Search
Search Results
-
RETRACTED ARTICLE: A novel dynamic en-route and slot allocation method based on receding horizon control
As an important part in civil air traffic control, en-route management plays a commander role in the whole control process. En-route and slot...
-
Receding-Horizon Dynamic Optimization of Port-City Traffic Interactions Over Shared Urban Infrastructure
We introduce a receding-horizon dynamic optimization approach for the real-time efficient management of conflicting traffic flows from a port...
-
Do we Benefit from the Categorization of the News Flow in the Stock Price Prediction Problem?
AbstractThe power of machine learning is widely leveraged in the task of company stock price prediction. It is essential to incorporate historical...
-
A receding horizon event-driven control strategy for intelligent traffic management
In this paper, the intelligent traffic management within a smart city environment is addressed by develo** an ad-hoc model predictive control...
-
Nonlinear Autoregressive Neural Network and Wavelet Transform for Rainfall Prediction
AbstractRainfall prediction is one of the most important tools for water management, prompting scientists to develop several techniques in recent...
-
Comparison of Joint Modelling and Landmarking Approaches for Dynamic Prediction Using Bootstrap Simulation
Prediction models for clinical outcomes can greatly help clinicians with early diagnosis, cost-effective management and primary prevention of many...
-
Bitcoin daily price prediction through understanding blockchain transaction pattern with machine learning methods
Bitcoin has became one of the most popular investment asset recent years. The volatility of bitcoin price in financial market attracting both...
-
Wind Power Prediction Using Artificial Neural Network Model: A Case Study
Considering the high level of pollution that threatens our earth, energy from the wind represents a major alternative to fossil fuels, thanks to its...
-
Markov risk map**s and risk-sensitive optimal prediction
We formulate a probabilistic Markov property in discrete time under a dynamic risk framework with minimal assumptions. This is useful for recursive...
-
Finite-Data Error Bounds for Koopman-Based Prediction and Control
The Koopman operator has become an essential tool for data-driven approximation of dynamical (control) systems, e.g., via extended dynamic mode...
-
Offshore Wind Energy Prediction Using Machine Learning with Multi-Resolution Inputs
The ever-increasing scale and penetration of offshore wind energy in modern day electricity systems is continually raising the need for wind resource...
-
Limits of epidemic prediction using SIR models
The Susceptible-Infectious-Recovered (SIR) equations and their extensions comprise a commonly utilized set of models for understanding and predicting...
-
Time Adaptivity in Model Predictive Control
The core of the Model Predictive Control (MPC) method in every step of the algorithm consists in solving a time-dependent optimization problem on the...
-
Covariance prediction via convex optimization
We consider the problem of predicting the covariance of a zero mean Gaussian vector, based on another feature vector. We describe a covariance...
-
Adaptive Bet-Hedging Revisited: Considerations of Risk and Time Horizon
Models of adaptive bet-hedging commonly adopt insights from Kelly’s famous work on optimal gambling strategies and the financial value of...
-
Some Problems of Implementing Optimal Control Theory in Automated Control Systems
AbstractThe paper analyses the state of applied optimal control theory in automated control synthesis problems and the issues of its practical...
-
Ordering in Games with Reduced Memory and Planning Horizon of Players
We suggested and investigated a model of generations change for Cournot competition with predictions and memory. Then, we described the general...
-
Deep Learning in Multi-step Prediction of Chaotic Dynamics From Deterministic Models to Real-World Systems
The book represents the first attempt to systematically deal with the use of deep neural networks to forecast chaotic time series. Differently from...
-
Predictive Path Following Control Without Terminal Constraints
We consider model predictive path-following control (MPFC) without stabilizing terminal constraints or costs. We investigate sufficient stability...
-
Optimal Control of Output Variables Within a Given Range Based on a Predictive Model
This paper is devoted to the problem of digital control design to keep the output variables of the controlled process in a given range. Such a...