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ANFIS modelling of PIV tests

Leonardo Clemente

ANFIS modelling of PIV tests.

Rel. Bernardo Ruggeri, Carlos Enrique Gomez Camacho. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Chimica E Dei Processi Sostenibili, 2019

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Abstract:

This thesis work concerns the modelling of experimental data, regarding velocity fields obtained through Particle Image Velocimetry measurement method. Such data sets are correlated through an Adaptive Neuro Fuzzy Inference system, which is a non - deterministic type of model. This is achieved using a Fuzzy inference system structure, specifically a First - order Sugeno type. Modifiable parameters of the presented models are characterised by a hybrid machine Learning method, which iteratively takes a certain amount of data to train the initial raw model. Different types of models are created and analysed in terms of correlation and computational performances. This has the purpose of making considerations about the Neuro Fuzzy system itself. Moreover, another aim consists in trying to extract informations and unrevealed patterns through the obtained models. After many trials, the best model is presented through its characteristics and its pros and cons.

Relators: Bernardo Ruggeri, Carlos Enrique Gomez Camacho
Academic year: 2019/20
Publication type: Electronic
Number of Pages: 90
Subjects:
Corso di laurea: Corso di laurea magistrale in Ingegneria Chimica E Dei Processi Sostenibili
Classe di laurea: New organization > Master science > LM-22 - CHEMICAL ENGINEERING
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/12138
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