Results 41 to 50 of about 1,355,935 (279)
A Novel Kernel for RBF Based Neural Networks [PDF]
Abstract and Applied Analysis, 2014 Radial basis function (RBF) is well known to provide excellent performance in function approximation and pattern classification. The conventional RBF uses basis functions which rely on distance measures such as Gaussian kernel of Euclidean distance (ED) between feature vector and neuron’s center, and so forth.
Wasim Aftab +2 more
openaire +4 more sources
Artificial Neural Network Approaches for Predicting the Heat Transfer in a Mini-Channel Heatsink with Alumina/Water Nanofluid [PDF]
Journal of Heat and Mass Transfer ResearchThis work uses artificial neural networks to evaluate heat transfer in a mini-channel heatsink using an alumina/water nanofluid. The multi-layer perceptron (MLP) and radial basis function (RBF) neural networks are employed for the modeling.
Mohammad Mahdi Tafarroj +3 more
doaj +1 more source
Prediction in Photovoltaic Power by Neural Networks [PDF]
, 2017 The ability to forecast the power produced by renewable energy plants in the short and middle term is a key issue to allow a high-level penetration of the distributed generation into the grid infrastructure. Forecasting energy production is mandatory for
Altilio, Rosa +3 more
core +1 more source
IET Networks, EarlyView., 2022 Abstract Sensor technology advancements have provided a viable solution to fight COVID and to develop healthcare systems based on Internet of Things (IoTs). In this study, image processing and Artificial Intelligence (AI) are used to improve the IoT framework.
Noor M Allayla +2 more
wiley +1 more source
Parallel implementation of RBF neural networks [PDF]
, 1996 This report presents several parallel implementations, on a MIMD machine, of a learning algorithm called OLS (Orthogonal Least Squares) for RBF (Radial Basis Function) neural networks. The sequential version is first described, and a straightforward parallel version is proposed.
V. Demian +3 more
openaire +1 more source
Efficient training of RBF neural networks for pattern recognition [PDF]
IEEE Transactions on Neural Networks, 2001 The problem of training a radial basis function (RBF) neural network for distinguishing two disjoint sets in R(n) is considered. The network parameters can be determined by minimizing an error function that measures the degree of success in the recognition of a given number of training patterns.
F. LAMPARIELLO, SCIANDRONE, MARCO
openaire +5 more sources
GRNN-Based Scattering Parameter Modeling Investigation for HBT at Different Temperature
IEEE Access, 2023 In this paper, the scattering parameter (S-parameter) modeling method for heterojunction bipolar transistor (HBT) at different temperatures is investigated. S-parameters of HBT at different temperatures are randomly divided into training and testing sets,
Qian Lin, Xiao-Zheng Wang, Hai-Feng Wu
doaj +1 more source
Predicting real-time roadside CO and NO2 concentrations using neural networks [PDF]
, 2008 The main aim of this paper is to develop a model based on neural network (NN) theory to estimate real-time roadside CO and $hbox{NO}_{2}$ concentrations using traffic and meteorological condition data.
Bell, M.C., Chen, H., Zito, P.
core +1 more source
Using growing RBF-nets in rubber industry process control [PDF]
, 2010 This paper describes the use of a Radial Basis Function (RBF) neural network in the approximation of process parameters for the extrusion of a rubber profile in tyre production. After introducing the rubber industry problem, the RBF network model and the
Brause, Rüdiger W., Pietruschka, Ulf
core
Advanced Materials, EarlyView.Screen gate‐based transistors are presented, enabling tunable analog sigmoid and Gaussian activations. The SA‐transistor improves MRI classification accuracy, while the GA‐transistor supports precise Gaussian kernel tuning for forecasting. Both functions are implemented in a single device, offering compact, energy‐efficient analog AI processing ...
Junhyung Cho +9 more
wiley +1 more source