A Novel Adaptive Kernel for the RBF Neural Networks [PDF]
Circuits, Systems, and Signal Processing, 2016 In this paper, we propose a novel adaptive kernel for the radial basis function (RBF) neural networks. The proposed kernel adaptively fuses the Euclidean and cosine distance measures to exploit the reciprocating properties of the two. The proposed framework dynamically adapts the weights of the participating kernels using the gradient descent method ...
Shujaat Khan +3 more
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Frontiers in Earth Science, 2023 Risk assessment is critical to ensure the safe operation of oil and gas pipeline systems. The core content of such risk assessment is to determine the failure probability of the pipelines quantitatively and accurately.
Lexin Zhao +3 more
doaj +1 more source
Extending the functional equivalence of radial basis functionnetworks and fuzzy inference systems [PDF]
, 1996 We establish the functional equivalence of a generalized class of Gaussian radial basis function (RBFs) networks and the full Takagi-Sugeno model (1983) of fuzzy inference.
Haas, R., Hunt, K.J., Murray-Smith, R.
core +1 more source
Forecasting of financial data: a novel fuzzy logic neural network based on error-correction concept and statistics [PDF]
, 2018 First, this paper investigates the effect of good and bad news on volatility in the BUX return time series using asymmetric ARCH models. Then, the accuracy of forecasting models based on statistical (stochastic), machine learning methods, and soft ...
Marček, Dušan
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Combining Parametric and Non-parametric Algorithms for a Partially Unsupervised Classification of Multitemporal Remote-Sensing Images [PDF]
, 2002 In this paper, we propose a classification system based on a multiple-classifier architecture, which is aimed at updating land-cover maps by using multisensor and/or multisource remote-sensing images.
Bruzzone, Lorenzo +2 more
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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
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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
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
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Convergent Decomposition Techniques for Training RBF Neural Networks [PDF]
Neural Computation, 2001 In this article we define globally convergent decomposition algorithms for supervised training of generalized radial basis function neural networks. First, we consider training algorithms based on the two-block decomposition of the network parameters into the vector of weights and the vector of centers.
C. BUZZI, L. GRIPPO, SCIANDRONE, MARCO
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Embedded CRISPRi Enhances Gene‐Silencing Efficiency in Drosophila
Advanced Science, EarlyView.Current CRISPR interference (CRISPRi) technology in Drosophila has limited efficiency. This study introduces the emCRISPRi platform, which significantly enhances transcriptional silencing efficacy by embedding inhibitory domains within the dCas9 architecture.
Pengchong Fu +7 more
wiley +1 more source