Estudo Comparativo de Métricas de Pontuação para Aprendizagem Estrutural de Redes Bayesianas

Bayesian networks are powerful tools as they represent probability distributions as graphs. They work with uncertainties of real systems. Since last decade there is a special interest in learning network structures from data. However learning the best network structure is a NP-Hard problem, so many heuristics algorithms to generate network structures from data were created. Many of these algorithms use score metrics to generate the network model. This thesis compare three of most used score metrics. The K-2 algorithm and two pattern benchmarks, ASIA and ALARM, were used to carry out the comparison. Results show that score metrics with hyperparameters that strength the tendency to select simpler network structures are better than score metrics with weaker tendency to select simpler network structures for both metrics (Heckerman-Geiger and modified MDL). Heckerman-Geiger Bayesian score metric works better than MDL with large datasets and MDL works better than Heckerman-Geiger with small datasets. The modified MDL gives similar results to Heckerman-Geiger for large datasets and close results to MDL for small datasets with stronger tendency to select simpler network structures

Gestão do Conhecimento e o uso estratégico de Tecnologias de Informação: um estudo em Redes de Franquias no Estado do Rio Grande do Norte

This study discusses the use of information technologies for knowledge management in networks of franchises in the Rio Grande do Norte/Brazil, whose management and operation are complex activities, characterized by the geographic spread of their network unities, creating barriers to communication and information sharing between franchisors, franchisees and final customers. In view of this, the following hypotheses were formulated: the knowledge management can be a positive alternative for improving communication between units; and information technology can eliminate many problems related mainly to capture and share knowledge. In general, it aims to investigate, in qualitative and quantitative aspects, how information technology can support knowledge management in networks of franchises. Specifically purposes to register the existence of managerial practices related to knowledge management in enterprises at the franchising sector; to verify whether they have the technological resources with the potential to facilitate the sharing of information; to identify what are the technologies of information and communication used in the organizational environment; and suggest measures that will facilitate the process of organizational learning, using information technology and communication as tools. It concludes that knowledge management becomes a positive alternative, especially in strengthening of bonds of communication and sharing of knowledge between the franchises. In this regard, information technology must provide all the services of the corporation to facilitate communication between franchisor and franchisee, through a single and integrated system. However, they still show unsuitable for more sophisticated technology platforms

Identificação não linear usando uma rede fuzzy wavelet neural network modificada

In last decades, neural networks have been established as a major tool for the identification of nonlinear systems. Among the various types of networks used in identification, one that can be highlighted is the wavelet neural network (WNN). This network combines the characteristics of wavelet multiresolution theory with learning ability and generalization of neural networks usually, providing more accurate models than those ones obtained by traditional networks. An extension of WNN networks is to combine the neuro-fuzzy ANFIS (Adaptive Network Based Fuzzy Inference System) structure with wavelets, leading to generate the Fuzzy Wavelet Neural Network - FWNN structure. This network is very similar to ANFIS networks, with the difference that traditional polynomials present in consequent of this network are replaced by WNN networks. This paper proposes the identification of nonlinear dynamical systems from a network FWNN modified. In the proposed structure, functions only wavelets are used in the consequent. Thus, it is possible to obtain a simplification of the structure, reducing the number of adjustable parameters of the network. To evaluate the performance of network FWNN with this modification, an analysis of network performance is made, verifying advantages, disadvantages and cost effectiveness when compared to other existing FWNN structures in literature. The evaluations are carried out via the identification of two simulated systems traditionally found in the literature and a real nonlinear system, consisting of a nonlinear multi section tank. Finally, the network is used to infer values of temperature and humidity inside of a neonatal incubator. The execution of such analyzes is based on various criteria, like: mean squared error, number of training epochs, number of adjustable parameters, the variation of the mean square error, among others. The results found show the generalization ability of the modified structure, despite the simplification performed