Se prosigue con el estudio de los espacios métricos fuzzy introducidos por George y Veeramani. Se aportan nuevas propiedades y se tratan cuestiones como la completación, la continuidad uniforme y teoremas de punto fijo. Se introducen nuevos ejemplos (alguno de ellos de especial relevancia) y se dan resultados acerca de la precompacidad en espacios métricos fuzzy. Además, se desarrolla el estudio de las métricas fuzzy no arquimedianas y se aborda la cuestión de la completación de los espacios métricos fuzzy y se comprueba que, en este aspecto, existe una diferencia significativa con la teoría de los espacios métricos, pues no todo espacio métrico fuzzy admite completación. Se estudia la noción de continuidad uniforme y se definen los conceptos de equinormalidad y propiedad de Lebesgue para una métrica fuzzy ("análogos" a los clásicos) que permiten demostrar un teorema en el que se caracterizan los espacios métricos fuzzy en los que toda función real continua es uniformemente continua por el hecho de que la métrica fuzzy sea equinormal o cumpla la propiedad de Lebesgue. Además, se introduce el concepto de continuidad t-uniforme (que no tiene "homólogo" en la teoría clásica pero está estrechamente relacionado con la noción de contractividad que se aporta en el último capítulo) que permite caracterizar los espacios métricos fuzzy en los que toda función real continua es t-uniformemente continua mediante una adecuada definición de métrica fuzzy t-equinormal. Por último se introduce el concepto de aplicación contractiva fuzzy y se obtienen teoremas de punto fijo para este tipo de aplicaciones en espacios métricos fuzzy. Se establece que toda aplicación contractiva fuzzy en un espacio métrico fuzzy completo en el que toda sucesión contractiva fuzzy es una sucesión de Cauchy posee un único punto fijo. / Sapena Piera, A. (2002). Espacios métricos fuzzy definidos por t-normas [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/5425 / Palancia

This thesis deals with the use of basic means of artificial intelligence, respectively fuzzy logic to evaluate the benefits of company's potential suppliers. There are mainly used principles of fuzzy logic in MATLAB, expert systems and analysis in MS Excel respectively. VBA development environment.

This Master’s thesis deals with adaptation of fuzzy system parameters with main aim on artificial neural network. Current knowledge of methods connecting fuzzy systems and artificial neural networks is discussed in the search part of this work. The search in Student’s works is discussed either. Chapter focused on methods application deals with classifying ability verification of the chosen fuzzy-neural network with Kohonen learning algorithm. Later the model of fuzzy system with parameters adaptation based on fuzzyneural network with Kohonen learning algorithm is shown.

This Master's thesis deals with design of models for rating of M. K. R. plus suppliers of steel wires using fuzzy logic. The decision-making models are created in the MS Excel and in the MATLAB software. This thesis includes acknowledgement with theory, which will be used in the practical part for design of individual models. Current and potential suppliers of the company will be evaluated using the created models and their benefit to company will be assessed.

This master thesis deals with the application of fuzzy logic for evaluating the quality of customers in company PPL CZ s.r.o. For evaluation was used Microsoft Excel and MathWorks MATLAB. There are going to be created models using fuzzy logic. The introductory part is devoted to the theoretical bases that are necessary to understand the whole issue. The second part includes information about the company and analyzes of the current situation. Finally, the last part presents fuzzy models used to evaluation of existing customers.

A project report submitted to the Faculty of Engineering, University of Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science in Engineering. Johannesburg 1997. / This project report describes the design of a fuzzy logic controller for automatic generation control (AGC) in Eskom in 1995 and the process of re-optimisation of the fuzzy logic controller in 1997. The main purpose of the AGC controller is to determine the shortfall or surplus generation of electricity for South Africa. The difficulties associated with optimising the original AGC controller, the design,implementation and optimisation of the fuzzy controller are described in detail. [Abbreviated Abstract. Open document to view full version] / AC2017

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Data Envelopment Analysis (DEA) is a linear programming technique that has been used extensively in the literature to measure relative efficiency. One of the main attributes of DEA is that it can model multiple inputs and multiple outputs in the model. In this research work, attributes pertaining to service quality have been modeled using the Network model.The primary research is the augmentation of the existing Network model to include input/ output variables that are imprecise from a measurement point of view. These variables are qualitative assessments that have a linguistic representation/ interpretation. A very good example of this variable would be "Pleasantness". Given the fact that there are different evaluators, there is a certain degree of impreciseness associated with the representation of each of these qualitative variables. This imprecision is captured using the fuzzy sets. The triangular membership functions were used to describe the membership functions. So a unique network model that captured fuzzy variables was created. The second main research contribution is that this is the first attempt of capturing service quality and efficiency of customer service representatives. The generic model that was created was used to evaluate the performance of the customer service representatives in a major airline. The results that were obtained, was shared with the decision makers at the airline for validation. The results that were obtained from the model also helped us validate the model with the other existing models.One of the main advantages of using the DEA/ Network/ Fuzzy model was that the imprecision involved in measuring the customer service representatives were accounted for. This enabled the decision maker in making the right decisions and not penalizing a customer service representative for imprecision in the data. Graphical Interpretations were also provided for the results that were obtained from the analysis. / Master of Science

No / A robust method, fuzzy kNNModel, for toxicity prediction of chemical compounds is proposed. The method is based on a supervised clustering method, called kNNModel, which employs fuzzy partitioning instead of crisp partitioning to group clusters. The merits of fuzzy kNNModel are two-fold: (1) it overcomes the problems of choosing the parameter ¿ ¿ allowed error rate in a cluster and the parameter N ¿ minimal number of instances covered by a cluster, for each data set; (2) it better captures the characteristics of boundary data by assigning them with different degrees of membership between 0 and 1 to different clusters. The experimental results of fuzzy kNNModel conducted on thirteen public data sets from UCI machine learning repository and seven toxicity data sets from real-world applications, are compared with the results of fuzzy c-means clustering, k-means clustering, kNN, fuzzy kNN, and kNNModel in terms of classification performance. This application shows that fuzzy kNNModel is a promising method for the toxicity prediction of chemical compounds.

No / There has been an increased interest in video indexing and retrieval in recent years. In this work, indexing and retrieval system of the visual contents is based on feature extracted from the compressed domain. Direct possessing of the compressed domain spares the decoding time, which is extremely important when indexing large number of multimedia archives. A fuzzy-categorizing structure is designed in this paper to improve the retrieval performance. In our experiment, a database that consists of basketball videos has been constructed for our study. This database includes three categories: full-court match, penalty and close-up. First, spatial and temporal feature extraction is applied to train the fuzzy membership functions using the minimum entropy optimal algorithm. Then, the max composition operation is used to generate a new fuzzy feature to represent the content of the shots. Finally, the fuzzy-based representation becomes the indexing feature for the content-based video retrieval system. The experimental results show that the proposal algorithm is quite promising for semantic-based video retrieval.