A neural network algorithm to simplify 2D meshes

In this paper we propose a neural network model to simplify and 2D meshes. This model is based on the Growing Neural Gas model and is able to simplify any mesh with different topologies and sizes. A triangulation process is included with the objective to reconstruct the mesh. This model is applied to some problems related to urban networks.

Growing Neural Gas approach for obtaining homogeneous maps by restricting the insertion of new nodes

The Growing Neural Gas model is used widely in artificial neural networks. However, its application is limited in some contexts by the proliferation of nodes in dense areas of the input space. In this study, we introduce some modifications to address this problem by imposing three restrictions on the insertion of new nodes. Each restriction aims to maintain the homogeneous values of selected criteria. One criterion is related to the square error of classification and an alternative approach is proposed for avoiding additional computational costs. Three parameters are added that allow the regulation of the restriction criteria. The resulting algorithm allows models to be obtained that suit specific needs by specifying meaningful parameters.

Synthesis, structural order and magnetic behavior of self-assembled epsilon-Co nanocrystal arrays

The synthesis of magnetic nanoparticles with monodispere size distributions, their self assembly into ordered arrays and their magnetic behavior as a function of structural order (ferrofluids and 2D assemblies) are presented. Magnetic colloids of monodispersed, passivated, cobalt nanocrystals were produced by the rapid pyrolysis of cobalt carbonyl in solution. The size, size distribution (std. dev.< 5%) and the shape of the nanocrystals were controlled by varying the surfactant, its concentration, the reaction rate and the reaction temperature. The Co particles are defect-free single crystals with a complex cubic structure related to the beta phase of manganese (epsilon-Co). In the 2D assembly, a collective behavior was observed in the low-field susceptibility measurements where the magnetization of the zero field cooled process increases steadily and the magnetization of the field cooling process is independent the temperature. This was different from the observed behavior in a sample comprised of disordered interacting particles. A strong paramagnetic contribution appears at very low temperatures where the magnetization increases drastically after field cooling the sample. This has been attributed to the Co surfactant-particle interface since no magnetic atomic impurities are present in these samples.

Yhteiskunnallisen sisältöpalvelun sosioteknologinen tarkastelu Kohosen itseorganisoituvien karttojen avulla - Case deittipalvelu

Työn tavoitteena on tutkia deittipalvelun käyttäjien anonyymiaineistoa neuroverkko-opetuksessa segmentoituneiden piirrekarttojen (SOM, Self-Organizing Map) avulla. Näiden piirrekarttojen avulla on tarkoitus selvittää, löytyykö mahdollisesti selkeitä SMS- ja e-mail - käyttäjäryhmiä. Tutkimusta lähestytään perehtymällä ensin yrityksen tekniseen palvelualusta-arkkitehtuuriin ja myös varsinaiseen deittipalveluun käyttäjän kannalta.Tutkimus aloitettiin koodaamalla tietoaineisto SOM Toolbox-ohjelmalle käytettäväksi. Varsinaisia tutkimustuloksia analysoitiin valitsemalla otoksia neuroverkko-opetuksessa segmentoituneista piirrekartoista. Saadut tulokset osoittavat, ettäSOM-teknologia soveltuu hyvin sisältöpalveluiden sosioteknologiseen tutkimukseen ja sitä on myös mahdollista käyttää asiakkuudenhallinnassa erilaisten käyttäjäryhmien profilointiin.

Predicting diffusion of innovations with self-organisation and machine learning

The main subject of this master's thesis was predicting diffusion of innovations. The prediction was done in a special case: product has been available in some countries, and based on its diffusion in those countries the prediction is done for other countries. The prediction was based on finding similar countries with Self-Organizing Map~(SOM), using parameters of countries. Parameters included various economical and social key figures. SOM was optimised for different products using two different methods: (a) by adding diffusion information of products to the country parameters, and (b) by weighting the country parameters based on their importance for the diffusion of different products. A novel method using Differential Evolution (DE) was developed to solve the latter, highly non-linear optimisation problem. Results were fairly good. The prediction method seems to be on a solid theoretical foundation. The results based on country data were good. Instead, optimisation for different products did not generally offer clear benefit, but in some cases the improvement was clearly noticeable. The weights found for the parameters of the countries with the developed SOM optimisation method were interesting, and most of them could be explained by properties of the products.

