Thesis (M.A.)--University of Missouri-Columbia, 2008. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on July 7, 2009) Includes bibliographical references.

Another extension of Nonlinear Integral, Upper and Lower Nonlinear Integrals, which is a pair of extreme nonlinear integrals to contain all types of Nonlinear Integrals in the same scheme, is also proposed. It can give a set of upper and lower bounds which include all types of Nonlinear Integrals. We tried to find a solution with the smallest distance between the upper and lower bounds and the smallest error which is a NP hard problem. So we use the multi-objective optimization method to find a set of results for the regression model based on the Upper and Lower Nonlinear Integrals. We can just select one or more optimal solution(s) for a specific problem from the set of results. A weather predictor based on this model has been constructed to predict the next days temperature changing trend and range. / Finally, a NI based data mining framework has been established for identifying the chance of developing liver cancer based on the Hepatitis B Virus DNA sequence data. We have shown that the framework obtains the best diagnosing performance amongst many existing classifiers. / Nonlinear Integral (NI) is a useful integration tool. It has been applied to many areas including classification and regression. The classical method relies on a large number of training data, which lead to large time and space complexity. Moreover, the classical Nonlinear Integral has many limitations. For dealing with different situation, we propose Double Nonlinear Integrals and Nonlinear Integrals with Polynomial Kernel to deal with the problems transversely and longitudinally. / The classical Nonlinear Integrals implement projection along a line with respect to the features. But in many cases the linear projection cannot achieve good performance for classification or regression due to the limitation of the integrand. The linear function used for the integrand is just a special type of polynomial functions with respect to the features. We propose Nonlinear Integral with Polynomial Kernel (NIPK) in which a polynomial function is used as the integrand of Nonlinear Integral. It enables the projection to be along different types of curves on the virtual space, so that the virtual values gotten by the Nonlinear Integrals with Polynomial Kernel can be better regularized and easier to deal with. Experiments show that there is evident improvement of performance for NIPK compared to classical NI. / When the data to be classified have special distribution in the data space, the projection may overlap and the classification accuracy will be lowered. For example, when one group of the data is surrounded by the data of another group, or the number of classes for the data is large. To handle this kind of problems; we propose a new classification model based on the Double Nonlinear Integrals. Double Nonlinear Integral means projecting to a 2-Dimensional space by using the Nonlinear Integral twice in succession and classifying the virtual values in the 2-D space corresponding to the original data. Double Nonlinear Integrals can lessen loss of information due to the intersection of different classes on real axis. Accuracy will also be increased accordingly. / Wang, Jinfeng. / Advisers: Kwong Sak Leung; Kin Hong Lee. / Source: Dissertation Abstracts International, Volume: 72-01, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 139-151). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaves 210-216). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaves 210-216). Also available on the Internet.

With growing demands for reliability, availability, safety and cost efficiency in modern machinery, accurate fault diagnosis is becoming of paramount importance so that potential failures can be better managed. Although various methods have been applied to machinery condition monitoring and fault diagnosis, the diagnostic accuracy that can be attained is far from satisfactory. As most machinery faults lead to increases in vibration levels, vibration monitoring has become one of the most basic and widely used methods to detect machinery faults. However, current vibration monitoring methods largely depend on signal processing techniques. This study is based on the recognition that a multi-parameter data fusion approach to diagnostics can produce more accurate results. Fuzzy measures and fuzzy integral data fusion theory can represent the importance of each criterion and express certain interactions among them. This research developed a novel, systematic and effective fuzzy measure and fuzzy integral data fusion approach for machinery fault diagnosis, which comprises feature set selection schema, feature level data fusion schema and decision level data fusion schema for machinery fault diagnosis. Different feature selection and fault diagnostic models were derived from these schemas. Two fuzzy measures and two fuzzy integrals were employed: the 2-additive fuzzy measure, the fuzzy measure, the Choquet fuzzy integral and the Sugeno fuzzy integral respectively. The models were validated using rolling element bearing and electrical motor experiments. Different features extracted from vibration signals were used to validate the rolling element bearing feature set selection and fault diagnostic models, while features obtained from both vibration and current signals were employed to assess electrical motor fault diagnostic models. The results show that the proposed schemas and models perform very well in selecting feature set and can improve accuracy in diagnosing both the rolling element bearing and electrical motor faults.

Orientadores: Laécio Carvalho de Barros, Barnabas Bede / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática, Estatística e Computação Científica / Made available in DSpace on 2018-08-24T00:25:40Z (GMT). No. of bitstreams: 1 Gomes_LucianaTakata_D.pdf: 2706174 bytes, checksum: bb5833b7b2c8f5e094b30eefd64e7426 (MD5) Previous issue date: 2014 / Resumo: A partir da proposta das definições de derivada e integral fuzzy via extensão de Zadeh dos respectivos operadores para funções clássicas, obtemos uma versão do teorema fundamental do cálculo e desenvolvemos uma nova teoria de equações diferenciais fuzzy (EDFs). Diferentemente dos conceitos anteriores de derivadas (Hukuhara e generalizadas) e integrais para funções fuzzy, em que as funções assumem valores em conjuntos fuzzy, a abordagem aqui proposta lida com tubos fuzzy de funções (subconjuntos fuzzy de espaços de funções). Sob condições razoáveis, as novas operações equivalem a diferenciar (ou integrar) as funções clássicas dos níveis. Apresentamos as abordagens anteriores de EDFs mais conhecidas e, para realizar comparações com a nova teoria, calculamos os conjuntos atingíveis fuzzy das soluções. Provamos que algumas soluções da teoria proposta equivalem às via derivada fortemente generalizada. Também demonstramos a equivalência, sob determinadas condições, com as soluções via inclusões diferenciais fuzzy e extensão de Zadeh da solução clássica. Apesar destas duas abordagens não tratarem de EDFs, elas são largamente difundidas por utilizarem derivadas de funções clássicas (de modo similar ao aqui proposto) e de preservarem características das soluções de sistemas dinâmicos clássicos. Esses são fatos vantajosos, pois mostram que a teoria proposta, além de tratar de EDFs, possui propriedades desejáveis das outras duas mencionadas, permitindo a ocorrência de estabilidade e periodicidade de soluções, por exemplo. A teoria é ilustrada através de sua aplicação em modelos biológicos e análise dos resultados / Abstract: From the definition of fuzzy derivative and integral via Zadeh's extension of the derivative and integral for classical functions we obtain a fundamental theorem of calculus and develop a new theory for fuzzy differential equations (FDEs). Different from the previous concepts of fuzzy derivatives (Hukuhara and generalized derivatives) and integrals, defined for fuzzy-set-valued functions, the approach we propose deals with fuzzy bunches of functions (fuzzy subsets of spaces of functions). Under reasonable conditions, the new operations are equivalent to differentiating (or integrating) the classical functions of the levels. We present the most known previous approaches of FDEs. Comparisons with the new theory we propose are carried out calculating fuzzy attainable sets of the solutions. Under certain conditions, the solutions via strongly generalized derivative coincide with solutions using our approach. The same happens with solutions to fuzzy differential inclusions and Zadeh's extension of the crisp solution. Although these two methods do not treat FDEs, they are widespread for making use of classical functions (similarly to what is proposed in this thesis) and for preserving properties of classical dynamical systems. These are advantageous features since it shows that the new theory presents desirable properties of the other two mentioned theories (allowing for instance periodicity and stability of solutions), besides treating FDEs. The theory is illustrated by applying it on biological models and commenting the results / Doutorado / Matematica Aplicada / Doutora em Matemática Aplicada