Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

International issues in education

Anxiety, negative attitudes, and attrition are all issues presented in the teaching of statistics to undergraduates in research-based degrees regardless of location. Previous works have looked at these obstacles, but none have consolidated a multilingual, multinational effort using a consistent method. Over 400 Spanish-, English-, and German-speaking undergraduate students enrolled in introductory psychology statistics courses were given the Composite Survey of Statistics Anxiety and Attitudes to determine the precursors and effects of existing problems. Results indicated that student background was heavily linked to attitudes and anxieties. The measure was supported as a viable method for more than one class or university in addressing issues in statistics education, developing interventions to improve students’ experiences, and then determining the success of those changes.

The Newsletter of the International Federation for Research in Women's History: Presentation to Its Conference in Sofia, 8 August 2007

Presented as part of the twentieth anniversary celebrations of the International Federation for Research in Women's History in Sofia in August 2007, this paper examines the association's newsletter to explore what it reveals about the expansion of the academic infrastructure for women's history from 1987 to 2007. It looks at the rapid advancement of the subject in the 1980s and early 1990s and its slower growth in the following decade. It also explores briefly the problems that the establishment of Women's Studies Centres presented for women's history.