Surrogate modeling, also called metamodeling, has evolved and been extensively used
over the past decades. A wide variety of methods and tools have been introduced for …

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 …

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Auto-encoder—a tricky three-layered neural network, known as auto-association before,
constructs the “building block” of deep learning, which has been demonstrated to achieve …

Recent breakthroughs in artificial intelligence (AI), and particularly in deep learning (DL),
have created tremendous excitement and opportunities in the earth and environmental …

In this paper, we develop an online sequential learning algorithm for single hidden layer
feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes in a …

Artificial neural networks (ANNs) becomes very popular tool in hydrology, especially in
rainfall–runoff modelling. However, a number of issues should be addressed to apply this …

Conventional model-based data processing methods are computationally expensive and
require experts' knowledge for the modelling of a system. Neural networks are a model-free …

The current study applies neural network (NN) modeling in forecasting lumpy demand. It is,
to the best of our knowledge, the first such study. Our study compares the performance of NN …

Managing intermittent demand is a vital task in several industrial contexts, and good
forecasting ability is a fundamental prerequisite for an efficient inventory control system in …