Searched for: subject:"symbolic%5C+regression"
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document
Märtens, M. (author)
This thesis is a contribution to a deeper understanding of how information propagates and what this process entails. At its very core is the concept of the network: a collection of nodes and links, which describes the structure of the systems under investigation. The network is a mathematical model which allows to focus on a very fundamental...
doctoral thesis 2018
document
Kubalìk, Jiřì (author), Alibekov, Eduard (author), Babuska, R. (author)
Model-based reinforcement learning (RL) algorithms can be used to derive optimal control laws for nonlinear dynamic systems. With continuous-valued state and input variables, RL algorithms have to rely on function approximators to represent the value function and policy mappings. This paper addresses the problem of finding a smooth policy...
conference paper 2017
document
Thorat, R.R. (author)
This thesis was performed in the framework of ErasmusMundus EU-INDIA scholarship programme. The main goal is to elucidate particle enhanced foam flow (surfactant water and nitrogen gas) in porous media near the critical micelle concentration. The thesis is divided in four parts: in the first part the modeling of foam flow is investigated, in the...
doctoral thesis 2016
document
Kubalìk, Jiřì (author), Alibekov, Eduard (author), Žegklitz, Jan (author), Babuska, R. (author)
This paper presents a first step of our research on designing an effective and efficient GP-based method for symbolic regression. First, we propose three extensions of the standard Single Node GP, namely (1) a selection strategy for choosing nodes to be mutated based on depth and performance of the nodes, (2) operators for placing a compact...
conference paper 2016
Searched for: subject:"symbolic%5C+regression"
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