Pagamento de proventos versus preços deações maduras e em expansão segundo Kohonen Maps

Este estudo investiga se a política de distribuição de resultados seria capaz de alterar os preços das ações de uma empresa. O objetivo deste trabalho é discutir os impactos do pagamento de proventos sobre os preços das ações, na data ex direito, de empresas maduras e de empresas em expansão, considerando-se ainda o efeito da classe da ação (ordinária ou preferencial) sobre os resultados. Para tal, adotou-se a metodologia de dados em painel, segmentando a amostra a partir dos Mapas Auto-organizáveis de Kohonen. Os resultados revelam que a estratégia de curto prazo de comprar ações na última data com, vender na primeira data ex e embolsar os dividendos é capaz de gerar perdas de capital que superam em até quatro vezes o ganho líquido decorrente do provento embolsado.

Utilização do algoritmo Self-Organizing Map na caracterização de organizações públicas portuguesas.

Dissertação apresentada como requisito parcial para a obtenção do grau de mestre em Estatística e Gestão de Informação.

Toiminnanjohtaja Teuvo Lagerstedt 30/5 1973

Kirje 30.5.1973

A tabela periódica dos elementos químicos prevista por redes neurais artificiais de Kohonen

Although several chemical elements were not known by end of the 18th century, Mendeleyev came up with an astonishing achievement: the periodic table of elements. He was not only able to predict the existence of (then) new elements but also to provide accurate estimates of their chemical and physical properties. This is certainly a relevant example of the human intelligence. Here, we intend to shed some light on the following question: Can an artificial intelligence system yield a classification of the elements that resembles, in some sense, the periodic table? To achieve our goal, we have fed a self-organized map (SOM) with information available at Mendeleyev's time. Our results show that similar elements tend to form individual clusters. Thus, SOM generates clusters of halogens, alkaline metals and transition metals that show a similarity with the periodic table of elements.

Arkistoluettelo 731: Teuvo Laine

Luettelo Kansalliskirjastossa olevan Teuvo Laineen arkiston sisällöstä

Pakkala, Teuvo

Teuvo Pakkala, urspr. Theodor Oskar Frosterus f. 9.4.1862 i Uleåborg d. 7.5.1925 i Kuopio Teuvo Pakkala var en av 1800-talets mest centrala realistiska författare, och särskilt känd för sina barnskildringar i novellform. I sina verk Vaaralla (sv. I Vaara) från 1891 och Elsa från 1894 tar Pakkala också i bruk naturalismens stilmedel och ideal i skildringen av destruktiva kvinnoöden. I hans produktion betonas speciellt en ny, modern människobild, baserad på djuppsykologi. Också i barnskildringarna kombineras en psykologiskt inkännande skildring med objektivt betraktande. Barngestalterna är trovärdigt beskrivna komplexa karaktärer, även om den samtida kritiken ofta inte insåg värdet i dessa noveller. Pakkala gav också ut tre dramer. Sånglustspelet Tukkijoella (sv. Timmerflottare) från 1896, som utnyttjar timmerflottarromantiska stämningar, är en av tidernas mest populära finska dramer i hemlandet. http://www.kansallisbiografia.fi/kb/artikkeli/2841/

On-Line Handwritten Character Recognition using Kohonen Networks

This paper presents an efficient Online Handwritten character Recognition System for Malayalam Characters (OHR-M) using Kohonen network. It would help in recognizing Malayalam text entered using pen-like devices. It will be more natural and efficient way for users to enter text using a pen than keyboard and mouse. To identify the difference between similar characters in Malayalam a novel feature extraction method has been adopted-a combination of context bitmap and normalized (x, y) coordinates. The system reported an accuracy of 88.75% which is writer independent with a recognition time of 15-32 milliseconds

The Potential Of Kohonen Method For Classifying Rivers Focused On Regional Environmental Flow Determination With ELOHA

This article highlights the potential benefits that the Kohonen method has for the classification of rivers with similar characteristics by determining regional ecological flows using the ELOHA (Ecological Limits of Hydrologic Alteration) methodology. Currently, there are many methodologies for the classification of rivers, however none of them include the characteristics found in Kohonen method such as (i) providing the number of groups that actually underlie the information presented, (ii) used to make variable importance analysis, (iii) which in any case can display two-dimensional classification process, and (iv) that regardless of the parameters used in the model the clustering structure remains. In order to evaluate the potential benefits of the Kohonen method, 174 flow stations distributed along the great river basin “Magdalena-Cauca” (Colombia) were analyzed. 73 variables were obtained for the classification process in each case. Six trials were done using different combinations of variables and the results were validated against reference classification obtained by Ingfocol in 2010, whose results were also framed using ELOHA guidelines. In the process of validation it was found that two of the tested models reproduced a level higher than 80% of the reference classification with the first trial, meaning that more than 80% of the flow stations analyzed in both models formed invariant groups of streams.

Application of MLP and Kohonen networks for recognition of wear patterns of single-point dressers

Grinding is a parts finishing process for advanced products and surfaces. However, continuous friction between the workpiece and the grinding wheel causes the latter to lose its sharpness, thus impairing the grinding results. This is when the dressing process is required, which consists of sharpening the worn grains of the grinding wheel. The dressing conditions strongly affect the performance of the grinding operation; hence, monitoring them throughout the process can increase its efficiency. The objective of this study was to estimate the wear of a single-point dresser using intelligent systems whose inputs were obtained by the digital processing of acoustic emission signals. Two intelligent systems, the multilayer perceptron and the Kohonen neural network, were compared in terms of their classifying ability. The harmonic content of the acoustic emission signal was found to be influenced by the condition of dresser, and when used to feed the neural networks it is possible to classify the condition of the tool under study.

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.