Adaptive memetic method of multi-objective genetic evolutionary algorithm for backpropagation neural network
Sign Up to like & getrecommendations! Published in 2019 at "Neural Computing and Applications"
DOI: 10.1007/s00521-018-03990-0
Abstract: In recent years, multi-objective evolutionary optimization algorithms have shown success in different areas of research. Due to their efficiency and power, many researchers have concentrated on adapting evolutionary algorithms to generate Pareto solutions. This paper… read more here.
Keywords: algorithm; network; multi objective; method ... See more keywords
Hybrid no-propagation learning for multilayer neural networks
Sign Up to like & getrecommendations! Published in 2018 at "Neurocomputing"
DOI: 10.1016/j.neucom.2018.08.034
Abstract: Abstract A hybrid learning algorithm suitable for hardware implementation of multi-layer neural networks is proposed. Though backpropagation is a powerful learning method for multilayer neural networks, its hardware implementation is difficult due to complexities of… read more here.
Keywords: neural networks; hybrid propagation; multilayer neural; backpropagation ... See more keywords
Memorized Sparse Backpropagation
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.08.055
Abstract: Abstract Neural network learning is usually time-consuming since backpropagation needs to compute full gradients and backpropagate them across multiple layers. Despite its success of existing works in accelerating propagation through sparseness, the relevant theoretical characteristics… read more here.
Keywords: sparse backpropagation; backpropagation; memorized sparse; loss ... See more keywords
Continual Learning with Speculative Backpropagation and Activation History
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3166158
Abstract: Continual learning is gaining traction these days with the explosive emergence of deep learning applications. Continual learning suffers from a severe problem called catastrophic forgetting. It means that the trained model loses the previously learned… read more here.
Keywords: speculative backpropagation; backpropagation activation; continual learning; backpropagation ... See more keywords
Inverse Source Problem for a Host Medium Having Pointlike Inhomogeneities
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2018.2809913
Abstract: The reconstruction of a source embedded within a multipath environment, which is created by inserting a grid of point scatterers in the scene, is addressed. In particular, the source Fourier spectrum is assumed known so… read more here.
Keywords: resolution; source; inverse source; resolution improvement ... See more keywords
Levenberg–Marquardt Backpropagation Training of Multilayer Neural Networks for State Estimation of a Safety-Critical Cyber-Physical System
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Transactions on Industrial Informatics"
DOI: 10.1109/tii.2017.2777460
Abstract: As an important safety-critical cyber-physical system (CPS), the braking system is essential to the safe operation of the electric vehicle. Accurate estimation of the brake pressure is of great importance for automotive CPS design and… read more here.
Keywords: estimation; system; critical cyber; safety critical ... See more keywords
Backpropagation-Based Learning Techniques for Deep Spiking Neural Networks: A Survey.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2023.3263008
Abstract: With the adoption of smart systems, artificial neural networks (ANNs) have become ubiquitous. Conventional ANN implementations have high energy consumption, limiting their use in embedded and mobile applications. Spiking neural networks (SNNs) mimic the dynamics… read more here.
Keywords: backpropagation based; neural networks; spiking neural; backpropagation ... See more keywords
Dynamic Block-Wise Local Learning Algorithm for Efficient Neural Network Training
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Very Large Scale Integration (VLSI) Systems"
DOI: 10.1109/tvlsi.2021.3097341
Abstract: In the backpropagation algorithm, the error calculated from the output of the neural network should backpropagate the layers to update the weights of each layer, making it difficult to parallelize the training process and requiring… read more here.
Keywords: block; neural network; learning algorithm; local learning ... See more keywords
Backpropagation With Sparsity Regularization for Spiking Neural Network Learning
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Neuroscience"
DOI: 10.3389/fnins.2022.760298
Abstract: The spiking neural network (SNN) is a possible pathway for low-power and energy-efficient processing and computing exploiting spiking-driven and sparsity features of biological systems. This article proposes a sparsity-driven SNN learning algorithm, namely backpropagation with… read more here.
Keywords: neural network; network; spiking neural; sparsity ... See more keywords