Using radial basis function networks for function approximation and classification
Y Wu, H Wang, B Zhang, KL Du - … Scholarly Research Notices, 2012 - Wiley Online Library
The radial basis function (RBF) network has its foundation in the conventional approximation
theory. It has the capability of universal approximation. The RBF network is a popular …
theory. It has the capability of universal approximation. The RBF network is a popular …
[ΒΙΒΛΙΟ][B] Neural networks in a softcomputing framework
Conventional model-based data processing methods are computationally expensive and
require experts' knowledge for the modelling of a system. Neural networks are a model-free …
require experts' knowledge for the modelling of a system. Neural networks are a model-free …
An efficient self-organizing RBF neural network for water quality prediction
HG Han, Q Chen, JF Qiao - Neural networks, 2011 - Elsevier
This paper presents a flexible structure Radial Basis Function (RBF) neural network (FS-
RBFNN) and its application to water quality prediction. The FS-RBFNN can vary its structure …
RBFNN) and its application to water quality prediction. The FS-RBFNN can vary its structure …
Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computing
Due to the strong competition that exists today, most manufacturing organizations are in a
continuous effort for increasing their profits and reducing their costs. Accurate sales …
continuous effort for increasing their profits and reducing their costs. Accurate sales …
Modeling biogas production from anaerobic wastewater treatment plants using radial basis function networks and differential evolution
This study presents a new method for modeling biogas production obtained from anaerobic
digestion treatment plants with increased accuracy. The method is based on artificial neural …
digestion treatment plants with increased accuracy. The method is based on artificial neural …
[ΒΙΒΛΙΟ][B] Neuronale Netze: Eine Einführung in die Neuroinformatik
R Brause - 2013 - books.google.com
Programmiersprachen und-systeme zur Simulation neuronaler Netze eingegangen. Der
Schwerpunkt des Buches liegt damit im Zusammenfassen und Ordnen einer Breite von …
Schwerpunkt des Buches liegt damit im Zusammenfassen und Ordnen einer Breite von …
Radial basis function network training using a nonsymmetric partition of the input space and particle swarm optimization
This paper presents a novel algorithm for training radial basis function (RBF) networks, in
order to produce models with increased accuracy and parsimony. The proposed …
order to produce models with increased accuracy and parsimony. The proposed …
Deep learning in structural bioinformatics: current applications and future perspectives
N Kumar, R Srivastava - Briefings in Bioinformatics, 2024 - academic.oup.com
In this review article, we explore the transformative impact of deep learning (DL) on
structural bioinformatics, emphasizing its pivotal role in a scientific revolution driven by …
structural bioinformatics, emphasizing its pivotal role in a scientific revolution driven by …
Generalized predictive control for industrial processes based on neuron adaptive splitting and merging RBF neural network
An adaptive generalized predictive control (GPC) scheme for an industrial process is
designed based on a neuron adaptive splitting and merging radial basis function neural …
designed based on a neuron adaptive splitting and merging radial basis function neural …
Nonlinear identification of a gasoline HCCI engine using neural networks coupled with principal component analysis
Homogeneous charge compression ignition (HCCI) is a futuristic combustion technology
that operates with high efficiency and reduced emissions. HCCI combustion is characterized …
that operates with high efficiency and reduced emissions. HCCI combustion is characterized …