Rbf neural network - Open Access .click

Results 41 to 50 of about 14,713 (239)

PrOMet: Accurate and Robust Metal‐Binding Site and Type Prediction

Advanced Science, EarlyView.
PrOMet is a two‐stage deep learning framework for jointly predicting metal‐binding sites and ion identities from protein structures. Surface candidate sites are first pruned, followed by refinement of the most probable sites and metal types. This hierarchical strategy enables robust and accurate metal prediction for cryo‐EM and AlphaFold‐derived ...
Gangeun Park, Chaok Seok, Hahnbeom Park
wiley +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

LLM‐Assisted Design of Ultralow‐κ$\kappa$ Chalcogenides

Advanced Science, EarlyView.
An AI assisted, physics guided workflow accelerates the discovery of ultralow thermal conductivity materials by combining Bayesian optimization, evidence conditioned LLM reasoning, and automated physics‐based validation. The framework uncovers dynamically stable Ag based chalcogenides with ultralow thermal conductivity and promising thermoelectric ...
Yujie Liu +9 more
wiley +1 more source

Research on Neural Network Terminal Sliding Mode Control of Robotic Arms Based on Novel Reaching Law and Improved Salp Swarm Algorithm

Actuators, 2023
Modeling errors and external disturbances have significant impacts on the control accuracy of robotic arm trajectory tracking. To address this issue, this paper proposes a novel method, the neural network terminal sliding mode control (ALSSA-RBFTSM ...
Jianguo Duan +3 more
doaj +1 more source

Self‐Driving Laboratory Optimizes the Lower Critical Solution Temperature of Thermoresponsive Polymers

Advanced Intelligent Discovery, EarlyView.
A low‐cost, self‐driving laboratory is developed to democratize autonomous materials discovery. Using this "frugal twin" hardware architecture with Bayesian optimization, the platform rapidly converges to target lower critical solution temperature (LCST) values while self‐correcting from off‐target experiments, demonstrating an accessible route to data‐
Guoyue Xu, Renzheng Zhang, Tengfei Luo
wiley +1 more source

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley +1 more source

Research on location and layout of auto-body sheet metal based on NSGA-Ⅱ and RBF neural network

Journal of Hebei University of Science and Technology, 2019
In order to solve the problem of low efficiency and easy clamping deformation in the location layout design of auto-body sheet metal,alocation layout design method of auto-body sheet metal based on NSGA-Ⅱ and RBF neural network is proposed.With the ...
Peng WANG, Jiachuan XU, Fan CAO, Di LI
doaj +1 more source

Deep Learning–Based Extraction of Promising Material Groups and Common Features from High‐Dimensional Data: A Case of Optical Spectra of Inorganic Crystals

Advanced Intelligent Discovery, EarlyView.
We report a novel interpretation method for deep learning models based on feature extraction and clustering. Applying this method to an atomistic line graph neural network (ALIGNN) model trained on optical absorption spectra of 2,681 inorganic compounds obtained from first‐principles calculations, we successfully identify key factors underlying ...
Akira Takahashi +3 more
wiley +1 more source

An RBF Neural Network Combined with OLS Algorithm and Genetic Algorithm for Short-Term Wind Power Forecasting

Journal of Applied Mathematics, 2013
An accurate forecasting method for power generation of the wind energy conversion system (WECS) is urgently needed under the relevant issues associated with the high penetration of wind power in the electricity system. This paper proposes a hybrid method
Wen-Yeau Chang
doaj +1 more source

Research of time-delay control of wireless network based on RBF neural network PID control

Gong-kuang zidonghua, 2013
In view of problems that uncertain time-delay influences on control performance of wireless network control system and normal PID control strategy cannot meet control requirement of network, the paper proposed a time-delay control scheme of wireless ...
SUN Xiao-xi, HUANG You-rui, QU Li-guo
doaj +1 more source

mechanical engineering and machinery