Results 91 to 100 of about 538,196 (286)
Fully supervised training of Gaussian radial basis function networks in WEKA [PDF]
, 2014 Radial basis function networks are a type of feedforward network with a long history in machine learning. In spite of this, there is relatively little literature on how to train them so that accurate predictions are obtained.
Frank, Eibe
core +1 more source
Radial Basis Function Networks for Conversion of Sound Spectra
EURASIP Journal on Advances in Signal Processing, 2001 In many advanced signal processing tasks, such as pitch shifting, voice conversion or sound synthesis, accurate spectral processing is required. Here, the use of Radial Basis Function Networks (RBFN) is proposed for the modeling of the spectral changes ...
Carlo Drioli
doaj +1 more source
Advanced Functional Materials, EarlyView.Rational halogen mixing strategy was employed to shift the bandgap of Cs2PbBr2I2 from ultraviolet to visible region, enabling first realization of a visible‐light photodetector with this 2D layered Ruddlesden‐Popper perovskite material. Under illumination, light‐induced internal field forms and drives trap‐mediated persistent photoconductivity ...
Md Fahim Al Fattah +11 more
wiley +1 more source
On Comparison between Radial Basis Function and Wavelet Basis Functions Neural Networks
Ibn Al-Haitham Journal for Pure and Applied Sciences, 2017 In this paper we study and design two feed forward neural networks. The first approach uses radial basis function network and second approach uses wavelet basis function network to approximate the mapping from the input to the output space.
L.N.M. Tawfiq, T.A.M. Rashid
doaj
Organisms modeling: The question of radial basis function networks
ITM Web of Conferences, 2014 There exists usually a gap between bio-inspired computational techniques and what biologists can do with these techniques in their current researches. Although biology is the root of system-theory and artifical neural networks, computer scientists are ...
Muzy Alexandre +2 more
doaj +1 more source
Predicting Atomic Charges in MOFs by Topological Charge Equilibration
Advanced Functional Materials, EarlyView.An atomic charge prediction method is presented that is able to accurately reproduce ab‐initio‐derived reference charges for a large number of metal–organic frameworks. Based on a topological charge equilibration scheme, static charges that fulfill overall neutrality are quickly generated.
Babak Farhadi Jahromi +2 more
wiley +1 more source
Advanced Functional Materials, EarlyView.Covalent organic frameworks (COFs) with metals have been recognized as versatile platforms for photocatalytic CO2 reduction (CO2PRR). Herein, an overview of metal integration strategies for COFs is systematically summarized. Regulatory mechanisms and structure–activity relationships between metal integration and COF‐based CO2PRR are emphasized.
Jie He +5 more
wiley +1 more source
Jixie chuandong, 2004 Radial basis function neural network is a type of three-layer feedforward non-linear network. It has many good properties, such as powerful ability for function approximation, classification.
陆爽, 侯跃谦, 田野
doaj
Advanced Functional Materials, EarlyView.Dicarboxylate‐modified anionic hairy cellulose nanocrystals exhibit a high selectivity for dysprosium(III) over neodymium(III). This selectivity arises from disordered dicarboxylate cellulose “hairs” that enable cooperative ionic coordination, hydrogen bonding, and strain‐induced conformational shrinkage.
Roya Koshani +6 more
wiley +1 more source
Trap‐Assisted Transport and Neuromorphic Plasticity in Lead‐Free 2D Perovskites PEA2SnI4
Advanced Functional Materials, EarlyView.An artificial retina built from lead‐free layered perovskite (PEA)2SnI4 converts light input into a persistent photocurrent and sums successive flashes over time. Micro/nanocrystals integrated on electrodes act as synapse‐like pixels that perform temporal integration directly in hardware. This in‐sensor preprocessing merges detection and computation on
Ofelia Durante +17 more
wiley +1 more source