AIMC Journal:
IEEE transactions on neural networks and learning systems

Showing 321 to 330 of 780 articles

Spiking Neural Network Regularization With Fixed and Adaptive Drop-Keep Probabilities.

IEEE transactions on neural networks and learning systems Aug 3, 2022
Dropout and DropConnect are two techniques to facilitate the regularization of neural network models, having achieved the state-of-the-art results in several benchmarks. In this paper, to improve the generalization capability of spiking neural networ...
Neural Networks, Computer Algorithms Neurons Probability Generalization, Psychological
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Global Negative Correlation Learning: A Unified Framework for Global Optimization of Ensemble Models.

IEEE transactions on neural networks and learning systems Aug 3, 2022
Ensembles are a widely implemented approach in the machine learning community and their success is traditionally attributed to the diversity within the ensemble. Most of these approaches foster diversity in the ensemble by data sampling or by modifyi...
Humans Neural Networks, Computer Machine Learning Algorithms
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Lifelong Incremental Reinforcement Learning With Online Bayesian Inference.

IEEE transactions on neural networks and learning systems Aug 3, 2022
A central capability of a long-lived reinforcement learning (RL) agent is to incrementally adapt its behavior as its environment changes and to incrementally build upon previous experiences to facilitate future learning in real-world scenarios. In th...
Neural Networks, Computer Algorithms Bayes Theorem Reinforcement, Psychology Education, Continuing
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Toward the Optimal Design and FPGA Implementation of Spiking Neural Networks.

IEEE transactions on neural networks and learning systems Aug 3, 2022
The performance of a biologically plausible spiking neural network (SNN) largely depends on the model parameters and neural dynamics. This article proposes a parameter optimization scheme for improving the performance of a biologically plausible SNN ...
Neural Networks, Computer Computer Simulation Learning Action Potentials Neurons
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Finite-Time Synchronization of Complex-Valued Memristive-Based Neural Networks via Hybrid Control.

IEEE transactions on neural networks and learning systems Aug 3, 2022
The finite-time synchronization problem is investigated for the master-slave complex-valued memristive neural networks in this article. A novel Lyapunov-function based finite-time stability criterion with impulsive effects is proposed and utilized to...
Neural Networks, Computer Time Factors
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Remote State Estimation of Nonlinear Systems Over Fading Channels via Recurrent Neural Networks.

IEEE transactions on neural networks and learning systems Aug 3, 2022
In this article, we consider the remote state estimation for nonlinear dynamic systems with known linear dynamics and unknown nonlinear perturbations. The nonlinear dynamic plant is monitored by multiple distributed sensors over a random access wirel...
Neural Networks, Computer Algorithms Communication Noise Nonlinear Dynamics
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Motion Planning and Adaptive Neural Tracking Control of an Uncertain Two-Link Rigid-Flexible Manipulator With Vibration Amplitude Constraint.

IEEE transactions on neural networks and learning systems Aug 3, 2022
This article deals with an uncertain two-link rigid-flexible manipulator with vibration amplitude constraint, intending to achieve its position control via motion planning and adaptive tracking approach. In motion planning, the motion trajectories fo...
Neural Networks, Computer Computer Simulation Uncertainty Vibration
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A Plug-in Method for Representation Factorization in Connectionist Models.

IEEE transactions on neural networks and learning systems Aug 3, 2022
In this article, we focus on decomposing latent representations in generative adversarial networks or learned feature representations in deep autoencoders into semantically controllable factors in a semisupervised manner, without modifying the origin...
Neural Networks, Computer Machine Learning Learning Semantics
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Optimizing Attention for Sequence Modeling via Reinforcement Learning.

IEEE transactions on neural networks and learning systems Aug 3, 2022
Attention has been shown highly effective for modeling sequences, capturing the more informative parts in learning a deep representation. However, recent studies show that the attention values do not always coincide with intuition in tasks, such as m...
Neural Networks, Computer Machine Learning Learning Reinforcement, Psychology
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Target Tracking Control of a Biomimetic Underwater Vehicle Through Deep Reinforcement Learning.

IEEE transactions on neural networks and learning systems Aug 3, 2022
In this article, the underwater target tracking control problem of a biomimetic underwater vehicle (BUV) is addressed. Since it is difficult to build an effective mathematic model of a BUV due to the uncertainty of hydrodynamics, target tracking cont...
Neural Networks, Computer Algorithms Biomimetics Markov Chains Reinforcement, Psychology
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