Search results for "Zed"
showing 10 items of 5581 documents
Distributed Adaptive Consensus Tracking Control of Uncertain High-order Nonlinear Systems under Directed Graph Condition
2018
In this paper, we investigate the output consensus tracking problem for a class of high-order nonlinear systems subjected to unknown parameters and uncertain external disturbances. A novel backstepping based distributed adaptive control scheme is presented under the directed communication status. For the subsystems without direct access to time-varying desired trajectory, local estimators are introduced and the corresponding adaptive laws are designed in a totally distributed fashion. With the presented scheme, the assumption on linearly parameterized reference signal and the information exchange operation of subsystem inputs in the existing results are no longer needed. It is shown that al…
Distributed adaptive leader–follower and leaderless consensus control of a class of strict-feedback nonlinear systems : a unified approach
2020
In this paper, distributed adaptive consensus for a class of strict-feedback nonlinear systems under directed topology condition is investigated. Both leader–follower and leaderless cases are considered in a unified framework. To design distributed controller for each subsystem, a local compensatory variable is generated based on the signals collected from its neighbors. Such a technique enables us to solve the leader–follower consensus and leaderless consensus problems in a unified framework. And it further allows us to treat the leaderless consensus as a special case of the leader–follower consensus. For leader–follower consensus, the assumption that the leader trajectory is linearly para…
Extreme minimal learning machine: Ridge regression with distance-based basis
2019
The extreme learning machine (ELM) and the minimal learning machine (MLM) are nonlinear and scalable machine learning techniques with a randomly generated basis. Both techniques start with a step in which a matrix of weights for the linear combination of the basis is recovered. In the MLM, the feature mapping in this step corresponds to distance calculations between the training data and a set of reference points, whereas in the ELM, a transformation using a radial or sigmoidal activation function is commonly used. Computation of the model output, for prediction or classification purposes, is straightforward with the ELM after the first step. In the original MLM, one needs to solve an addit…
Density Flow in Dynamical Networks via Mean-Field Games
2016
Current distributed routing control algorithms for dynamic networks model networks using the time evolution of density at network edges, while the routing control algorithm ensures edge density to converge to a Wardrop equilibrium, which was characterized by an equal traffic density on all used paths. We rearrange the density model to recast the problem within the framework of mean-field games. In doing that, we illustrate an extended state-space solution approach and we study the stochastic case where the density evolution is driven by a Brownian motion. Further, we investigate the case where the density evolution is perturbed by a bounded adversarial disturbance. For both the stochastic a…
Impact of accounting process characteristics on accounting outsourcing - Comparison of users and non-users of cloud-based accounting information syst…
2019
Article In Press The article of record as published may be found at https://doi.org/10.1016/j.accinf.2019.06.002 Prior literature informs us that a company's decision to outsource a business process depends on process characteristics such as how frequently the process is performed or how specific the assets required by the process are. In this article, we compare the effects of accounting process characteristics on outsourcing decisions across users of traditional and cloud-based accounting information systems (AIS). By focusing on outsourcing of accounting processes among small and medium sized enterprises, we investigate the effect of five business process characteristics (frequency, huma…
K-nearest neighbor driving active contours to delineate biological tumor volumes
2019
Abstract An algorithm for tumor delineation in positron emission tomography (PET) is presented. Segmentation is achieved by a local active contour algorithm, integrated and optimized with the k-nearest neighbor (KNN) classification method, which takes advantage of the stratified k-fold cross-validation strategy. The proposed approach is evaluated considering the delineation of cancers located in different body districts (i.e. brain, head and neck, and lung), and considering different PET radioactive tracers. Data are pre-processed in order to be expressed in terms of standardized uptake value, the most widely used PET quantification index. The algorithm uses an initial, operator selected re…
Game Theoretic Decentralized Feedback Controls in Markov Jump Processes
2017
This paper studies a decentralized routing problem over a network, using the paradigm of mean-field games with large number of players. Building on a state-space extension technique, we turn the problem into an optimal control one for each single player. The main contribution is an explicit expression of the optimal decentralized control which guarantees the convergence both to local and to global equilibrium points. Furthermore, we study the stability of the system also in the presence of a delay which we model using an hysteresis operator. As a result of the hysteresis, we prove existence of multiple equilibrium points and analyze convergence conditions. The stability of the system is ill…
A Hierarchical Learning Scheme for Solving the Stochastic Point Location Problem
2012
Published version of a chapter in the book: Advanced Research in Applied Artificial Intelligence. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-31087-4_78 This paper deals with the Stochastic-Point Location (SPL) problem. It presents a solution which is novel in both philosophy and strategy to all the reported related learning algorithms. The SPL problem concerns the task of a Learning Mechanism attempting to locate a point on a line. The mechanism interacts with a random environment which essentially informs it, possibly erroneously, if the unknown parameter is on the left or the right of a given point which also is the current guess. The first pioneering work […
Do terrorism, organized crime (drug production), and state weakness affect contemporary armed conflicts? An empirical analysis
2015
ABSTRACTIn 2014, the UN Security Council emphasized the dangers of terrorism, criminal activity (especially drug production and trafficking), and state weakness in conflict areas. However, neither policy debates nor scholarly analyses have focussed on the potential impact of these elements on conflict dynamics and characteristics, and the investigated partial relationships have led to inconclusive results. This article explores the presence in armed conflicts of terrorist groups among fighting parties, major drug production (indicating the presence of activities typical of criminal organizations), and state failure in the period 1990–2011. Focussing on intrastate conflicts, this article hig…
Identification of Objects Based on Generalized Amplitude-Phase Images Statistical Models
2017
The article presents the dynamical objects identification technology based on statistical models of amplitude-phase images (APIm) – multidimensional data arrays (semantic models) and statistical correlation analysis methods using the generalized discrete Hilbert transforms (DHT) – 2D Hilbert (Foucault) isotropic (HTI), anisotropic (HTA) and total transforms – AP-analysis (APA) to calculate the APIm. The identified objects are modeled with 3D airplanes templates rotated in space around the center of Cartesian coordinate system. The DHT domain system of coordinates displaying the plane projections (2D flat images) remains to be space-invariant. That causes the anisotropic properties of APIm a…