  Floating centroid method (FCM) offers an efficient way to solve a
fixed-centroid problem for the neural network classifiers. However,
evolutionary computation as its optimization method restrains the FCM to
achieve satisfactory performance for different neural network structures,
because of the high computational complexity and inefficiency. Traditional
gradient-based methods have been extensively adopted to optimize the neural
network classifiers. In this study, a gradient-based floating centroid (GDFC)
method is introduced to address the fixed centroid problem for the neural
network classifiers optimized by gradient-based methods. Furthermore, a new
loss function for optimizing GDFC is introduced. The experimental results
display that GDFC obtains promising classification performance than the
comparison methods on the benchmark datasets.