Self-organized sociopolitical interactions as the best way to achieve organized patterns in human social systems: going beyond the top-down control of classical political regimes

La monografía presenta la auto-organización sociopolítica como la mejor manera de lograr patrones organizados en los sistemas sociales humanos, dada su naturaleza compleja y la imposibilidad de las tareas computacionales de los regímenes políticos clásico, debido a que operan con control jerárquico, el cual ha demostrado no ser óptimo en la producción de orden en los sistemas sociales humanos. En la monografía se extrapola la teoría de la auto-organización en los sistemas biológicos a las dinámicas sociopolíticas humanas, buscando maneras óptimas de organizarlas, y se afirma que redes complejas anárquicas son la estructura emergente de la auto-organización sociopolítica.

Self-organising neural networks for adaptive control

Self-organizing neural networks have been implemented in a wide range of application areas such as speech processing, image processing, optimization and robotics. Recent variations to the basic model proposed by the authors enable it to order state space using a subset of the input vector and to apply a local adaptation procedure that does not rely on a predefined test duration limit. Both these variations have been incorporated into a new feature map architecture that forms an integral part of an Hybrid Learning System (HLS) based on a genetic-based classifier system. Problems are represented within HLS as objects characterized by environmental features. Objects controlled by the system have preset targets set against a subset of their features. The system's objective is to achieve these targets by evolving a behavioural repertoire that efficiently explores and exploits the problem environment. Feature maps encode two types of knowledge within HLS — long-term memory traces of useful regularities within the environment and the classifier performance data calibrated against an object's feature states and targets. Self-organization of these networks constitutes non-genetic-based (experience-driven) learning within HLS. This paper presents a description of the HLS architecture and an analysis of the modified feature map implementing associative memory. Initial results are presented that demonstrate the behaviour of the system on a simple control task.

Neural network feature maps for Chinese phonemes

It has been shown through a number of experiments that neural networks can be used for a phonetic typewriter. Algorithms can be looked on as producing self-organizing feature maps which correspond to phonemes. In the Chinese language the utterance of a Chinese character consists of a very simple string of Chinese phonemes. With this as a starting point, a neural network feature map for Chinese phonemes can be built up. In this paper, feature map structures for Chinese phonemes are discussed and tested. This research on a Chinese phonetic feature map is important both for Chinese speech recognition and for building a Chinese phonetic typewriter.

Simulated annealing technique for fast learning of SOM networks

The Self-Organizing Map (SOM) is a popular unsupervised neural network able to provide effective clustering and data visualization for multidimensional input datasets. In this paper, we present an application of the simulated annealing procedure to the SOM learning algorithm with the aim to obtain a fast learning and better performances in terms of quantization error. The proposed learning algorithm is called Fast Learning Self-Organized Map, and it does not affect the easiness of the basic learning algorithm of the standard SOM. The proposed learning algorithm also improves the quality of resulting maps by providing better clustering quality and topology preservation of input multi-dimensional data. Several experiments are used to compare the proposed approach with the original algorithm and some of its modification and speed-up techniques.

Extração de conhecimento em forma de regras difusas a partir de mapas auto-organizáveis de Kohonen: aplicação em diagnóstico de faltas incipientes em transformadores

Apesar das diversas vantagens oferecidas pelas redes neurais artificiais (RNAs), algumas limitações ainda impedem sua larga utilização, principalmente em aplicações que necessitem de tomada de decisões essenciais para garantir a segurança em ambientes como, por exemplo, em Sistemas de Energia. Uma das principais limitações das RNAs diz respeito à incapacidade que estas redes apresentam de explicar como chegam a determinadas decisões; explicação esta que seja humanamente compreensível. Desta forma, este trabalho propõe um método para extração de regras a partir do mapa auto-organizável de Kohonen, projetando um sistema de inferência difusa capaz de explicar as decisões/classificação obtidas através do mapa. A metodologia proposta é aplicada ao problema de diagnóstico de faltas incipientes em transformadores, em que se obtém um sistema classificatório eficiente e com capacidade de explicação em relação aos resultados obtidos, o que gera mais confiança aos especialistas da área na hora de tomar decisões.

