Chaotic time series widely exists in nature and society (eg, meteorology, physics,
economics, etc.), which usually exhibits seemingly unpredictable features due to its inherent …

Chaotic time series prediction, a challenging research topic in dynamic system modeling,
has drawn great attention from researchers around the world. In recent years extensive …

The broad learning system (BLS) is an emerging approach for effective and efficient
modeling of complex systems. The inputs are transferred and placed in the feature nodes …

Reservoir computing is a machine learning method that is closely linked to dynamical
systems theory. This connection is highlighted in a brief introduction to the general concept …

Spiking neural P (SNP) systems are a class of distributed and parallel neural-like computing
models that are inspired by the mechanism of spiking neurons and are 3rd-generation …

Due to the strong randomness of wind speed, wind power generation is difficult to integrate
into the grid. It is very important to predict wind speed reliably and accurately so that wind …

Echo state network (ESN) is a reservoir computing structure consisting randomly generated
hidden layer which enables a rapid learning and extrapolation process. On the other hand …

As a recurrent neural network that requires no training, the reservoir computing (RC) model
has attracted widespread attention in the last decade, especially in the context of time series …

Noisy time series prediction is a hot research topic in practical applications. Echo state
networks (ESNs) have superior performance on time series prediction. However, the ill …

In this paper, considering the infinite memory capability of fractional-order differential
equations and the advantages of broad echo state network, a broad fractional-order echo …