Several prediction techniques belonging from both computational intelligence and statistical fields have been challenged in this task.
There is an abundance of literature on process fault diagnosis ranging from analytical methods to artificial intelligence and statistical approaches.
The literature on process fault diagnosis, ranging from analytical methods to artificial intelligence and statistical approaches, is largely widespread.
This work presents advances in the design of a hybrid methodology that combines artificial intelligence and statistical tools to induce a model of explicit knowledge in relation to the dynamics of a wastewater treatment plant.
Cognitive data-driven analytics, including artificial and computational intelligence techniques, statistical analyses, and data-mining practices, offers an attractive alternate especially in presence of high-dimensional data space and predictive modeling of an extremely nonlinear system.
Probabilistic inference vastly influenced artificial intelligence and statistical learning and the inverse probability allows to infer unknowns, learn from data and make predictions [6, 7].
These methods are applied to nonlinear reliability prediction when compared with physical model methods, which mainly include computational intelligence methods, statistical methods, and state space methods.
Data were double-entered and verified in Dbase IV (dataBased Intelligence) and statistical analyses were carried out using Stata 10.0 (Stata Corporation, College Station, Texas, USA).
Such advances include the advent of artificial intelligence research in statistical model development.
Turhan et al. (2007) presented experimental, computational intelligence based, and statistical investigations of warp tensions in different back-rest oscillations.
