Results 81 to 90 of about 1,466,211 (327)
Learning Topology and Dynamics of Large Recurrent Neural Networks
, 2014 Large-scale recurrent networks have drawn increasing attention recently because of their capabilities in modeling a large variety of real-world phenomena and physical mechanisms.
He, Yuejia, She, Yiyuan, Wu, Dapeng
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Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning [PDF]
Acta NumericaPhysics-informed neural networks (PINNs) and their variants have been very popular in recent years as algorithms for the numerical simulation of both forward and inverse problems for partial differential equations.
Tim De Ryck, Siddhartha Mishra
semanticscholar +1 more source
International Journal of Adaptive Control and Signal Processing, EarlyView.Summary Data‐driven forecasting of ship motions in waves is investigated through feedforward and recurrent neural networks as well as dynamic mode decomposition. The goal is to predict future ship motion variables based on past data collected on the field, using equation‐free approaches.
Matteo Diez +2 more
wiley +1 more source
Learning a DFT-based sequence with reinforcement learning: a NAO implementation
Paladyn, 2012 The implementation of sequence learning in robotic platforms offers several challenges. Deciding when to stop one action and continue to the next requires a balance between stability of sensory information and, of course, the knowledge about what action ...
Durán Boris, Lee Gauss, Lowe Robert
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A reinforcement learning theory for homeostatic regulation [PDF]
, 2011 Reinforcement learning models address animal’s behavioral adaptation to its changing “external” environment, and are based on the assumption that Pavlovian, habitual and goal-directed responses seek to maximize reward acquisition.
Gutkin, B. S., Keramati, M.
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A Q‐Learning Algorithm to Solve the Two‐Player Zero‐Sum Game Problem for Nonlinear Systems
International Journal of Adaptive Control and Signal Processing, Volume 39, Issue 3, Page 566-581, March 2025.A Q‐learning algorithm to solve the two‐player zero‐sum game problem for nonlinear systems. ABSTRACT This paper deals with the two‐player zero‐sum game problem, which is a bounded L2$$ {L}_2 $$‐gain robust control problem. Finding an analytical solution to the complex Hamilton‐Jacobi‐Issacs (HJI) equation is a challenging task.
Afreen Islam +2 more
wiley +1 more source
IEEE Access, 2018 Through exploration and exploitation of new knowledge emergence, the collective intelligent decision-making (CID) level of Web-of-Cells (WoC) proposed by ELECTRA will be dramatically improved.
Lefeng Cheng, Tao Yu
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Data expansion with Huffman codes [PDF]
, 1995 The following topics were dealt with: Shannon theory; universal lossless source coding; CDMA; turbo codes; broadband networks and protocols; signal processing and coding; coded modulation; information theory and applications; universal lossy source ...
Cheng, Jung-Fu +3 more
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Neural Network Adaptive Control With Long Short‐Term Memory
International Journal of Adaptive Control and Signal Processing, EarlyView.ABSTRACT In this study, we propose a novel adaptive control architecture that provides dramatically better transient response performance compared to conventional adaptive control methods. This is accomplished by the synergistic employment of a traditional adaptive neural network (ANN) controller and a long short‐term memory (LSTM) network.
Emirhan Inanc +4 more
wiley +1 more source
ITEGAM-JETIA, 2020 The design objective behind lead and lag compensator is to meet the relative stability as well as to meet desired performance. Both in time domain or frequency domain, the compensator design can be carried out.
Badri Narayan Mohapatra, Jijnyasa Joshi
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