For linear time-invariant dynamic systems with exactly known coefficients of their system matrices for which measurements bounded errors are available at discrete time instants, an optimal polygonal state estimation scheme was recently published. This allows tightly enclosing all possible trajectories in presence uncertain, but bounded, inputs may be varying arbitrarily within bounds. Moreover,...

AlWlmet--ln the context of bounded-error estimation, one is interested in characterizing the set of all the values of the parameters to be estimated that are consistent with the data in the sense that the errors between the data and model outputs fall within prior bounds. While the problem can be considered as solved when the model output is linear in the parameters, the situation is far less a...

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The use of a constructive algorithm for pattern classi cation is examined. The algorithm, a `Perceptron Cascade', has been shown to converge to zero errors whilst learning any consistent classi cation of real-valued pattern vectors (Burgess, 1992). Limiting network size and producing bounded decision regions are noted to be important for the generalization ability of a network. A scheme is sugg...

In this paper we consider a probabilistic approach to the problem of localization in wireless sensor networks and propose a distributed algorithm that helps unknown nodes to determine confident position estimates. The proposed algorithm is RF based, robust to range measurement inaccuracies and can be tailored to varying environmental conditions. The proposed position estimation algorithm consid...

We consider a generalized version of the correlation clustering problem, defined as follows. Given a complete graph G whose edges are labeled with + or −, we wish to partition the graph into clusters while trying to avoid errors: + edges between clusters or − edges within clusters. Classically, one seeks to minimize the total number of such errors. We introduce a new framework that allows the o...

Given a set of species and their similarity data, an important problem in evolutionary biology is how to reconstruct a phylogeny (also called evolutionary tree) so that species are close in the phylogeny if and only if they have high similarity. Assume that the similarity data are represented as a graph G = (V,E) where each vertex represents a species and two vertices are adjacent if they repre...

We introduce a new dimension to the widely studied on-line approximate string matching problem, by introducing an error threshold parameter so that the algorithm is allowed to miss occurrences with probability . This is particularly appropriate for this problem, as approximate searching is used to model many cases where exact answers are not mandatory. We show that the relaxed version of the pr...

This paper analyzes two classes of consensus algorithms in presence of bounded measurement errors. The considered protocols adopt an updating rule based either on constant or vanishing weights. Under the assumption of bounded error, the consensus problem is cast in a set-membership framework, and the agreement of the team is studied by analyzing the evolution of the feasible state set. Bounds o...

in previous papers authors have considered agents as inertia-less self driven particles and designed a flocking algorithm. application of this algorithm to agents with considerable inertial characteristics needs a behavioural controller. the controller uses the local information and helps every agent to imitate the desired behaviour as a member of the flocking frame which covers the main issue ...