Sentence examples for a set of uncertain from inspiring English sources
Then, the uncertain TS fuzzy model is represented as a set of uncertain linear systems and a controller design algorithm is proposed.
One of the major challenges in multi-target tracking is the estimation of the number of targets and their positions in the scene, based on a set of uncertain observations.
This paper proposes a receding horizon control scheme for a set of uncertain discrete-time linear systems with randomly jumping parameters described by a finite-state Markov process whose jumping transition probabilities are assumed to belong to some convex sets.
Before implementing the design, a set of uncertain constants is defined as begin{aligned}& W_{i}=M_{i}Vert theta_{i} Vert _{2}^{2}=M_{i}theta_{i}^{T} theta_{i}, quad i=1, ldots,n, end{aligned} (6) where (theta_{i}=[theta_{i,1},ldots,theta_{i,N_{i}}]) and (N_{i}) are the weight vector and the number of neurons in ith hidden layer, respectively.
By using L-K functional and novel inequalities to estimate integral terms in L-K functional, some novel sufficient conditions for a bounded reachable set of uncertain systems are obtained.
The computation of enclosures for the reachable set of uncertain dynamic systems is a crucial component in a wide variety of applications, from global and robust dynamic optimization to safety verification and fault detection.
In order to reduce the controller gain and improve performance we propose a new approach based on partitioning the set of uncertain parameters into smaller subsets.
Then we begin the Section "Financial market model and good-deal constraints" by a description of the financial market and the (non-dominated) confidence set of (uncertain) priors that captures the combined ambiguity about drifts and volatilities.
Next a robust model matching problem is solved to design a post-filter that augments the performance of the geometric filter over the set of uncertain plants.
Next a robust model matching problem is solved to design a filter that robustly matches the performance of the geometric filter over the set of uncertain plants.
The ultimate goal of this work is to significantly reduce the set of uncertain parameter points used in these problems.
