Results 21 to 30 of about 35,631 (308)

Radial Basis Function Neural Network Model for Dissolved Oxygen Concentration Prediction Based on an Enhanced Clustering Algorithm and Adam

IEEE Access, 2021
In fishery aquaculture, water quality directly determines the economic benefits of aquatic products, and dissolved oxygen is an important factor affecting water quality.
Dashe Li, Xueying Wang, Jiajun Sun, Yanli Feng   +3 more
doaj   +1 more source

Fully complex-valued radial basis function networks: orthogonal least squares regression and classification [PDF]

, 2008
We consider a fully complex-valued radial basis function (RBF) network for regression and classification applications. For regression problems, the locally regularised orthogonal least squares (LROLS) algorithm aided with the D-optimality experimental ...
Harris, C. J., Hong, Xia, Chen, Sheng, Hong, X., Harris, Chris J., Hanzo, Lajos, Hanzo, L., Chen, S.   +7 more
core   +1 more source

Equalizer Design: HBOA-DE-trained radial basis function neural networks

Egyptian Informatics Journal
Communication systems that rely on wireless technology require signal processing techniques to improve their channel performance. Wireless communications are susceptible to various signal distortions during transmission, including inter-symbol ...
Santosh Kumar Das, Satya Ranjan Pattanaik, Pradyumna Kumar Mohapatra, Saroja Kumar Rout, Abdulaziz S. Almazyad, Muhammed Basheer Jasser, Guojiang Xiong, Ali Wagdy Mohamed   +7 more
doaj   +1 more source

Construction of tunable radial basis function networks using orthogonal forward selection

, 2009
An orthogonal forward selection (OFS) algorithm based on leave-one-out (LOO) criteria is proposed for the construction of radial basis function (RBF) networks with tunable nodes.
Harris, C. J., Hong, Xia, Luk, B. L., Chen, Sheng, Hong, X., Harris, Chris J., Chen, S., Luk, Bing L.   +7 more
core   +1 more source

Identification of Nonlinear Systems Using Radial Basis Function Neural Network

, 2014
This paper uses the radial basis function neural network (RBFNN) for system identification of nonlinear systems. Five nonlinear systems are used to examine the activity of RBFNN in system modeling of nonlinear systems; the five nonlinear systems are ...
Pislaru, Crinela, Shebani, Amer, A. Shebani, C. Pislaru   +3 more
core   +1 more source

Developmental programmes drive cellular plasticity, disease progression and therapy resistance in lung adenocarcinoma

Molecular Oncology, EarlyView.
This study shows that lung adenocarcinomas exploit developmental branching morphogenesis to acquire a therapy resistant basal‐like tumour cell state. This process was found to be regulated by combined TP53 loss‐of‐function and type‐I interferon signalling, identifying a novel axis for biomarker and therapeutic target discovery.
Kamila J Bienkowska, Stephany Gallardo, Nur S. Zainal, Leena Arora, Matthew Ellis, Maria‐Antoinette Lopez, Judith Austine, Sai Pittla, Serena J Chee, Aiman Alzetani, Emily C Shaw, Christian H Ottensmeier, Gareth J Thomas, Christopher J Hanley   +13 more
wiley   +1 more source

Network Intrusion Detection Based on Extended RBF Neural Network With Offline Reinforcement Learning

IEEE Access, 2021
Network intrusion detection focuses on classifying network traffic as either normal or attack carrier. The classification is based on information extracted from the network flow packets.
Manuel Lopez-Martin, Antonio Sanchez-Esguevillas, Juan Ignacio Arribas, Belen Carro   +3 more
doaj   +1 more source

Online Learning with Radial Basis Function Networks

The Journal of Financial Data Science, 2022
Financial time series are characterised by their nonstationarity and autocorrelation. Even if these time series are differenced, technically ensuring their stationarity, they experience regular covariate shifts and concept drifts. Against this backdrop, we combine feature representation transfer with sequential optimisation to provide multi-horizon ...
Gabriel Borrageiro, Nick Firoozye, Paolo Barucca   +2 more
openaire   +3 more sources

Grey-box radial basis function modelling

, 2011
A fundamental principle in data modelling is to incorporate available a priori information regarding the underlying data generating mechanism into the modelling process.
Xia Hong, Hong, Xia, Sheng Chen, Chris J. Harris, Chen, Sheng, Harris, Chris J., Harris, Chris   +6 more
core   +1 more source

Molecular dynamics simulations of positively selected codons in FcγRI reveal novel biochemical binding properties

FEBS Open Bio, EarlyView.
Evolutionary analysis across 32 placental mammals identified positive selection at residues H148 and W149 in the immune receptor FcγR1. Ancestral reconstruction combined with molecular dynamics simulations reveals how these mutations may influence receptor structure and dynamics, providing insight into the evolution of antibody recognition and immune ...
David A. Young, Omar Arias‐Gaguancela, Fiona Gibson, Morgan Grainger, Jodie Score, Amit Nathubhai, Mauricio Cafiero, Lee Richard Machado   +7 more
wiley   +1 more source