Titlebook: Mathematical Modeling and Computational Intelligence in Engineering Applications; Ant?nio José da Silva Neto,Orestes Llanes Santiago Book

Creditee

municipality

權宜之計

accomplishment

intertwine

gence.Offers a broad coverage of applications in automation,.This book brings together a rich selection of studies in mathematical modeling and computational intelligence, with?application in several fields of engineering, like automation, biomedical, chemical, civil, electrical, electronic, geophys

真繁榮

Book 2016engineering, like automation, biomedical, chemical, civil, electrical, electronic, geophysical and mechanical engineering, on a multidisciplinary approach.?Authors from five countries and 16 different research centers contribute with their expertise in both the fundamentals and real problems applica

愛管閑事

Fault Diagnosis with Missing Data Based on Hopfield Neural Networks,rcome this inconvenience. The proposal is tested using the development and application of methods for the actuator diagnostic in industrial control systems (DAMADICS) benchmark, with successful performance.

epinephrine

Diagnosing Time-Dependent Incipient Faults,euristics: Differential Evolution and its variation Differential Evolution with Particle Collision. The proposed methodology is tested using simulated data from the Two Tanks system, which is recognized as benchmark for control and diagnosis. The results indicate that this proposal is suitable for the aforementioned diagnosis.

Detain

An Indirect Kernel Optimization Approach to Fault Detection with KPCA,larm rate and false detection rate indicators that are combined in a single indicator: the area under the ROC curve. This approach was tested on the Tennessee Eastman (TE) process, where a significant decrease in false and missing alarms was observed.

Spinous-Process

Uncertainty Quantification in Chromatography Process Identification Based on Markov Chain Monte Carocity chromatography model is quantified by means of a Bayesian method, the delayed rejection adaptive metropolis algorithm, which is a variant of the Markov Chain Monte Carlo (MCMC) method. The model is also evaluated for a random sample of parameters, being then determined the uncertainty in the prediction.