38 resultados para polynomial identities

em CentAUR: Central Archive University of Reading - UK


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Identity issues are under-explored in construction management. We provide a brief introduction to the organization studies literature on subjectively construed identities, focusing on discourse, agency, relations of power and identity work. The construction management literature is investigated in order to examine identity concerns as they relate to construction managers centred on (1) professionalism; (2) ethics; (3) relational aspects of self-identity; (4) competence, knowledge and tools; and (5) national culture. Identity, we argue, is a key performance issue, and needs to be accounted for in explanations of the success and failure of projects. Our overriding concern is to raise identity issues in order to demonstrate their importance to researchers in construction management and to spark debate. The purpose of this work is not to provide answers or to propose prescriptive models, but to explore ideas, raise awareness and to generate questions for further programmatic research. To this end, we promote empirical work and theorizing by outlining elements of a research agenda which argues that 'identity' is a potentially generative theme for scholars in construction management.

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The problem of identification of a nonlinear dynamic system is considered. A two-layer neural network is used for the solution of the problem. Systems disturbed with unmeasurable noise are considered, although it is known that the disturbance is a random piecewise polynomial process. Absorption polynomials and nonquadratic loss functions are used to reduce the effect of this disturbance on the estimates of the optimal memory of the neural-network model.

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The paper proposes a method of performing system identification of a linear system in the presence of bounded disturbances. The disturbances may be piecewise parabolic or periodic functions. The method is demonstrated effectively on two example systems with a range of disturbances.

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Neurofuzzy modelling systems combine fuzzy logic with quantitative artificial neural networks via a concept of fuzzification by using a fuzzy membership function usually based on B-splines and algebraic operators for inference, etc. The paper introduces a neurofuzzy model construction algorithm using Bezier-Bernstein polynomial functions as basis functions. The new network maintains most of the properties of the B-spline expansion based neurofuzzy system, such as the non-negativity of the basis functions, and unity of support but with the additional advantages of structural parsimony and Delaunay input space partitioning, avoiding the inherent computational problems of lattice networks. This new modelling network is based on the idea that an input vector can be mapped into barycentric co-ordinates with respect to a set of predetermined knots as vertices of a polygon (a set of tiled Delaunay triangles) over the input space. The network is expressed as the Bezier-Bernstein polynomial function of barycentric co-ordinates of the input vector. An inverse de Casteljau procedure using backpropagation is developed to obtain the input vector's barycentric co-ordinates that form the basis functions. Extension of the Bezier-Bernstein neurofuzzy algorithm to n-dimensional inputs is discussed followed by numerical examples to demonstrate the effectiveness of this new data based modelling approach.

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This paper introduces a new neurofuzzy model construction algorithm for nonlinear dynamic systems based upon basis functions that are Bezier-Bernstein polynomial functions. This paper is generalized in that it copes with n-dimensional inputs by utilising an additive decomposition construction to overcome the curse of dimensionality associated with high n. This new construction algorithm also introduces univariate Bezier-Bernstein polynomial functions for the completeness of the generalized procedure. Like the B-spline expansion based neurofuzzy systems, Bezier-Bernstein polynomial function based neurofuzzy networks hold desirable properties such as nonnegativity of the basis functions, unity of support, and interpretability of basis function as fuzzy membership functions, moreover with the additional advantages of structural parsimony and Delaunay input space partition, essentially overcoming the curse of dimensionality associated with conventional fuzzy and RBF networks. This new modeling network is based on additive decomposition approach together with two separate basis function formation approaches for both univariate and bivariate Bezier-Bernstein polynomial functions used in model construction. The overall network weights are then learnt using conventional least squares methods. Numerical examples are included to demonstrate the effectiveness of this new data based modeling approach.

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This article explores the impact of wider social contact on the experience of Chinese postgraduate students of adaptation to life in the UK. Focus group and individual interviews were conducted with a group of 11 Chinese students on an MA programme at a university in southern England and individual interviews with three representatives of a local volunteer group (LVG) offering support to the Chinese students. Although it was perceived that the students’ support needs were not adequately met by the University, the additional support offered outside the University was unanimously valued and considered as enriching their cultural and linguistic experiences and meeting their expectations. However, frequent social contact with the LVG, whose members were mostly Christians, also had an impact on their values, religious beliefs and identities. In a discussion framed within the sociological perspective of proselytization or religious conversion and the broad framework of international education and globalization, the different responses to this contact are described in terms of believers, doubters, empathisers and commentators. Implications are considered for universities, people involved in providing social support for international students, and sponsors of international students.