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Theses

machine learning for modeling dynamic stochastic systems : application to adaptive control on deep-brain stimulation

Abstract : The past recent years have been marked by the emergence of a large amount of database in many fields like health. The creation of many databases paves the way to new applications. Properties of data are sometimes complex (non linearity, dynamic, high dimensions) and require to perform machine learning models. Belong existing machine learning models, artificial neural network got a large success since the last decades. The success of these models lies on the non linearity behavior of neurons, the use of latent units and the flexibility of these models to adapt to many different problems. Boltzmann machines presented in this thesis are a family of generative neural networks. Introduced by Hinton in the 80's, this family have got a large interest at the beginning of the 21st century and new extensions are regularly proposed.This thesis is divided into two parts. A first part exploring Boltzmann machines and their applications. In this thesis the unsupervised learning of intracranial electroencephalogram signals on rats with Parkinson's disease for the control of the symptoms is studied.Boltzmann machines gave birth to Diffusion networks which are also generative model based on the learning of a stochastic differential equation for dynamic and stochastic data. This model is studied again in this thesis and a new training algorithm is proposed. Its use is tested on toy data as well as on real database.
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Submitted on : Monday, April 19, 2021 - 3:58:19 PM
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  • HAL Id : tel-03202196, version 1

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Rémi Souriau. machine learning for modeling dynamic stochastic systems : application to adaptive control on deep-brain stimulation. Signal and Image Processing. Université Paris-Saclay, 2021. English. ⟨NNT : 2021UPASG004⟩. ⟨tel-03202196⟩

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