Advances in Self Organising Maps

The Self-Organizing Map (SOM) with its related extensions is the most popular artificial neural algorithm for use in unsupervised learning, clustering, classification and data visualization. Over 5,000 publications have been reported in the open literature, and many commercial projects employ the SOM as a tool for solving hard real-world problems. Each two years, the "Workshop on Self-Organizing Maps" (WSOM) covers the new developments in the field. The WSOM series of conferences was initiated in 1997 by Prof. Teuvo Kohonen, and has been successfully organized in 1997 and 1999 by the Helsinki University of Technology, in 2001 by the University of Lincolnshire and Humberside, and in 2003 by the Kyushu Institute of Technology. The Universit\'{e} Paris I Panth\'{e}on Sorbonne (SAMOS-MATISSE research centre) organized WSOM 2005 in Paris on September 5-8, 2005.Comment: Special Issue of the Neural Networks Journal after WSOM 05 in Pari

Sustainable rural livelihoods to analyse family farming dynamics: A comparative perspective

The very nature of family farming makes it a complex scientific subject, being at the same time a social form of production and an economic agent. Its nature challenges disciplines that most of the time overlook dimensions that do not fit in with their own framework leading to partial views in anthropology, sociology, political science or economics, just to mention the most common disciplinary focus on rural societies. We suggest exploring the well-known Sustainable Rural Livelihood framework as a comprehensive and open conceptual design to address the evolution of family farming. While the entry point concerns individuals, it also considers the social structures and institutions in which they are embedded. It also contemplates natural, social and human assets in addition to physical and financial ones. The activity system developed by each individual within its social setting goes beyond sectorial approaches; the strategies developed are contextualized and influenced by policies. To illustrate how this framework can be implemented, we developed a case study approach in contrasting rural contexts ranging from Argentina, Brazil or Nicaragua for Latin American situations, to Indonesia, China or India for Asia, or to Mali, Cameroon or Mozambique for African illustrations. The cases will not be extensively presented here as we choose to highlight some of the main findings and crosscutting themes as ways and means of adapting to changing contexts. We also discuss the challenges and perspectives faced by family farming from other forms of production and provide some insight into "blind" issues: the social drawbacks and political dimensions linked to agriculture related to broader territorial and national concerns

Predictive functional control for the temperature control of a chemical batch reactor

A predictive functional control (PFC) technique is applied to the temperature control of a pilot-plant batch reactor equipped with a mono-fluid heating/cooling system. A cascade control structure has been implemented according to the process sub-units reactor and heating/cooling system. Hereby differences in the sub-units dynamics are taken into consideration. PFC technique is described and its main differences with a standard model predictive control (MPC) technique are discussed. To evaluate its robustness, PFC has been applied to the temperature control of an exothermic chemical reaction. Experimental results show that PFC enables a precise tracking of the set-point temperature and that the PFC performances are mainly determined by its internal dynamic process model. Finally, results show the performance of the cascade control structure to handle different dynamics of the heating/cooling system