The optimal generation of initial connection weight parameters and dynamic updating
strategies of connection weights are critical for adjusting the performance of back …

Radial basis function neural network (RBFN) has the power of the universal function
approximation. But how to construct an RBFN to solve a given problem is usually not …

Based on self-growing probabilistic decision-based neural networks (SPDNNs), user
adaptation of the parameters of SPDNN is formulated as incremental reinforced and anti …

The problem of training a radial basis function (RBF) neural network for distinguishing two
disjoint sets in R/sup n/is considered. The network parameters can be determined by …

This paper proposes field-programmable gate array (FPGA) acceleration on a scalable multi-
layer perceptron (MLP) neural network for classifying handwritten digits. First, an …

Recognition of handwritten digits is one of the most important and challenging issues in
recent decades in the field of computer science. Its cursive nature, the right to left writing …

The writing style of the same writer varies from instance to instance in Arabic and English
handwritten digit recognition, making handwritten digit recognition challenging. Currently …

Recent advances in training deep (multi-layer) architectures have inspired a renaissance in
neural network use. For example, deep convolutional networks are becoming the default …

Results of recent research suggest that carefully designed multiplayer neural networks with
local “receptive fields” and shared weights may be unique in providing low error rates on …

We introduce the fractional-order global optimal backpropagation machine, which is trained
by an improved fractional-order steepest descent method (FSDM). This is a fractional-order …