Modified radial basis function neural network using output transformation

A modified radial basis function (RBF) neural network and its identification algorithm based on observational data with heterogeneous noise are introduced. The transformed system output of Box-Cox is represented by the RBF neural network. To identify the model from observational data, the singular value decomposition of the full regression matrix consisting of basis functions formed by system input data is initially carried out and a new fast identification method is then developed using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator (MLE) for a model base spanned by the largest eigenvectors. Finally, the Box-Cox transformation-based RBF neural network, with good generalisation and sparsity, is identified based on the derived optimal Box-Cox transformation and an orthogonal forward regression algorithm using a pseudo-PRESS statistic to select a sparse RBF model with good generalisation. The proposed algorithm and its efficacy are demonstrated with numerical examples.

A study of factors contributing to late submission of course work

This case study uses log-linear modelling to investigate the interrelationships between factors that may contribute to the late submission of coursework by undergraduate students. A class of 86 computing students are considered. These students were exposed to traditional teaching methods supported by e-learning via a Managed Learning Environment (MLE). The MLE warehouses detailed data about student usage of the various areas of the environment, which can be used to interpret the approach taken to learning. The study investigates the interrelationship between these factors with the information as to whether the student handed in their course work on time or whether they were late. The results from the log-linear modelling technique show that there is an interaction between participating in Discussions within the MLE and the timely submission of course work, indicating that participants are more likely to hand in on time, than those students who do not participate.