Non-coding RNAs (ncRNAs) play an important role in revealing the mechanism of human
disease for anti-tumor and anti-virus substances. Detecting subcellular locations of ncRNAs …

Although the practicability of using wavelet neural networks (WNNs) in nonlinear function
approximation has been addressed extensively, selecting the optimal number of hidden …

A radial basis function neural network (RBFNN), with a strong function approximation ability,
was proven to be an effective tool for nonlinear process modeling. However, in many …

The real-time availability of key water quality parameters is of great importance for an
advanced and optimized process control in wastewater treatment plants (WWTPs). However …

Monitoring and managing wastewater treatment plants (WWTPs) is crucial for environmental
protection. The presection of the quality of treated water is essential for energy efficient …

Soft sensors have been widely applied to predict key variables that are difficult to measure
for industrial process modeling. In this paper, a novel randomized interval type-2 fuzzy …

Due to significant differences in data distribution under different working conditions during
Pichia pastoris biochemical reaction process, traditional soft-sensor model suffer from the …

Numerous studies on learning algorithms have been done in the neural networks
community, however concerning to training strategy is limited. In this article, to enhance the …

Aiming to improve the prediction accuracy and to obtain a compact structure, an online
adjusting radial basis function neural network (OA-RBFNN) is proposed in this paper. The …

Industrial processes often operate in the complex dynamic environment. They challenge the
cost-effective and reliable operation of industrial processes. This article proposes a …