Identification of linear systems in the presence of piecewise polynomial disturbances

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.

Democracy and identity: educating the student voice

Considering the role of student voice in music education in connection with the role of music in identity formation. A report on a small-scale study.

Neurofuzzy network model construction using Bézier-Bernstein polynomial functions

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.

Generalized neurofuzzy network modeling algorithms using Bezier-Bernstein polynomial functions and additive decomposition

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.

The Identity of the St Bees Lady, Cumbria: An Osteobiographical Approach

Using an Osteobiographical approach, this contribution considers the identity of the woman found alongside the St Bees Man, one of the best-preserved archaeological bodies ever discovered. Osteological, isotopic and radiocarbon analyses, combined with the archaeological context of the burial and documented social history, provide the basis for the identification of a late 14th-century heiress whose activities were at the heart of medieval northern English geopolitics.

Autobiographical memory, autonoetic consciousness, and identity in Asperger syndrome

Previous results from research on individuals with Asperger syndrome (AS) suggest a diminished ability for recalling episodic autobiographical memory (AM). The primary aim of this study was to explore autobiographical memory in individuals with Asperger syndrome and specifically to investigate whether memories in those with AS are characterized by fewer episodic 'remembered' events (due to a deficit in autonoetic consciousness). A further aim was to examine whether such changes in AM might also be related to changes in identity, due to the close relationship between memory and the self and to the established differences in self-referential processes in AS. Eleven adults with AS and fifteen matched comparison participants were asked to recall autobiographical memories from three lifetime periods and for each memory to give either a remember response (autonoetic consciousness) or a know response (noetic consciousness). The pattern of results shows that AS participants recalled fewer memories and that these memories were more often rated as known, compared to the comparison group. AS participants also showed differences in reported identity, generating fewer social identity statements and more abstract, trait-linked identities. The data support the view that differences in both memory and reported personal identities in AS are characterized by a lack of specificity.