Results 71 to 80 of about 59,339 (255)

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

Optimization of modeling and temperature control of air-cooled PEMFC based on TLBO-DE

Energy and AI
The temperature control of the air-cooled proton exchange membrane fuel cell (PEMFC) is important for effective and safe operation. To develop a practical and precise controller, this study combines the Radial Basis Function (RBF) neural network with ...
Pu He, Jun-Hong Chen, Chen-Zi Zhang, Zi-Yan Yu, Ming-Yang Wang, Jun-Yu Chen, Jia-Le Song, Yu-Tong Mu, Kun-Ying Gong, Wen-Quan Tao   +9 more
doaj   +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

Combining case based reasoning with neural networks [PDF]

, 1993
This paper presents a neural network based technique for mapping problem situations to problem solutions for Case-Based Reasoning (CBR) applications. Both neural networks and CBR are instance-based learning techniques, although neural nets work with ...
Murray-Smith, R, Thakar, S.
core   +2 more sources

A New RBF Neural Network With Boundary Value Constraints

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2009
We present a novel topology of the radial basis function (RBF) neural network, referred to as the boundary value constraints (BVC)-RBF, which is able to automatically satisfy a set of BVC. Unlike most existing neural networks whereby the model is identified via learning from observational data only, the proposed BVC-RBF offers a generic framework by ...
Hong, X., Chen, Sheng
openaire   +3 more sources

Bayesian Optimisation for the Experimental Sciences: A Practical Guide to Data‐Efficient Optimisation of Laboratory Workflows

Advanced Intelligent Systems, EarlyView.
This study provides an introduction to Bayesian optimisation targeted for experimentalists. It explains core concepts, surrogate modelling, and acquisition strategies, and addresses common real‐world challenges such as noise, constraints, mixed variables, scalability, and automation.
Chuan He, Martin Singull, T. Jesper Jacobsson   +2 more
wiley   +1 more source

Combined Prediction Energy Model at Software Architecture Level

IEEE Access, 2020
Accurate prediction of software energy consumption is of great significance for the sustainable development of the environment. In order to overcome the limitations of a single prediction method and further improve the prediction accuracy, a combined ...
Junke Li, Kai Liu, Mingjiang Li, Deguang Li   +3 more
doaj   +1 more source

Learning Bag-of-Features Pooling for Deep Convolutional Neural Networks

, 2017
Convolutional Neural Networks (CNNs) are well established models capable of achieving state-of-the-art classification accuracy for various computer vision tasks. However, they are becoming increasingly larger, using millions of parameters, while they are
Passalis, Nikolaos, Tefas, Anastasios
core   +1 more source

Slip‐Adaptive Neural Control of Gecko‐Inspired Adhesive Robots

Advanced Intelligent Systems, EarlyView.
This study introduces a neural adhesion controller to improve the stability of gecko‐inspired climbing robots. By integrating an echo state network and a multilayer perceptron, the system utilizes joint torque feedback to accurately estimate adhesion in both normal and shear directions and predict slips. This enables effective recovery from slip events,
Donghao Shao, Jian Chen, Guangyuan Zang, Yang Li   +3 more
wiley   +1 more source

Adaptive Neural Sliding Mode Control of Active Power Filter

Journal of Applied Mathematics, 2013
A radial basis function (RBF) neural network adaptive sliding mode control system is developed for the current compensation control of three-phase active power filter (APF). The advantages of the adaptive control, neural network control, and sliding mode
Juntao Fei, Zhe Wang
doaj   +1 more source