An echo-state network (ESN) is an effective alternative to gradient methods for training recurrent
neural network. However, it is difficult to determine the structure (mainly the reservoir) of …

Protein kinases play central role in cellular regulation by catalyzing the phosphorylation of
target proteins involved in complex physiological processes, achieved by their structural …

The electrical current blockade of a peptide or protein threading through a nanopore can be
used as a fingerprint of the molecule in biosensor applications. However, threading of full-…

Multilayer echo state networks (ESNs) are powerful on learning hierarchical temporal
representation. However, how to determine the depth of multilayer ESNs is still an open issue. In …

In this paper, a self-organizing cascade neural network (SCNN) with random weights is
proposed for nonlinear system modeling. This SCNN is constructed via simultaneous structure …

The current research investigated whether employees' self‐construals moderated the effects
of supplementary fit and complementary fit on their work‐related outcomes (ie affective …

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-posed …

In this paper, a novel constructive algorithm, named fast cascade neural network (FCNN), is
proposed to design the fully connected cascade feedforward neural network (FCCFNN). First…

Reservoir computing (RC), with the idea of using a large randomly and sparsely connected
recurrent layer, has turned out to be an efficient paradigm for training recurrent neural …

The clustering of raw meal composition is of great significance for accurately dividing the
production conditions of raw meal composition and improving the quality of clinker calcination. …