AIMC Topic: Time Factors

Clear Filters Showing 1631 to 1640 of 2001 articles

H∞ State Estimation for Discrete-Time Delayed Systems of the Neural Network Type With Multiple Missing Measurements.

IEEE transactions on neural networks and learning systems
This paper investigates the H∞ state estimation problem for a class of discrete-time nonlinear systems of the neural network type with random time-varying delays and multiple missing measurements. These nonlinear systems include recurrent neural netw...

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...

Multiple actor-critic structures for continuous-time optimal control using input-output data.

IEEE transactions on neural networks and learning systems
In industrial process control, there may be multiple performance objectives, depending on salient features of the input-output data. Aiming at this situation, this paper proposes multiple actor-critic structures to obtain the optimal control via inpu...

Robot-assisted surgery for gastric carcinoma: Five years follow-up and beyond: A single western center experience and long-term oncological outcomes.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
INTRODUCTION: Robot-assisted surgery for the treatment of gastric cancer is considered to be safe and feasible with early post-operative outcomes comparable to open and laparoscopic series. However, data regarding long-term oncological outcomes are l...

A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis.

IEEE transactions on neural networks and learning systems
This paper proposes a probabilistic neural network (NN) developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimension...

On Extended Dissipativity of Discrete-Time Neural Networks With Time Delay.

IEEE transactions on neural networks and learning systems
In this brief, the problem of extended dissipativity analysis for discrete-time neural networks with time-varying delay is investigated. The definition of extended dissipativity of discrete-time neural networks is proposed, which unifies several perf...

Does surgeon subjective nerve sparing score predict recovery time of erectile function following robot-assisted radical prostatectomy?

The journal of sexual medicine
INTRODUCTION: During robot-assisted radical prostatectomy (RARP), the quality of nerve sparing (NS) was usually classified by laterality of NS (none, unilateral, and bilateral) or degree of NS (none, partial, and full). Recently, side-specific NS hav...

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 ...

Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition.

Environmental research
Hydrological time series forecasting is one of the most important applications in modern hydrology, especially for the effective reservoir management. In this research, an artificial neural network (ANN) model coupled with the ensemble empirical mode...

New exponential synchronization criteria for time-varying delayed neural networks with discontinuous activations.

Neural networks : the official journal of the International Neural Network Society
This paper investigates the problem of exponential synchronization of time-varying delayed neural networks with discontinuous neuron activations. Under the extended Filippov differential inclusion framework, by designing discontinuous state-feedback ...