Results 21 to 30 of about 6,478,030 (244)
Identification of Nonlinear Systems Using Radial Basis Function Neural Network [PDF]
, 2014 This paper uses the radial basis function neural network (RBFNN) for system identification of nonlinear systems. Five nonlinear systems are used to examine the activity of RBFNN in system modeling of nonlinear systems; the five nonlinear systems are ...
Pislaru, Crinela +3 more
core +3 more sources
Similarity of Neural Networks with Gradients
CoRR, 2020 A suitable similarity index for comparing learnt neural networks plays an important role in understanding the behaviour of the highly-nonlinear functions, and can provide insights on further theoretical analysis and empirical studies. We define two key steps when comparing models: firstly, the representation abstracted from the learnt model, where we ...
Shuai Tang +4 more
openaire +3 more sources
Gradients as a Measure of Uncertainty in Neural Networks [PDF]
2020 IEEE International Conference on Image Processing (ICIP), 2020 IEEE International Conference on Image Processing (ICIP ...
Jinsol Lee, Ghassan AlRegib
openaire +4 more sources
New Scaled Conjugate Gradient Algorithm for Training Artificial Neural Networks Based on Pure Conjugacy Condition [PDF]
Kirkuk Journal of Science, 2015 Conjugate gradient methods constitute excellent neural network training methods characterized by their simplicity efficiency and their very low memory requirements.
Khalil K. Abbo, Hind H. Mohamed
doaj +1 more source
IEEE Access, 2020 Two different-level schemes are researched in this article for achieving the kinematic control of redundant manipulators, one of which is exploited at the acceleration level, and the other is at the jerk level.
Li He, Dan Su, Mei Liu, Zhiguan Huang
doaj +1 more source
An efficient algorithm for data parallelism based on stochastic optimization
Alexandria Engineering Journal, 2022 Deep neural network models can achieve greater performance in numerous machine learning tasks by raising the depth of the model and the amount of training data samples.
Khalid Abdulaziz Alnowibet +3 more
doaj +1 more source
Gradient Boosting Neural Networks: GrowNet
CoRR, 2020 A novel gradient boosting framework is proposed where shallow neural networks are employed as ``weak learners''. General loss functions are considered under this unified framework with specific examples presented for classification, regression, and learning to rank.
Badirli, Sarkhan +5 more
openaire +2 more sources
Gradient Techniques To Predict Distributed Denial-Of-Service Attack
Iraqi Journal for Computer Science and Mathematics, 2022 A distributed denial-of-service (DDoS) attack attempts to prevent people from accessing a server. A website may become inaccessible due to a DDoS attack because the server is inundated with fake requests and cannot handle real ones.
Roheen Qamar
doaj +1 more source
Universality of gradient descent neural network training [PDF]
Neural Networks, 2022 It has been observed that design choices of neural networks are often crucial for their successful optimization. In this article, we therefore discuss the question if it is always possible to redesign a neural network so that it trains well with gradient descent.
openaire +5 more sources
Weight and Gradient Centralization in Deep Neural Networks [PDF]
, 2021 Batch normalization is currently the most widely used variant of internal normalization for deep neural networks. Additional work has shown that the normalization of weights and additional conditioning as well as the normalization of gradients further improve the generalization. In this work, we combine several of these methods and thereby increase the
Wolfgang Fuhl, Enkelejda Kasneci
openaire +3 more sources