Search Results - Backpropagation through time algorithms

Recurrent Neural Networks Trained With Backpropagation Through Time Algorithm to Estimate Nonlinear Load Harmonic Currents by Mazumdar, J., Harley, R.G.

“… This paper proposes a neural network solution to this problem. A recurrent neural network trained with the backpropagation through time training algorithm is used to find…”
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Research on Cascading Fault Location of Chemical Material Networks Based on BFS-Time-Reversal Backpropagation Algorithm by Wang, Zheng, Li, Huapeng, Liu, Ruijie, Hou, Jingmin, Dong, Ran, Hu, Yiyi, Jia, Xiaoping, Wang, Fang

“…)-time-reversal backpropagation algorithm to study…”
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Analisis Tingkat Akurasi Variasi Algoritma Min-Max Backpropagation sebagai Pre-Processing Data Time Series by Mandailina, Vera, Abdillah, Abdillah, Syaharuddin, Syaharuddin

“… This paper aims to test eight types of Min-Max backpropagation algorithms with several types of data, namely static data, seational data, monotonically fluctuating data up and down…”
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Event-based backpropagation can compute exact gradients for spiking neural networks by Wunderlich, Timo C., Pehle, Christian

“… was previously hindered by the existence of discrete spike events and discontinuities. For the first time, this work derives the backpropagation algorithm for a continuous…”
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Training Recurrent Neural Networks With the Levenberg-Marquardt Algorithm for Optimal Control of a Grid-Connected Converter by Fu, Xingang, Li, Shuhui, Fairbank, Michael, Wunsch, Donald C., Alonso, Eduardo

“… This paper explores how to incorporate FATT into the LM algorithm. The results show that the combination of the LM and FATT algorithms trains RNNs better than the conventional backpropagation through time algorithm…”
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A parallel compact Marine Predators Algorithm applied in time series prediction of Backpropagation neural network (BNN) and engineering optimization by Pan, Jeng-Shyang, Zhang, Zhen, Chu, Shu-Chuan, Zhang, Si-Qi, Wu, Jimmy Ming-Tai

“…This study introduces a novel approach for integrating a compact mechanism into the Marine Predator Algorithm (MPA…”
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Backpropagation through time and the brain by Lillicrap, Timothy P, Santoro, Adam

“… It has long been speculated that the backpropagation-of-error algorithm (backprop) may be a model of how the brain learns…”
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The Symplectic Adjoint Method: Memory-Efficient Backpropagation of Neural-Network-Based Differential Equations by Matsubara, Takashi, Miyatake, Yuto, Yaguchi, Takaharu

“… The backpropagation algorithm consumes memory in proportion to the number of uses times of the network size, causing practical difficulties…”
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Noise-boosted bidirectional backpropagation and adversarial learning by Adigun, Olaoluwa, Kosko, Bart

“… Special injected noise can then improve the algorithm’s training time and accuracy because backpropagation has a likelihood structure…”
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An Equivalence Between Adaptive Dynamic Programming With a Critic and Backpropagation Through Time by Fairbank, Michael, Alonso, Eduardo, Prokhorov, Danil

“… We extend DHP into a new algorithm that we call Value-Gradient Learning, VGL(λ), and prove equivalence of an instance of the new algorithm to Backpropagation Through Time for Control with a greedy policy…”
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The backpropagation algorithm implemented on spiking neuromorphic hardware by Renner, Alpha, Sheldon, Forrest, Zlotnik, Anatoly, Tao, Louis, Sornborger, Andrew

“… In particular, the workhorse of modern deep learning, the backpropagation algorithm, has proven difficult to translate to neuromorphic hardware…”
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Efficient training of spiking neural networks with temporally-truncated local backpropagation through time by Guo, Wenzhe, Fouda, Mohammed E., Eltawil, Ahmed M., Salama, Khaled Nabil

“… The well-known backpropagation through time (BPTT) algorithm proposed to train SNNs suffers from large memory footprint and prohibits backward and update unlocking…”
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Robust Neural Control of Discrete Time Uncertain Nonlinear Systems Using Sliding Mode Backpropagation Training Algorithm by Zaidi, Imen, Chtourou, Mohamed, Djemel, Mohamed

“… In previous works, the neural models are trained classically by backpropagation (BP) algorithm. In this work, the sliding mode-backpropagation…”
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Replay as a Basis for Backpropagation Through Time in the Brain by Cheng, Huzi, Brown, Joshua W

“… The function of the replay events is a subject of active debate. Recurrent connectivity, computational simulations show, enables sequence learning when combined with a suitable learning algorithm such as backpropagation through time (BPTT…”
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A Deep Convolutional Gated Recurrent Unit for CT Image Reconstruction by Ikuta, Masaki, Zhang, Jun

“…Computed tomography (CT) is one of the most important medical imaging technologies in use today. Most commercial CT products use a technique known as the…”
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Uniformly Stable Backpropagation Algorithm to Train a Feedforward Neural Network by de Jesús Rubio, José, Angelov, Plamen, Pacheco, Jaime

“… It is true that in some research papers this problem has been considered, but this concerns continuous-time NN…”
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Backpropagation Algorithms for a Broad Class of Dynamic Networks by Orlando De Jesus, Hagan, M.T.

“…: backpropagation-through-time (BPTT) and real-time recurrent learning (RTRL). The structure of the LDDN framework enables an efficient implementation of both algorithms for arbitrary dynamic networks…”
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A general backpropagation algorithm for feedforward neural networks learning by Xinghuo Yu, Efe, M.O., Kaynak, O.

“…A general backpropagation algorithm is proposed for feedforward neural network learning with time varying inputs…”
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A simplification of the backpropagation-through-time algorithm for optimal neurocontrol by Bersini, H., Gorrini, V.

“…Backpropagation-through-time (BPTT) is the temporal extension of backpropagation which allows a multilayer neural network to approximate an optimal state-feedback control law provided some prior knowledge (Jacobian matrices…”
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A new Neuro-Fuzzy Inference System with Dynamic Neurons (NFIS-DN) for system identification and time series forecasting by Samanta, S., Suresh, S., Senthilnath, J., Sundararajan, N.

“… The antecedent and the consequent parameters of NFIS-DN are updated using a self-regulated backpropagation through time learning algorithm…”
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