An improved incremental normalized least mean square (INLMS) algorithm is developed by minimizing the Huber cost function, which is robust against impulsive noises, over distributed networks.
In the first one the robust waveforms are computed by minimizing the estimation error of the worst-case target realization and in the second, target estimation error of the scaled least square (SLS) estimator is minimized.
Moreover, the variation in protein levels is optimized for a more robust complex production by minimizing the stochastic fluctuations in the concentration of large complexes and in the concentration of the least abundant protein in a complex.
The robust ILC input is computed by minimizing the worst-case value of a performance index under model uncertainty, yielding a convex optimization problem.
Based on affine quadratic stability and multi-convexity concept, the robust optimal bang bang controller is newly derived by minimizing the time derivative of affine Lyapunov function subjected to the limit of control force.
In order to approach the robust design, the tolerance design is achieved by minimizing the total expense, which is the sum of the manufacture cost and the quality loss.
We used a Gram matrix approach for robust determination of sensor locations by minimizing the interferences (maximizing the signal strength) among sensors for engine health monitoring systems.
To design robust fault detection filters, two fault detection design problems are formulated as multiple objective optimization problems by minimizing the effects of disturbances and maximizing the fault sensitivity involving an LTI system with disturbance and fault signals.
The identification is realized through a robust multi-stage optimization scheme based on genetic algorithms, which searches for the best estimates of parameters by minimizing the errors between the measured accelerations and the reconstructed accelerations from the identified parameters.
The identification is carried out through a robust multi-stage optimization scheme based on genetic algorithms, which searches for the best estimates of parameters by minimizing the errors between the measured accelerations and the reconstructed accelerations from the moving vehicles.
