The development of the wind power market has led various countries to begin shifting the
construction of wind farms from land to offshore. Accurately predicting the short-term wind …

Reservoir computing systems, a class of recurrent neural networks, have recently been
exploited for model-free, data-based prediction of the state evolution of a variety of chaotic …

Powerful knowledge acquisition tools and techniques have the ability to increase both the
quality and the quantity of knowledge-based systems for real-world problems. In this paper …

Residual analysis using hybrid Elman–NARX neural network along with embedding
theorem is used to analyze and predict chaotic time series. Using embedding theorem, the …

Cooperative coevolution decomposes a problem into subcomponents and employs
evolutionary algorithms for solving them. Cooperative coevolution has been effective for …

Background Selecting a subset of relevant properties from a large set of features that
describe a dataset is a challenging machine learning task. In biology, for instance, the …

Determining the optimal number of hidden nodes and their proper initial locations are
essentially crucial before the wavelet neural networks (WNNs) start their learning process. In …

Collaboration enables weak species to survive in an environment where different species
compete for limited resources. Cooperative coevolution (CC) is a nature-inspired …

Although much efforts have been devoted to the optimal design of the energy systems, there
is a research gap about the multi-year load growth-based optimal planning of microgrids …

Time series forecasting has important theoretical significance and engineering application
value. A number of studies have shown that hybrid modelling is very successful in various …