In order to get the best possible outset for predicting the implementation effort, it is of vital importance to obtain some data of the developers' experiences, and also how they performed in the past.
Energy forecasting is an important task of immense value for predicting the future development and implementation of energy technologies.
The present paper describes the implementation of two state-of-the art non-linear finite element models for predicting the pull-out strength.
Our implementation of the Eifel algorithm, to the best of our knowledge, is faithful to the algorithm described in [4] for predicting the RTT and setting the RTO.
The autoregressive with exogenous inputs (ARX), as a parametric model and the adaptive-network-based fuzzy inference system (ANFIS), as an intelligent modeling approach that has the advantages of both neural networks and fuzzy logic, have been investigated and compared for simple and fast implementation to predict the response of human operators.
For implementation: predict the glycemic outcome of a clinical intervention during a relevant 1- to 4-hour timeframe typical of TGC intervention frequencies [24, 38, 46, 47, 49, 50, 71, 72].
The robotic arm shows a successful implementation of the learning algorithm in predicting the behavior for arm exoskeleton.
Finally, numerical simulations for cantilever beam test and composite contact test are carried out to validate the finite element implementation and predict the impact behavior of composite structures.
The regression coefficients (derived from the logistic regressions for the period before implementation) were used to predict the probability of mortality for every patient (in periods before and after implementation).
Appropriate implementation of this method leads to save time by predicting the number of interruptions.
This fact proves the urgent needs for a robust design tool that is capable of predicting the desalination system performance at a certain location before implementation decision.
