Results 81 to 90 of about 27,765 (261)

Feedforward neuronske mreže: Levenberg-Marquardt optimizacija i optimal brain surgeon pruning / Feedforward neural network: the Levenberg-Marquardt opitmization and the Optimal Brain Surgeon Pruning / Нейронные сети с прямой связью: алгоритм Левенберга — Марквардта и оптимальный нейрохирургический прунинг

Vojnotehnički Glasnik, 2015
This paper presents the training, testing and pruning of a feedforward neural network with one hidden layer that was used for the prediction of the vowel ”a”.
Danijela D. Protić
doaj   +1 more source

Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy

Advanced Intelligent Systems, EarlyView.
This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne, Haodong Yao, Liyuan Wu, Yihe Pang, Fazhi Qi, Lina Zhao   +5 more
wiley   +1 more source

Skin layer classification by feedforward neural network in bioelectrical impedance spectroscopy. [PDF]

J Electr Bioimpedance, 2023
Ibrahim KA, Baidillah MR, Wicaksono R, Takei M.   +3 more
europepmc   +1 more source

The Key Technologies and Technological Evolution Mechanisms of Robotic Systems for Fracture Reduction Surgery

Advanced Intelligent Systems, EarlyView.
This review traces three decades of technological evolution in robot‐assisted fracture reduction systems, from early robotic concepts to clinically oriented platforms. Key advances in robotic architecture, navigation, path planning, interaction control, and surgical integration are summarized, while current translational challenges and future ...
Shaolong Kuang, Xiaoyang Hu, Jianhua Li, Fang Wang, Xue Zhang, Lihai Zhang   +5 more
wiley   +1 more source

Quaternion Wavelet Transform and a Feedforward Neural Network-Aided Intelligent Distributed Optical Fiber Sensing System. [PDF]

Sensors (Basel), 2023
Fan L, Wang Y, Zhang H, Li C, Huang X, Zhang Q, Xin X.   +6 more
europepmc   +1 more source

Vector‐Based SparseGPT: A Vector‐Based Sparsity Method for Large Language Model

Advanced Intelligent Systems, EarlyView.
Conventional pruning such as SparseGPT produces harmful accumulated errors through step‐by‐step weight editing. V‐SparseGPT prunes and updates target weights simultaneously to curb these errors for N:M structured sparsity. Block‐sparsity and layer‐parallel sparsity greatly boost pruning efficiency.
Junfei He, Mingxi Tan, Junchao Dong, Pengfei Ying   +3 more
wiley   +1 more source

Retracted: Potential Fault Diagnosis Method and Classification Accuracy Detection of IGBT Device Based on Improved Single Hidden Layer Feedforward Neural Network. [PDF]

Comput Intell Neurosci, 2023
Intelligence And Neuroscience C.
europepmc   +1 more source

Residual Strength Prediction of Chemically Reactive Two‐Phase Nanofluid Flow in a Bingham–Papanastasiou Rheological Theory Using a Morlet‐Based Wavelet Neural Network Approach

Asia-Pacific Journal of Chemical Engineering, EarlyView.
ABSTRACT The behavior of nanofluid flow involving a zero‐mass flux condition has received considerable interest because of a realistic scenario. In reality, this condition confines the optimistic accumulation or disappearance of nanoparticles past a sheet, constructing a more physically realistic demonstration through several applications, such as heat
Umair Khan, Hamza Rauf, Aurang Zaib, J. K. Madhukesh   +3 more
wiley   +1 more source

Application of machine learning methods in oil and gas geology and geophysics: from theoretical foundations to practical implementation

Известия высших учебных заведений: Нефть и газ
This paper presents a systematic analysis of modern machine learning methods and their practical applications in solving key problems in petroleum geology and geophysics.
N. R. Medvedev, S. R. Bembel, M. E. Savina   +2 more
doaj   +1 more source

A novel grey prediction model with a feedforward neural network based on a carbon emission dynamic evolution system and its application. [PDF]

Environ Sci Pollut Res Int, 2023
Nie W, Ao O, Duan H.
europepmc   +1 more source