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Hierarchical optimisation model for waste management forecasting in EU
The level of waste management varies significantly from one EU state to another and therefore they have different starting position regarding...
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A dynamic programming based method for optimal control of a cascaded heat pump system with thermal energy storage
The residential heating and cooling sector has been increasingly electrifying, predominantly using electrically driven heat pumps (HP) in combination...
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A Sojourn-Based Approach to Semi-Markov Reinforcement Learning
In this paper we introduce a new approach to discrete-time semi-Markov decision processes based on the sojourn time process. Different...
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Influence maximization in social media networks concerning dynamic user behaviors via reinforcement learning
This study examines the influence maximization (IM) problem via information cascades within random graphs, the topology of which dynamically changes...
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Comparing Short-Term Univariate and Multivariate Time-Series Forecasting Models in Infectious Disease Outbreak
Predicting infectious disease outbreak impacts on population, healthcare resources and economics and has received a special academic focus during...
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Multistage Models
In the previous chapters we focus on two-stage models, for many good reasons. First, the size of the problem is exponential in the number of stages....
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Exploring Hierarchical Forecasting of Data Popularity in High-Energy Physics Experiments
AbstractIn high-energy physics, the current large-scale distributed computing environments are responsible for processing and analyzing vast amounts...
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Peri-Net-Pro: the neural processes with quantified uncertainty for crack patterns
This paper develops a deep learning tool based on neural processes (NPs) called the Peri-Net-Pro, to predict the crack patterns in a moving disk and...
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Model Predictive Control
In this lecture we present an algorithmic approximation of the optimal feedback control called model predictive control (MPC). In the last several...
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Association of a fractional order controller with an optimal model-based approach for a robust, safe and high performing control of nonlinear systems
In order to extend the efficiency of linear fractional order controllers for nonlinear and preview systems, this paper shows how to design an...
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SPADE4: Sparsity and Delay Embedding Based Forecasting of Epidemics
Predicting the evolution of diseases is challenging, especially when the data availability is scarce and incomplete. The most popular tools for...
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An ensemble of artificial neural network models to forecast hourly energy demand
We propose an ensemble artificial neural network (EANN) methodology for predicting the day ahead energy demand of a district heating operator (DHO)....
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Trajectory optimization of unmanned aerial vehicles in the electromagnetic environment
We consider a type of routing problems common in defence and security, in which we control a fleet of unmanned aerial vehicles (UAVs) that have to...
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Using General Least Deviations Method for Forecasting of Crops Yields
Nowadays much attention has been paid to the development of the software, which makes it possible to process and visualize images, and in particular,...
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Dynamically integrated regression model for online auction data
We propose a dynamically integrated regression model to predict the price of online auctions, including the final price. Different from existing...
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Explicit MPC Solution Using Hasse Diagrams: Construction, Storage and Retrieval
This chapter provides new methods for the construction, storage and retrieval of the explicit MPC solution in the case with quadratic cost and linear...
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High-frequency trading with fractional Brownian motion
In the high-frequency limit, conditionally expected increments of fractional Brownian motion converge to a white noise, shedding their dependence on...
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Introduction to Chaotic Dynamics’ Forecasting
Chaotic dynamics are the paradigm of complex and unpredictable evolution due to their built-in feature of amplifying arbitrarily small perturbations....