AIMC Journal:
IEEE transactions on neural networks and learning systems

Showing 721 to 730 of 817 articles

Adaptive control of uncertain nonaffine nonlinear systems with input saturation using neural networks.

IEEE transactions on neural networks and learning systems Dec 19, 2014
This paper presents a tracking control methodology for a class of uncertain nonlinear systems subject to input saturation constraint and external disturbances. Unlike most previous approaches on saturated systems, which assumed affine nonlinear syste...
Neural Networks, Computer Algorithms Computer Simulation Humans Nonlinear Dynamics Adaptation, Physiological Uncertainty Learning Feedback
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Application of Reinforcement Learning Algorithms for the Adaptive Computation of the Smoothing Parameter for Probabilistic Neural Network.

IEEE transactions on neural networks and learning systems Dec 19, 2014
In this paper, we propose new methods for the choice and adaptation of the smoothing parameter of the probabilistic neural network (PNN). These methods are based on three reinforcement learning algorithms: Q(0)-learning, Q(λ)-learning, and stateless ...
Probability Reinforcement, Psychology Humans Neural Networks, Computer Algorithms Models, Theoretical
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Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals.

IEEE transactions on neural networks and learning systems Dec 18, 2014
Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-base...
Energy Transfer Humans Stochastic Processes Neural Networks, Computer Time Factors Uncertainty Computer Simulation Decision Making Statistics, Nonparametric Wind
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Comparison of l₁-Norm SVR and Sparse Coding Algorithms for Linear Regression.

IEEE transactions on neural networks and learning systems Dec 18, 2014
Support vector regression (SVR) is a popular function estimation technique based on Vapnik's concept of support vector machine. Among many variants, the l1-norm SVR is known to be good at selecting useful features when the features are redundant. Spa...
Linear Models Time Factors Neural Networks, Computer Support Vector Machine Algorithms
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Dynamic Surface Control Using Neural Networks for a Class of Uncertain Nonlinear Systems With Input Saturation.

IEEE transactions on neural networks and learning systems Dec 4, 2014
In this paper, a dynamic surface control (DSC) scheme is proposed for a class of uncertain strict-feedback nonlinear systems in the presence of input saturation and unknown external disturbance. The radial basis function neural network (RBFNN) is emp...
Neural Networks, Computer Computer Simulation Nonlinear Dynamics Uncertainty Feedback Humans
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Passivity and Passification of Memristor-Based Recurrent Neural Networks With Additive Time-Varying Delays.

IEEE transactions on neural networks and learning systems Nov 13, 2014
This paper presents a new design scheme for the passivity and passification of a class of memristor-based recurrent neural networks (MRNNs) with additive time-varying delays. The predictable assumptions on the boundedness and Lipschitz continuity of ...
Reaction Time Neurons Memory Humans Algorithms Time Factors Neural Networks, Computer Models, Neurological
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Adaptive Synchronization of Memristor-Based Neural Networks with Time-Varying Delays.

IEEE transactions on neural networks and learning systems Nov 7, 2014
In this paper, adaptive synchronization of memristor-based neural networks (MNNs) with time-varying delays is investigated. The dynamical analysis here employs results from the theory of differential equations with discontinuous right-hand sides as i...
Nonlinear Dynamics Memory Time Factors Neurons Algorithms Humans Computer Simulation Neural Networks, Computer
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Properties and Performance of Imperfect Dual Neural Network-Based kWTA Networks.

IEEE transactions on neural networks and learning systems Nov 3, 2014
The dual neural network (DNN)-based k -winner-take-all ( k WTA) model is an effective approach for finding the k largest inputs from n inputs. Its major assumption is that the threshold logic units (TLUs) can be implemented in a perfect way. However,...
Feedback Time Factors Probability Decision Support Techniques Computer Simulation Neural Networks, Computer Algorithms Humans
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Feedforward Categorization on AER Motion Events Using Cortex-Like Features in a Spiking Neural Network.

IEEE transactions on neural networks and learning systems Oct 22, 2014
This paper introduces an event-driven feedforward categorization system, which takes data from a temporal contrast address event representation (AER) sensor. The proposed system extracts bio-inspired cortex-like features and discriminates different p...
Models, Neurological Action Potentials Neurons Humans Neural Networks, Computer Nerve Net Motion Cerebral Cortex
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Learning Stable Multilevel Dictionaries for Sparse Representations.

IEEE transactions on neural networks and learning systems Oct 16, 2014
Sparse representations using learned dictionaries are being increasingly used with success in several data processing and machine learning applications. The increasing need for learning sparse models in large-scale applications motivates the developm...
Pattern Recognition, Automated Neural Networks, Computer Humans Algorithms Computer Simulation Learning Cluster Analysis
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