From experimental data, models were built up to describe distilled percentage and methyl concentration at the residue stream in function of operating conditions.
By using an FCM-based Adaptive neuro-fuzzy inference system (FCM-ANFIS) and a set of experimental data, models were developed to predict the effective viscosity of nanofluids.
Based on the experimental data, models of thermal conductivity and viscosity are developed in terms of volume concentration and temperature using neural network.
Thesecondquestionis:"Whatcanreturncomputationtoneuroscience(the new neurocybernetics)?" That is to say, how can mathematics, electronics, c- puter science, and arti cial intelligence help the neurobiologists to improve their experimental data modeling and to move a step forward towards the understa- ing of the nervous system?
As a result, the source-to-outcome continuum becomes populated with information that includes experimental data, model-derived data, and connection models.
Due to the high number of kinetic parameters compared to little experimental data, model fitting can yield multiple parameter values that can describe the data equally well.
Given a set of experimental data, models corresponding to competing network connectivies are first constructed, all of which can explain wild type data equally well.
PMM-Lab additionally contains a database that stores all information (experimental data, models, metadata, and workflows) in relational database tables [ 20].
In contrast, model 3 qualitatively captures the increase with increasing input strength, which we also observe in our experimental data (model 3: r(9) = 0.67, P = 0.024; experimental data: r(4) = 0.97, P = 9.4 × 10−4).
In addition, the procedure needs less experimental data in modelling.
Two questions arise when experimental data is modeled: Is the model statistically compliant with the data?
