Neurofuzzy mixture of experts network parallel learning and model construction algorithms

A connection between a fuzzy neural network model with the mixture of experts network (MEN) modelling approach is established. Based on this linkage, two new neuro-fuzzy MEN construction algorithms are proposed to overcome the curse of dimensionality that is inherent in the majority of associative memory networks and/or other rule based systems. The first construction algorithm employs a function selection manager module in an MEN system. The second construction algorithm is based on a new parallel learning algorithm in which each model rule is trained independently, for which the parameter convergence property of the new learning method is established. As with the first approach, an expert selection criterion is utilised in this algorithm. These two construction methods are equivalent in their effectiveness in overcoming the curse of dimensionality by reducing the dimensionality of the regression vector, but the latter has the additional computational advantage of parallel processing. The proposed algorithms are analysed for effectiveness followed by numerical examples to illustrate their efficacy for some difficult data based modelling problems.

Problem-based learning approaches in meteorology

Problem-Based Learning, despite recent controversies about its effectiveness, is used extensively as a teaching method throughout higher education. In meteorology, there has been little attempt to incorporate Problem-Based Learning techniques into the curriculum. Motivated by a desire to enhance the reflective engagement of students within a current field course module, this project describes the implementation of two test Problem-Based Learning activities and testing and improvement using several different and complementary means of evaluation. By the end of a 2-year program of design, implementation, testing, and reflection and re-evaluation, two robust, engaging activities have been developed that provide an enhanced and diverse learning environment in the field course. The results suggest that Problem-Based Learning techniques would be a useful addition to the meteorology curriculum and suggestions for courses and activities that may benefit from this approach are included in the conclusions.

Reflections on designing a Biology/Humanities interdisciplinary module

This paper uses the reflections of a recent workshop on biology and the humanities subject areas to consider the potential for designing a first year interdisciplinary module that brings together teachers and learners in the Biosciences with their counterparts in English and History. It considers three building blocks of module design: aims and objectives; teaching and learning strategies; and assessment; and provides a commentary on the discussion of interdisciplinarity in the broader literature. The authors argue that interdisciplinary teaching and learning must be transformative, but not in the way many previous advocates of interdisciplinarity have assumed. Rather than transcending disciplines, the authors contend that the aim should be to enhance disciplinary understanding. Learners should emerge from the interdisciplinary module not having lost their identity as biologists, but having enhanced it. They should have become ‘better’ biologists in the sense of having developed a broader, critical understanding of the precepts of their discipline, as a first step to an understanding of biology inflected with a literary and historical awareness.