Title: Robust Stochastic Configuration Networks with Kernel Density Estimation,
Abstract: Neural networks have been widely used as predictive models to fit data
distribution, and they could be implemented through learning a collection of
samples. In many applications, however, the given dataset may contain noisy
samples or outliers which may result in a poor learner model in terms of
generalization. This paper contributes to a development of robust stochastic
configuration networks (RSCNs) for resolving uncertain data regression
problems. RSCNs are built on original stochastic configuration networks with
weighted least squares method for evaluating the output weights, and the input
weights and biases are incrementally and randomly generated by satisfying with
a set of inequality constrains. The kernel density estimation (KDE) method is
employed to set the penalty weights for each training samples, so that some
negative impacts, caused by noisy data or outliers, on the resulting learner
model can be reduced. The alternating optimization technique is applied for
updating a RSCN model with improved penalty weights computed from the kernel
density estimation function. Performance evaluation is carried out by a
function approximation, four benchmark datasets and a case study on engineering
application. Comparisons to other robust randomised neural modelling
techniques, including the probabilistic robust learning algorithm for neural
networks with random weights and improved RVFL networks, indicate that the
proposed RSCNs with KDE perform favourably and demonstrate good potential for
real-world applications.