Visualization of dynamics using local dynamic modelling with self organizing maps

In this work, we describe a procedure to visualize nonlinear process dynamics using a self-organizing map based local model dynamical estimator. The proposed method exploits the topology preserving nature of the resulting estimator to extract visualizations (planes) of insightful dynamical features, that allow to explore nonlinear systems whose behavior changes with the operating point. Since the visualizations are obtained from a dynamical model of the process, measures on the goodness of this estimator (such as RMSE or AIC) are also applicable as a measure of the trustfulness of the visualizations. To illustrate the application of the proposed method, an experiment to analyze the dynamics of a nonlinear system on different operating points is include

Complex Systems Science: Dreams of Universality, Reality of Interdisciplinarity

Using a large database (~ 215 000 records) of relevant articles, we empirically study the "complex systems" field and its claims to find universal principles applying to systems in general. The study of references shared by the papers allows us to obtain a global point of view on the structure of this highly interdisciplinary field. We show that its overall coherence does not arise from a universal theory but instead from computational techniques and fruitful adaptations of the idea of self-organization to specific systems. We also find that communication between different disciplines goes through specific "trading zones", ie sub-communities that create an interface around specific tools (a DNA microchip) or concepts (a network).Comment: Journal of the American Society for Information Science and Technology (2012) 10.1002/asi.2264

Magnification Control in Self-Organizing Maps and Neural Gas

We consider different ways to control the magnification in self-organizing maps (SOM) and neural gas (NG). Starting from early approaches of magnification control in vector quantization, we then concentrate on different approaches for SOM and NG. We show that three structurally similar approaches can be applied to both algorithms: localized learning, concave-convex learning, and winner relaxing learning. Thereby, the approach of concave-convex learning in SOM is extended to a more general description, whereas the concave-convex learning for NG is new. In general, the control mechanisms generate only slightly different behavior comparing both neural algorithms. However, we emphasize that the NG results are valid for any data dimension, whereas in the SOM case the results hold only for the one-dimensional case.Comment: 24 pages, 4 figure

Industrial process monitoring by means of recurrent neural networks and Self Organizing Maps

Industrial manufacturing plants often suffer from reliability problems during their day-to-day operations which have the potential for causing a great impact on the effectiveness and performance of the overall process and the sub-processes involved. Time-series forecasting of critical industrial signals presents itself as a way to reduce this impact by extracting knowledge regarding the internal dynamics of the process and advice any process deviations before it affects the productive process. In this paper, a novel industrial condition monitoring approach based on the combination of Self Organizing Maps for operating point codification and Recurrent Neural Networks for critical signal modeling is proposed. The combination of both methods presents a strong synergy, the information of the operating condition given by the interpretation of the maps helps the model to improve generalization, one of the drawbacks of recurrent networks, while assuring high accuracy and precision rates. Finally, the complete methodology, in terms of performance and effectiveness is validated experimentally with real data from a copper rod industrial plant.Postprint (published version

Network geography: relations, interactions, scaling and spatial processes in GIS

This chapter argues that the representational basis of GIS largely avoidseven the most rudimentary distortions of Euclidean space as reflected, forexample, in the notion of the network. Processes acting on networks whichinvolve both short and longer term dynamics are often absent from GIscience. However a sea change is taking place in the way we view thegeography of natural and man-made systems. This is emphasising theirdynamics and the way they evolve from the bottom up, with networks anessential constituent of this decentralized paradigm. Here we will sketchthese developments, showing how ideas about graphs in terms of the waythey evolve as connected, self-organised structures reflected in theirscaling, are generating new and important views of geographical space.We argue that GI science must respond to such developments and needs tofind new forms of representation which enable both theory andapplications through software to be extended to embrace this new scienceof networks

A spatial analytical approach to urbanisation

Vaz, E., Damásio, B., Bação, F., Kotha, M., Penfound, E., & Rai, S. K. (2021). Mumbai's business landscape: A spatial analytical approach to urbanisation. Heliyon, 7(7), [e07522]. https://doi.org/10.1016/j.heliyon.2021.e07522India has proven to be one of the most diverse and dynamic economic regions in the world. Its industry focuses predominantly on the service sector and immediate economic growth seems to steer India into the economic superpower. India's unique business landscape is felt at a regional level, where massive urbanization has become an unavoidable consequence of population growth and spatial allocation to the economic hubs of metropolitan cities. Mumbai, one of the world's largest cities, represents a unique combination of a diverse economic landscape and the growth of a megacity. The role of Mumbai in India's growth is of crucial importance for India's business landscape. This paper explores the massive urbanization processes of Mumbai's peri-urban areas and compares urban sprawl with the location of its business landscape. A spatial accounting methodology based on the proximity of Mumbai's different economic hubs will be used to measure the underlying pattern of the Mumbai region, concerning past and present urbanization, and the effect of this urbanization process has on the possible location of businesses. This business-urban ecosystem perspective will be implemented by a spatial analysis on the correlation between urban compactness and urban footprints, in relation to business concentration and its spatiotemporal evolution over the last hundred years.publishersversionpublishe