However, none of these predictors was designed to classify patients based on their likelihood of developing a late recurrent disease, as the vast majority of recurrent cases for the development and testing of these predictors had recurrence within five years after initial treatment.
There are a few explanations why the connection between the predictive genes and relapse associated mechanism is not necessarily immediate: First, the predictors were designed to include relatively small number of genes while the actual mechanisms may include dozens of signaling pathways.
Two control architectures, model predictive control based on ULN model and single neuron PID (SN-PID) controller based on ULN predictor, are designed to control pH neutralization process, respectively.
The cloud auto-scaling autonomic elements (workload patterns and predictor) are designed such that the architecture can be implemented using the strategy design pattern [23] (Fig. 10).
In order to address this issue, a predictor is designed.
To handle the delays and data losses simultaneously, a predictor is designed for each subsystem estimator.
The predictor is designed using Lyapunov-based methods, by numerical solution of a linear matrix inequality.
Then, an adaptive predictor is designed based on the observer and is employed to predict non-measurable states.
With the non-persistent measurement data, a linear predictor is designed to provide the reference signal in the adaptive filter.
Our new method classifies image patches into several classes, for each class, a class-specific predictor is designed.
With the obtained nominal model, uncertainty bound and the robust criterion derived, a robust Smith predictor is designed.
