The integration of large-scale wind power introduces issues in modern power systems
operations due to its strong randomness and volatility. These issues can be resolved via …

In order to approximate the underlying process of temporal data, time series prediction has
been a hot research topic for decades. Develo** predictive models plays an important role …

Interest in using artificial neural networks (ANNs) for forecasting has led to a tremendous
surge in research activities in the past decade. While ANNs provide a great deal of promise …

Meshfree approximation methods are a relatively new area of research, and there are only a
few books covering it at present. Whereas other works focus almost entirely on theoretical …

Artificial Neural Networks (ANNs) are being used increasingly to predict and forecast water
resources variables. In this paper, the steps that should be followed in the development of …

Radial basis function (RBF) networks have advantages of easy design, good generalization,
strong tolerance to input noise, and online learning ability. The properties of RBF networks …

This work presents a new sequential learning algorithm for radial basis function (RBF)
networks referred to as generalized growing and pruning algorithm for RBF (GGAP-RBF) …

Parametric system identification, which is the process of uncovering the inherent dynamics
of a system based on the model built with the observed inputs and outputs data, has been …

Training artificial neural networks is considered as one of the most challenging machine
learning problems. This is mainly due to the presence of a large number of solutions and …

Feedforward artificial neural networks (ANNs) that are trained with the back-propagation
algorithm are a useful tool for modelling environmental systems. They have already been …