Translating Feedforward Neural Nets to SOM-like Maps

A major disadvantage of feedforward neural networks is still the difficulty to gain insight into their internal functionality. This is much less the case for, e.g., nets that are trained unsupervised, such as Kohonen¿s self-organizing feature maps (SOMs). These offer a direct view into the stored knowledge, as their internal knowledge is stored in the same format as the input data that was used for training or is used for evaluation. This paper discusses a mathematical transformation of a feed-forward network into a SOMlike structure such that its internal knowledge can be visually interpreted. This is particularly applicable to networks trained in the general classification problem domain

On the determination of probability density functions by using Neural Networks

It is well known that the output of a Neural Network trained to disentangle between two classes has a probabilistic interpretation in terms of the a-posteriori Bayesian probability, provided that a unary representation is taken for the output patterns. This fact is used to make Neural Networks approximate probability density functions from examples in an unbinned way, giving a better performace than ``standard binned procedures''. In addition, the mapped p.d.f. has an analytical expression.Comment: 13 pages including 3 eps figures. Submitted to Comput. Phys. Commu

Crack detection in a rotating shaft using artificial neural networks and PSD characterisation

Peer reviewedPostprin

New Models of Technology Assessment for Development

This report explores the role that ‘new models’ of technology assessment can play in improving the lives of poor and vulnerable populations in the developing world. The ‘new models’ addressed here combine citizen and decision-maker participation with technical expertise. They are virtual and networked rather than being based in a single office of technology assessment (as was the case in the United States in the 1970s-90s). They are flexible enough to address issues across disciplines and are increasingly transnational or global in their reach and scope. The report argues that these new models of technology assessment can make a vital contribution to informing policies and strategies around innovation, particularly in developing regions. They are most beneficial if they enable the broadening out of inputs to technology assessment, and the opening up of political debate around possible directions of technological change and their interactions with social and environmental systems. Beyond the process of technology assessment itself, the report argues that governance systems within which these processes are embedded play an important role in determining the impact and effectiveness of technology assessment. Finally, the report argues for training and capacity-building in technology assessment methodologies in developing countries, and support for internationally co-ordinated technology assessment efforts to address global and regional development challenges

Artificial Neural Network Representations for Hierarchical Preference Structures

In this paper, we introduce two artificial neural network formulations that can be used to predict the preference ratings from the pairwise comparison matrices of the Analytic Hierarchy Process (AHP). First, we introduce a modified Hopfield network that can be used to exactly determine the vector of preference ratings associated with a positive reciprocal comparison matrix. The dynamics of this network are mathematically equivalent to the power method, a widely used numerical method for computing the principal eigenvectors of square matrices. However, we show that the Hopfield network representation is incapable of generalizing the preference patterns, and consequently is not suitable for approximating the preference ratings if the preference information is imprecise. Then we present a feed-forward neural network formulation that does have the ability to accurately approximate the preference ratings. A simulation experiment is used to verify the robustness of the feed-forward neural network formulation with respect to imprecise pairwise judgments. From the results of this experiment, we conclude that the feed-forward neural network formulation appears to be a powerful tool for analyzing discrete alternative multicriteria decision problems with imprecise or fuzzy ratio-scale preference judgments

Cash & Compassion: The Somali Diaspora's Role in Relief, Development & Peacebuilding

This research report, commissioned by UNDP Somalia, is based on work done in six diaspora hubs (Dubai, London, Minneapolis, Nairobi, Oslo, and Toronto) as well as in Somaliland, Puntland and South/Central Somalia. It examines the involvement of Somalis in the diaspora in dynamics in their country of origin, including collective and social remittances. Volume 1 contains the full report. Volume 2 contains the research guides, terms of reference and other annexes