AIMC Topic: Uncertainty

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A new delay-independent condition for global robust stability of neural networks with time delays.

Neural networks : the official journal of the International Neural Network Society
This paper studies the problem of robust stability of dynamical neural networks with discrete time delays under the assumptions that the network parameters of the neural system are uncertain and norm-bounded, and the activation functions are slope-bo...

An Interval-Valued Neural Network Approach for Uncertainty Quantification in Short-Term Wind Speed Prediction.

IEEE transactions on neural networks and learning systems
We consider the task of performing prediction with neural networks (NNs) on the basis of uncertain input data expressed in the form of intervals. We aim at quantifying the uncertainty in the prediction arising from both the input data and the predict...

Adaptive Neural Network Dynamic Surface Control for a Class of Time-Delay Nonlinear Systems With Hysteresis Inputs and Dynamic Uncertainties.

IEEE transactions on neural networks and learning systems
In this paper, an adaptive neural network (NN) dynamic surface control is proposed for a class of time-delay nonlinear systems with dynamic uncertainties and unknown hysteresis. The main advantages of the developed scheme are: 1) NNs are utilized to ...

pth moment exponential stochastic synchronization of coupled memristor-based neural networks with mixed delays via delayed impulsive control.

Neural networks : the official journal of the International Neural Network Society
This paper concerns the pth moment synchronization in an array of generally coupled memristor-based neural networks with time-varying discrete delays, unbounded distributed delays, as well as stochastic perturbations. Hybrid controllers are designed ...

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

IEEE transactions on neural networks and learning systems
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...

Incorporating Wind Power Forecast Uncertainties Into Stochastic Unit Commitment Using Neural Network-Based Prediction Intervals.

IEEE transactions on neural networks and learning systems
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...

Dynamic Surface Control Using Neural Networks for a Class of Uncertain Nonlinear Systems With Input Saturation.

IEEE transactions on neural networks and learning systems
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...

Exploiting uncertainty measures in compounds activity prediction using support vector machines.

Bioorganic & medicinal chemistry letters
The great majority of molecular modeling tasks require the construction of a model that is then used to evaluate new compounds. Although various types of these models exist, at some stage, they all use knowledge about the activity of a given group of...

Approximate N-Player Nonzero-Sum Game Solution for an Uncertain Continuous Nonlinear System.

IEEE transactions on neural networks and learning systems
An approximate online equilibrium solution is developed for an N -player nonzero-sum game subject to continuous-time nonlinear unknown dynamics and an infinite horizon quadratic cost. A novel actor-critic-identifier structure is used, wherein a robus...

Globally Stable Adaptive Backstepping Neural Network Control for Uncertain Strict-Feedback Systems With Tracking Accuracy Known a Priori.

IEEE transactions on neural networks and learning systems
This paper addresses the problem of globally stable direct adaptive backstepping neural network (NN) tracking control design for a class of uncertain strict-feedback systems under the assumption that the accuracy of the ultimate tracking error is giv...