The single-layer perceptron, introduced by Rosenblatt in 1958, is one of the earliest and
simplest neural network models. However, it is incapable of classifying linearly inseparable …

This paper considers a class of online gradient learning methods for backpropagation (BP)
neural networks with a single hidden layer. We assume that in each training cycle, each …

This paper investigates new learning algorithms (LF I and LF II) based on Lyapunov function
for the training of feedforward neural networks. It is observed that such algorithms have …

In this paper, we propose a novel variant of the algorithm to improve the generalization
performance for Elman neural networks (ENN). Here, the weight decay term, also called L 2 …

New text categorization models using back-propagation neural network (BPNN) and
modified back-propagation neural network (MBPNN) are proposed. An efficient feature …

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 …

Wind turbine power curve is an indicator of wind turbine performance and important input of
wind farm design or power prediction, therefore can serve the system planning and …

In this paper, two simple-structure neural networks based on the error back-propagation
(BP) algorithm (ie, BP-type neural networks, BPNNs) are proposed, developed, and …

Appropriate vehicle lateral stability control is the key to ensure vehicle driving safety,
whereas accurate lateral stability recognition can help improve the performance of vehicle …

The online backpropagation (BP) training procedure has been extensively explored in
scientific research and engineering applications. One of the main factors affecting the …