learning approach that utilizes …

In this paper, convergence capability of the conventional fractional order gradient methods
(FOGMs) is analyzed and a new FOGM with guaranteed and faster convergence ability is …

Find the global optimal solution of the model is one promising research topic in
computational intelligent community. Dependent on analogies to natural processes, the …

This article presents a novel fractional order LMS (FOLMS) algorithm, which involves a
variable gradient order scheme. The fractional order gradient descent method is revisited …

Approximation ability is of much importance for neural networks. The broad learning system
(BLS)(Chen and Liu, 2018), widely used in the industry with good performance, has been …

In this research, we propose a novel fractional gradient descent-based learning algorithm
(FGD) for the radial basis function neural networks (RBF-NN). The proposed FGD is the …