Simulation results show that the proposed method in comparison with a genetic algorithm-based approach has better accuracy and higher performance.
For better accuracy, and often better range, use.2g BBs.
Our goal is to better understand the accuracy and performance characteristics of TRRM on massive scale problems, and to provide community software that facilitates further algorithmic development and potentially its application to a broader class of problems.
The simulations show that the cooperative approaches provide better accuracy performance compared with the noncooperative approaches including the linear UWLS and CWLS estimators since more measurements are involved into the model.
In the plot, an ROC curve, which lies to the lower left of another curve, has a better accuracy performance.
The results demonstrate that the SFSL-SANN is faster convergence and time-saving than BP-SANN, and produces better learning accuracy and generalization performance than other approaches.
Simulation results show that the proposed methods have better prediction accuracy and control performance than those achieved by the conventional Hammerstein model and its associated adaptive decentralized PID controller design, respectively.
The results show that, all models give a good prediction of user performance, even if the cognitive models show better accuracy of the user performance.
We show that lazy learning is able to provide better modeling accuracy and higher control performance at the cost of a reduced readability of the resulting approximator.
Cannon says that users are looking for higher levels of accuracy and better performance in suboptimal situations.
