A silent witness? Medieval urban landscapes and unfolding their mapping histories

This paper offers a critical reflection on the place of maps in writing medieval urban histories. Using findings from recent research on medieval Swansea, the case is made that mapping provides an interpretative space for exploring alternative narratives about past places. To do so the paper draws upon current critical debates on cartography, particularly the idea that mapping is fluid and iterative, to suggest that Swansea's medieval urban origins are open to a range of alternative interpretations. This approach to mapping differs from that often used by historians to map medieval urban landscapes, where historic maps are simply used as ‘sources’, and the landscapes they represent used as ‘witnesses’ to past events, for creating maps, both through digital and analogue media, instead opens up – or unfolds – a landscape's past. The paper uses past attempts to map medieval Swansea to highlight difficulties in interpreting its urban landscape features, and uses multiple mappings of the medieval townscape, resulting from recent research, to question how far any sources about the past really provide a coherent narrative. Instead, multiple mappings of the medieval urban landscape – reflecting different and competing perspectives – are more attuned with how places were perceived and understood during the Middle Ages.

A practical approach for training dynamic recurrent neural networks: use of Apriori information

Presents a technique for incorporating a priori knowledge from a state space system into a neural network training algorithm. The training algorithm considered is that of chemotaxis and the networks being trained are recurrent neural networks. Incorporation of the a priori knowledge ensures that the resultant network has behaviour similar to the system which it is modelling.

Dealing with complexity: an overview of the neural network approach

The problem of complexity is particularly relevant to the field of control engineering, since many engineering problems are inherently complex. The inherent complexity is such that straightforward computational problem solutions often produce very poor results. Although parallel processing can alleviate the problem to some extent, it is artificial neural networks (in various forms) which have recently proved particularly effective, even in dealing with the causes of the problem itself. This paper presents an overview of the current neural network research being undertaken. Such research aims to solve the complex problems found in many areas of science and engineering today.

Multiple PID mapping using neural networks in a MIMO steam generator system

A neural network was used to map three PID operating regions for a two-input two-output steam generator system. The network was used in stand alone feedforward operation to control the whole operating range of the process, after being trained from the PID controllers corresponding to each control region. The network inputs are the plant error signals, their integral, their derivative and a 4-error delay train.