Adaptive Predefined-Time Tracking Control for Robotic Manipulator Based on Actor-Critic Reinforcement Learning [PDF]
Sensors (Basel)This paper proposes a novel predefined-time adaptive neural tracking control method for uncertain manipulator systems based on Actor-Critic reinforcement learning framework.
Qin Y, Sun Y, Huang J, Li Y.
europepmc +2 more sources
Design and Implementation of Novel LMI-Based Iterative Learning Robust Nonlinear Controller
Complexity, 2021 An iterative learning robust fault-tolerant control algorithm is proposed for a class of uncertain discrete systems with repeated action with nonlinear and actuator faults. First, by defining an actuator fault coefficient matrix, we convert the iterative
Saleem Riaz +3 more
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The Impact of a Construction Play on 5- to 6-Year-Old Children’s Reasoning About Stability
Frontiers in Psychology, 2020 TheoryYoung children have an understanding of basic science concepts such as stability, yet their theoretical assumptions are often not concerned with stability.
Anke Maria Weber +2 more
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Probability Based Stochastic Iterative Learning Control for Batch Processes With Actuator Faults
IEEE Access, 2019 This paper proposes a new stochastic composite iterative learning control for batch processes with actuator faults that happen with a certain kind of probability.
Limin Wang, Bingyun Li
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An Actor-Critic Framework for Online Control With Environment Stability Guarantee
IEEE Access, 2023 Online actor-critic reinforcement learning is concerned with training an agent on-the-fly via dynamic interaction with the environment. Due to the specifics of the application, it is not generally possible to perform long pre-training, as it is commonly ...
Pavel Osinenko +3 more
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Seven properties of self-organization in the human brain [PDF]
, 2020 The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience ...
Dresp-Langley, Birgitta
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Evolving stochastic learning algorithm based on Tsallis entropic index [PDF]
, 2005 In this paper, inspired from our previous algorithm, which was based on the theory of Tsallis statistical mechanics, we develop a new evolving stochastic learning algorithm for neural networks.
Anastasiadis, A.D., Magoulas, George D.
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BACKPROPAGATION TRAINING ALGORITHM WITH ADAPTIVE PARAMETERS TO SOLVE DIGITAL PROBLEMS [PDF]
ICTACT Journal on Soft Computing, 2011 An efficient technique namely Backpropagation training with adaptive parameters using Lyapunov Stability Theory for training single hidden layer feed forward network is proposed.
R. Saraswathi
doaj
Hopf Bifurcation and Chaos in Tabu Learning Neuron Models [PDF]
, 2004 In this paper, we consider the nonlinear dynamical behaviors of some tabu leaning neuron models. We first consider a tabu learning single neuron model. By choosing the memory decay rate as a bifurcation parameter, we prove that Hopf bifurcation occurs in
CHUNGUANG LI +6 more
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Dynamic ILC for Linear Repetitive Processes Based on Different Relative Degrees
Mathematics, 2022 The current research on iterative learning control focuses on the condition where the system relative degree is equal to 1, while the condition where the system relative degree is equal to 0 or greater than 1 is not considered.
Lei Wang +3 more
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