Dynamic Fuzzy Pattern Recognition with Applications to Finance and Engineering

Angstenberger, Larisa

doi:10.1007/978-94-017-1312-2

Cited by 25 publications

(20 citation statements)

References 36 publications

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“…Hence they have to be controlled and, if necessary, adapted to the systems dynamic (Angstenberger 2001 …”

Section: H-j Zimmermannmentioning

confidence: 99%

Knowledge Management, Knowledge Discovery, and Dynamic Intelligent Data Mining

Zimmermann¹

2006

Cybernetics and Systems

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During the last two to three decades, many scientific as well business areas have moved from a situation of a lack of (electronically) readable information into a situation of abundant data. Data warehouses appeared, and the problem of extracting information from large masses of data became more and more important. Also knowledge became a very precious commodity, and its efficient use often makes the difference between success and failure. Finding useful information or patterns in raw data is known in the literature under various names, such as knowledge discovery in data bases, data mining, knowledge extraction, information discovery, information harvesting, data archaeology, etc. Many research areas, such as machine learning, pattern recognition, artificial intelligence, knowledge acquisition for expert systems, data visualization, and others are concerned with these activities, and the terminology used is not unequivocally defined.In this article, we shall first consider the different interpretations of the notions previously mentioned, and we shall then describe in more detail a recent technology that is very useful for data mining as well as for related areas.

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“…Hence they have to be controlled and, if necessary, adapted to the systems dynamic (Angstenberger 2001 …”

Section: H-j Zimmermannmentioning

confidence: 99%

Knowledge Management, Knowledge Discovery, and Dynamic Intelligent Data Mining

Zimmermann¹

2006

Cybernetics and Systems

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“…Since the segmentation process does not lead to a once and for all segmentation ( customers become older, richer or change their position) a repetition of the process may lead to other clusters and to other customers in existing clusters. This dynamic segmentation ( [1]) may also indicate changes in the structure of the customers which otherwise could not be recognized. To avoid misunderstandings it should be mentioned, that the customer segmentation described above is only the core of a larger "Financial Suite" that is used in banks.…”

Section: Segmenting Customers Of Banksmentioning

confidence: 99%

Applications of Fuzzy Technology in Business Intelligence

Meyer¹,

Zimmermann

2011

INT J COMPUT COMMUN

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Fuzzy Set Theory has been developed during the last decades to a demanding mathematical theory. There exist more than 50,000 publications in this area by now. Unluckily the number of reports on applications of fuzzy technology has become very scarce. The reasons for that are manifold: Real applications are normally not single-method-applications but rather complex combinations of different techniques, which are not suited for a publication in a journal. Sometimes considerations of competition my play a role, and sometimes the theoretical core of an application is not suited for publication. In this paper we shall focus on applications of fuzzy technology on real problems in business management. Two versions of fuzzy technology will be used: Fuzzy Knowledge based systems and fuzzy clustering. It is assumed that the reader is familiar with basic fuzzy set theory and the goal of the paper is, to show that the potential of applying fuzzy technology in management is still very large and hardly exploited so far.

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“…Briefly, the basic idea of the fuzzy similarity-based approach is to evaluate the similarity between the test degradation trajectory and the 20 c training P  complete run-to-failure training trajectories, and to use the RULs of these latter to estimate the RUL of the former, considering how similar they are [30], [78], [79]. The similarity is quantified by resorting to the definition of an "approximately zero" fuzzy set taken as a bellshaped function whose parameters can be set by following a trial and error procedure on a validation set of complete run-to-failure trajectories.…”

Section: Comparison With a Data-driven Fuzzy Similarity-based Approachmentioning

confidence: 99%

Remaining useful life estimation in heterogeneous fleets working under variable operating conditions

Al‐Dahidi

Maio

Baraldi

et al. 2016

Reliability Engineering & System Safety

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Abstract. The availability of condition monitoring data for large fleets of similar equipment motivates the development of data-driven prognostic approaches that capitalize on the information contained in such data to estimate equipment Remaining Useful Life (RUL). A main difficulty is that the fleet of equipment typically experiences different operating conditions, which influence both the condition monitoring data and the degradation processes that physically determine the RUL. We propose an approach for RUL estimation from heterogeneous fleet data based on three phases: firstly, the degradation levels (states) of an homogeneous discrete-time finite-state semi-markov model are identified by resorting to an unsupervised ensemble clustering approach. Then, the parameters of the discrete Weibull distributions describing the transitions among the states and their uncertainties are inferred by resorting to the Maximum Likelihood Estimation (MLE) method and to the Fisher Information Matrix (FIM), respectively. Finally, the inferred degradation model is used to estimate the RUL of fleet equipment by direct Monte Carlo (MC) simulation. The proposed approach is applied to two case studies regarding heterogeneous fleets of aluminum electrolytic capacitors and turbofan engines. Results show the effectiveness of the proposed approach in predicting the RUL and its superiority compared to a fuzzy similarity-based approach of literature. Keywords

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Dynamic Fuzzy Pattern Recognition with Applications to Finance and Engineering

Cited by 25 publications

References 36 publications

Knowledge Management, Knowledge Discovery, and Dynamic Intelligent Data Mining

Knowledge Management, Knowledge Discovery, and Dynamic Intelligent Data Mining

Applications of Fuzzy Technology in Business Intelligence

Remaining useful life estimation in heterogeneous fleets working under variable operating conditions

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