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In this paper we consider the output consensus problem of networked Hammerstein and Wiener systems in a noisy environment. The Hammerstein and Wiener systems are assumed to be open-loop stable, and its static nonlinearity is allowed to grow up no faster than a polynomial. A control algorithm based on the distributed stochastic approximation algorithm with expanding truncations is designed and it is shown that under the designed control the output consensus is achieved. The numerical simulation given in the paper justifies the theoretical assertions. [ABSTRACT FROM AUTHOR]

In this paper we consider linear fractional systems of commensurate orders and with commensurate delays, whose characteristic equation is a polynomial in the two variables sα (0 < α < 1) and e-ST (T > 0). These systems may have single or multiple chains of poles asymptotic to the imaginary axis. Location of poles of large modulus belonging to these chains are determined by approximation and simple necessary and suffcient H∞-stability conditions are derived. [ABSTRACT FROM AUTHOR]

We present a methodology for the global sampled-data stabilization of systems with a compact absorbing set and input/measurement delays. The methodology is based on the inter-sample-predictor, observer, predictor, delay-free controller (ISP-O-P-DFC) scheme, and the stabilization is robust to perturbations of the sampling schedule. The obtained results are novel even for the delay-free case. [ABSTRACT FROM AUTHOR]

Almost sure convergence rates for linear algorithms hk+1 = hk + 1/kχ (bk - Akhk) are studied, where χ ∊ (0, 1), {Ak}∞ k=1 are symmetric, positive semidefinite random matrices and {bk}∞ k=1 are random vectors. It is shown that nγ hn - A-1b → 0 a.s. for the γ ∊ [0, χ), positive definite A and vector b such that 1/nχ-γ ∑n k=1(Ak -A) → 0 and 1/nχ-γ ∑n k=1(bk -b) → 0 a.s. When χ - γ ∊ (1/2 , 1), these assumptions are implied by the Marcinkiewicz strong law of large numbers, which allows the {Ak} and {bk} to have heavy tails, long-range dependence, or both. Finally, corroborating experimental outcomes and decreasing-gain design considerations are provided. [ABSTRACT FROM AUTHOR]

In this paper, we extend the notion of person-by-person (pbp) optimization to binary decision spaces. The novelty of our approach is the adaptation to a dynamic team context of notions borrowed from the pseudo-boolean optimization field as completely local-global or unimodal functions and submodularity. We also generalize the concept of pbp optimization to the case where groups of m decisions makers make joint decisions sequentially, which we refer to as mbm optimization. The main contribution is a description of sufficient conditions, verifiable in polynomial time, under which a pbp or an mbm optimization algorithm converges to the team-optimum. As a second contribution, we present a local and greedy algorithm characterized by approximate decision strategies (i.e., strategies based on a local state vector) that return the same decisions as in the complete information framework (where strategies are based on full state vector). As a last contribution, we also show that there exists a subclass of submodular team problems, recognizable in polynomial time, for which the pbp optimization converges for at least an opportune initialization of the algorithm. [ABSTRACT FROM AUTHOR]

We present a data structure for answering approximate shortest path queries in a planar subdivision from a fixed source. Let ρ ⩾ 1 be a real number. Distances in each face of this subdivision are measured by a possibly asymmetric convex distance function whose unit disk is contained in a concentric unit Euclidean disk and contains a concentric Euclidean disk with radius 1/ρ. Different convex distance functions may be used for different faces, and obstacles are allowed. Let e be any number strictly between 0 and 1. Our data structure returns a (1+ϵ) approximation of the shortest path cost from the fixed source to a query destination in O(logρn/ϵ) time. Afterwards, a (1+ϵ)-approximate shortest path can be reported in O(log n) time plus the complexity of the path. The data structure uses O( ρ²n³/ϵ² log ρn/ϵ) space and can be built in O( ρ²n³/ϵ² log ρn/ϵ)²) time. Our time and space bounds do not depend on any other parameter; in particular, they do not depend on any geometric parameter of the subdivision such as the minimum angle. [ABSTRACT FROM AUTHOR]

We present a data structure for answering approximate shortest path queries in a planar subdivision from a fixed source. Let ρ ⩾ 1 be a real number. Distances in each face of this subdivision are measured by a possibly asymmetric convex distance function whose unit disk is contained in a concentric unit Euclidean disk and contains a concentric Euclidean disk with radius 1/ρ. Different convex distance functions may be used for different faces, and obstacles are allowed. Let ϵ be any number strictly between 0 and 1. Our data structure returns a (1+ϵ) approximation of the shortest path cost from the fixed source to a query destination in O(log ρn/ϵ ) time. Afterwards, a (1 +ϵ)-approximate shortest path can be reported in O(log n) time plus the complexity of the path. The data structure uses O( ρ²n³/ ϵ² log ρn/ϵ ) space and can be built in O( ρ²n³/ϵ² (log ρn/ϵ)²) time. Our time and space bounds do not depend on any other parameter; in particular, they do not depend on any geometric parameter of the subdivision such as the minimum angle. [ABSTRACT FROM AUTHOR]

This paper proposes a compositional approach to verifying whether a large discrete event system is nonblocking. The new approach avoids computing the synchronous product of a large set of finite-state machines. Instead, the synchronous product is computed gradually, and intermediate results are simplified using conflict-preserving abstractions based on process-algebraic results about fair testing. Heuristics are used to choose between different possible abstractions. By translating the problem representation, the same method can also be applied to verify safety properties, in particular, controllability. Experimental results show that the method is applicable to finite-state machine models of industrial scale and brings considerable improvements in performance over other methods for nonblocking verification. [ABSTRACT FROM AUTHOR]

In this paper we study a discrete version of the classical team decision problem. It has been shown previously that the general discrete team decision problem is NP-hard. Here we present an efficient approximation algorithm for this problem. For the maximization version of this problem with nonnegative rewards, this algorithm computes decision rules which are guaranteed to be within a fixed bound of optimal. [ABSTRACT FROM AUTHOR]