Name
Abstract | ||
|---|---|---|
A series of modelling methodologies based on artificial intelligence tools are applied to solve a complex real-world problem. Neural networks and support vector machines are used as models and differential evolution and clonal selection algorithms as optimizers for structural and parametric optimization of the models. The goal is to make a comparative analysis of these methods for the case study of the free radical polymerization of styrene, a complex, difficult to model process, where the monomer conversion and molecular masses are predicted as a function of reaction conditions, i.e. temperature, amount of initiator and time. Four modelling methodologies are developed and evaluated in terms of accuracy. |
Year | Venue | Field |
|---|---|---|
2014 | SIMULTECH | Parametric optimization,Computer science,Support vector machine,Polymerization,Differential evolution,Artificial intelligence,Radical polymerization,Artificial neural network,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
12 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Silvia Curteanu | 1 | 63 | 6.26 |
| Elena-Niculina Dragoi | 2 | 33 | 3.18 |
| Florin Leon | 3 | 71 | 15.03 |
| Cristina Butnariu | 4 | 0 | 0.34 |