Related to Undirect: Unshape, Undirected graph


v. t.1.To misdirect; to mislead.
who make false fires to undirect seamen in a tempest.
- Fuller.
Webster's Revised Unabridged Dictionary, published 1913 by G. & C. Merriam Co.
References in periodicals archive ?
Let G = (V, E, A) be a weighted undirected graph of order N which consists of a node set V = {[v.sub.1], [v.sub.2], ..., [v.sub.N]}, an undirect edges set E [subset or equal to] V x V, and a weighted adjacency matrix A = [([a.sub.ij]).sub.NxN] [member of] [R.sup.N x N] with [a.sub.ii] = 0 and nonnegative elements.
The Laplacian matrix L of an undirected network is symmetric and positive semi-definite.
By synthetically using the Lipschitz conditions, the variable structure technique [6], the feedback linearization technique [22], and the Lyapunov theory, all three control algorithms for consensus tracking under the undirect or the tree shaped communication topology are effectively designed.
The undirect graph G with one additional vertex representing a leader is used to model the leader-follower communication topologies in this paper.
In the problem of nonlinear consensus tracking, a kind of communication topology of N follower agents is modeled as an undirected graph G = {V, E, A}, where V = {1, 2, ..., i, ..., N} is a set of N integers, with the number i which means the ith vertex representing the ith agent, and E [subset] V x V is an edge set in which each edge is denoted by a pair of vertices (i, j).
The communication topology of these N followers is modeled as an undirected graph G = {V, E, A}.
The undirected graph G which models the network topology of N followers is connected and atleast one follower is informed about the state of the leader.
In calculations some presumptios and undirect analytical methods were used also.
The interconnection topology of n followers can be conveniently described by a undirected graph G = (V, E, A) of order n, where V = {[v.sub.1], [v.sub.2], ..., [v.sub.n]} is the set of n nodes, E [subset or equal to] V x V is the set of edges, and A = [[a.sub.ij]] is a weighted adjacency matrix.