Purpose The purpose of this paper is to conduct a comprehensive review of the noteworthy
contributions made in the area of the Feedforward neural network (FNN) to improve its …

Recurrent neural networks (RNNs) are widely utilized in time series forecasting tasks. In
practical applications, there are noises in real-life time series data. A model's generalization …

Undersampling is a widely adopted method to deal with imbalance pattern classification
problems. Current methods mainly depend on either random resampling on the majority …

In this paper, we first define a measure of statistical sensitivity of a zero-order Takagi–
Sugeno (TS) fuzzy neural network (FNN) with respect to perturbation of weights and …

We investigate essential relationships between generalization capabilities and fuzziness of
fuzzy classifiers (viz., the classifiers whose outputs are vectors of membership grades of a …

Solar radiation forecasting is a key technology to improve the control and scheduling
performance of photovoltaic power plants. In this paper, a deep learning based hybrid …

In this paper, a self-organizing radial basis function (SORBF) neural network is designed to
improve both accuracy and parsimony with the aid of adaptive particle swarm optimization …

A model-independent sensitivity analysis for (deep) neural network, Bilateral sensitivity
analysis (BiSA), is proposed to measure the relationship or dependency between neurons …

The concept of ensemble learning offers a promising avenue in learning from data streams
under complex environments because it better addresses the bias and variance dilemma …

This paper investigates a relationship between the fuzziness of a classifier and the
misclassification rate of the classifier on a group of samples. For a given trained classifier …