Structure and dynamics of supramolecular assemblies studied by advanced solid-state NMR spectroscopy

Ziel der vorliegenden Arbeit ist die Aufklärung von Struktur und Dynamik komplexer supramolekularer Systeme mittels Festkörper NMR Spektroskopie. Die Untersuchung von pi-pi Wechselwirkungen, welche einen entscheidenden Einfluss auf die strukturellen und dynamischen Eigenschaften supra- molekularer Systeme haben, hilft dabei, die Selbst- organisationsprozesse dieser komplexen Materialien besser zu verstehen. Mit dipolaren 1H-1H and 1H-13C Wiedereinkopplungs NMR Methoden unter schnellem MAS können sowohl 1H chemische Verschiebungen als auch dipolare 1H-1H und 1H-13C Kopplungen untersucht werden, ohne dass eine Isotopenmarkierung erforderlich ist. So erhält man detaillierte Informationen über die Struktur und die Beweglichkeit einzelner Molekül- segmente. In Verbindung mit sogenannten nucleus independent chemical shift (NICS) maps (berechnet mit ab-initio Methoden) lassen sich Abstände von Protonen relativ zu pi-Elektronensystemen bestimmen und so Strukturvorschläge ableiten. Mit Hilfe von homo- und heteronuklearen dipolaren Rotationsseitenbandenmustern könnenaußerdem Ordnungs- parameter für verschiedene Molekülsegmente bestimmt werden. Die auf diese Weise gewonnenen Informationen über die strukturellen und dynamischen Eigenschaften supramolekularer Systeme tragen dazu bei, strukturbestimmende Molekül- einheiten und Hauptordnungsphänomene zu identifizieren sowie lokale Wechselwirkungen zu quantifizieren, um so den Vorgang der Selbstorganisation besser zu verstehen.

Timed mind maps using MINDMANAGER & MS-PROJECT for educational innovation

The adaptation to the European Higher Education Area (EHEA) is becoming a great challenge for the University Community, especially for its teaching and research staff, which is involved actively in the teaching-learning process. It is also inducing a paradigm change for lecturers and students. Among the methodologies used for processes of teaching innovation, system thinking plays an important role when working mainly with mind maps, and is focused to highlighting the essence of the knowledge, allowing its visual representation. In this paper, a method for using these mind maps for organizing a particular subject is explained. This organization is completed with the definition of duration, precedence relationships and resources for each of these activities, as well as with their corresponding monitoring. Mind maps are generated by means of the MINDMANAGER package whilst Ms-PROJECT is used for establishing tasks relationships, durations, resources, and monitoring. Summarizing, a procedure and the necessary set of applications for self organizing and managing (timed) scheduled teaching tasks has been described in this paper.

Timed mind maps using mindmanager & msproject for educational innovation

The adaptation to the European Higher Education Area (EHEA) is becoming a great challenge for the University Community, especially for its teaching and research staff, which is involved actively in the teaching-learning process. It is also inducing a paradigm change for lecturers and students. Among the methodologies used for processes of teaching innovation, system thinking plays an important role when working mainly with mind maps, and is focused to highlighting the essence of the knowledge, allowing its visual representation. In this paper, a method for using these mind maps for organizing a particular subject is explained. This organization is completed with the definition of duration, precedence relationships and resources for each of these activities, as well as with their corresponding monitoring. Mind maps are generated by means of the MINDMANAGER package whilst Ms-PROJECT is used for establishing tasks relationships, durations, resources, and monitoring. Summarizing, a procedure and the necessary set of applications for self organizing and managing (timed) scheduled teaching tasks has been described in this paper

Representation of 2D objects with a topology preserving network

Comunicación presentada en el 2nd International Workshop on Pattern Recognition in Information Systems, Alicante, April, 2002.

Principles of the design of self-learning systems, USSR.

"Translation of the ... collection of reports" [entitled]: Print︠s︡ipy postroenii︠a︡ samoobuchaiushchikhsia sistem (romanized form) Kiev, 1962.