A robust adaptive fuzzy control design approach is developed for a class of multivariable nonlinear systems with modeling uncertainties and external disturbances. The controller design for the overall systems has been carried out through a number of simpler controller designs for a series of the relevant auxiliary systems based on the backstepping design technique. For each auxiliary system, an...

The least squares linear regression estimator is well-known to be highly sensitive to unusual observations in the data, and as a result many more robust estimators have been proposed as alternatives. One of the earliest proposals was least-sum of absolute deviations (LAD) regression, where the regression coefficients are estimated through minimization of the sum of the absolute values of the re...

Assessing the linear relationship between a set of continuous predictors and a continuous response is a well studied problem in statistics and is applied in many data mining situations. L2 based methods such as ordinary least squares and principal components regression can be used to determine this relationship. However, both of these methods become impaired when multicollinearity is present. T...

In 1993, Yang first extended the classification maximum likelihood (CML) to a so-called fuzzy CML, by combining fuzzy c-partitions with the CML function. Fuzzy c-partitions are generally an extension of hard c-partitions. It was claimed that this was more robust. However, the fuzzy CML still lacks some robustness as a clustering algorithm, such as its inability to detect different volumes of cl...

The problem of designing controls for a linear dynamic system under input disturbance is considered. The input disturbance is bounded but the bound information is either deterministic or fuzzy. The control design is purely deterministic. However, the resulting system performance is interpreted differently, depending on the bound information. It may be deterministic or fuzzy (i.e. with a spectru...

In intuitive design steps, a fuzzy logic-based robust controller is designed to address the first 1990-1992 American Control Conference benchmark problem. Using a conceptual transformation of the original flexible body into a perpetual rigid body mode, a final design which succeeds in stabilizing the system after a unit impulse disturbance is developed. The simulation results are shown to achie...

We present the first efficient and provably consistent estimator for the robust regression problem. The area of robust learning and optimization has generated a significant amount of interest in the learning and statistics communities in recent years owing to its applicability in scenarios with corrupted data, as well as in handling model mis-specifications. In particular, special interest has ...