Results 31 to 40 of about 6,478,030 (244)
GRADIENT-ENHANCED DEEP NEURAL NETWORK APPROXIMATIONS
Journal of Machine Learning for Modeling and Computing, 2022 We propose in this work the gradient-enhanced deep neural network (DNN) approach for function approximations and uncertainty quantification. More precisely, the proposed approach adopts both the function evaluations and the associated gradient information to yield enhanced approximation accuracy. In particular, the gradient information is included as a
Xiaodong Feng, Li Zeng
openaire +2 more sources
Traffic sign recognition algorithm based on improved convolution neural network
上海师范大学学报. 自然科学版, 2018 For the problem of low traffic sign recognition rate due to gradient diffusion in convolution neural network(CNN), an improved convolution neural network was proposed.
ZHU Yongjia, ZHANG Jing
doaj +1 more source
Optimization and Application of Fuzzy Neural Network
Journal of Harbin University of Science and Technology, 2020 In order to improve the generalization ability，convergence speed and reduce the error of the fuzzyneural network，the method of using momentum gradient descent algorithm and ＲMSprop optimization algorithm to optimize the fuzzy neural network is proposed ...
LI Hao-nan, LIU Yong
doaj +1 more source
Fixed-Time Convergent Gradient Neural Network for Solving Online Sylvester Equation
Mathematics, 2022 This paper aims at finding a fixed-time solution to the Sylvester equation by using a gradient neural network (GNN). To reach this goal, a modified sign-bi-power (msbp) function is presented and applied on a linear GNN as an activation function ...
Zhiguo Tan
doaj +1 more source
Applied Computational Intelligence and Soft Computing, 2021 COVID-19 is a type of an infectious disease that is caused by the new coronavirus. The spread of COVID-19 needs to be suppressed because COVID-19 can cause death, especially for sufferers with congenital diseases and a weak immune system.
Syaiful Anam +5 more
doaj +1 more source
Entropy, 2023 Physics-informed neural networks (PINNs) are effective for solving partial differential equations (PDEs). This method of embedding partial differential equations and their initial boundary conditions into the loss functions of neural networks has ...
Kuo Sun, Xinlong Feng
doaj +1 more source
An Adaptive Capsule Network for Hyperspectral Remote Sensing Classification
Remote Sensing, 2021 The capsule network (Caps) is a novel type of neural network that has great potential for the classification of hyperspectral remote sensing. However, the Caps suffers from the issue of gradient vanishing.
Xiaohui Ding +6 more
doaj +1 more source
Journal of Flood Risk Management, 2020 This study aimed to develop novel flood forecasting models using artificial neural network (ANN) algorithms. The models were assessed and validated for the case study of an alluvial river in the Kashmir region of the Indian Himalayas‐River Jhelum.
Ruhhee Tabbussum, Abdul Qayoom Dar
doaj +1 more source
Gradient Descent for Spiking Neural Networks
CoRR, 2017 Much of studies on neural computation are based on network models of static neurons that produce analog output, despite the fact that information processing in the brain is predominantly carried out by dynamic neurons that produce discrete pulses called spikes.
Dongsung Huh, Terrence J. Sejnowski
openaire +4 more sources
Enhanced gradient learning for deep neural networks [PDF]
IET Image Processing, 2021 Abstract Deep neural networks have achieved great success in both computer vision and natural language processing tasks. How to improve the gradient flows is crucial in training very deep neural networks. To address this challenge, a gradient enhancement approach is proposed through constructing the short circuit neural connections ...
Ming Yan 0007 +5 more
openaire +2 more sources