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Munchen, Germany

The Munich University of Applied science, ) was founded in 1971 and is the largest University of Applied science in Bavaria with about 14,000 students. Wikipedia.


Peters G.,Munich University of Applied Sciences
IEEE Transactions on Fuzzy Systems | Year: 2011

Granular computing (GrC) has gained increasing attention in the past decade. Although not uniquely defined, its basic idea is to approximate detailed machine-like information by a coarser presentation on a human-like level. Within granular computing, the mapping of continuous variables into intervals plays an important role. These intervals are often prerequisites for the formulation of linguistic variables. In this paper, we suggest a piecewise interval approximation and propose granular box regression. Its objective is to establish relationships between independent and dependent variables by multidimensional boxes. We interpret granular box regression as interval regression and show its potential for the extraction of fuzzy rules from data. In two experiments, we apply granular box regression to an artificial as well as to a real dataset in the field of finance and evaluate its properties. © 2011 IEEE. Source


Lachenmaier S.,Ludwig Maximilians University of Munich | Rottmann H.,Munich University of Applied Sciences
International Journal of Industrial Organization | Year: 2011

This paper estimates the effect of innovation on employment at the firm level. Our uniquely long innovation panel data set of German manufacturing firms covers more than 20 years and allows us to use various innovation measures. We can distinguish between product and process innovations as well as between innovation input and innovation output measures. Using dynamic panel GMM system estimation we find positive effects of innovation on employment. This is true for innovation input as well as for innovation output variables. Innovations show their positive effect on employment with a time lag and process innovations have higher effects than product innovations. © 2010 Elsevier B.V. All rights reserved. Source


Schmidbauer H.,Istanbul Bilgi University | Rosch A.,Munich University of Applied Sciences
Energy Economics | Year: 2012

Several times a year, OPEC hosts conferences among its members to agree on further oil production policies. Prior to OPEC conferences, there is usually rampant speculation about which decision concerning world oil production levels (no change, increase, or cut) will be announced. The purpose of our investigation is to assess the impact of OPEC announcements on expectation and volatility of daily oil price changes (returns).We modify dummy variables indicating the day of an OPEC announcement to reflect a certain pattern of impact on return expectation and volatility. A combination of regression and GARCH models can then differentiate between pre- and post-announcement effects, and distinguish between the three kinds of OPEC decisions. We find evidence for a post-announcement effect on expectation, which is negative in the case of a cut decision and positive in case of an increase or maintain decision, while there is a positive pre-announcement effect on volatility, which is strongest in the case of a cut decision. © 2012 Elsevier B.V. Source


Peters G.,Munich University of Applied Sciences | Peters G.,Australian Catholic University
Information Sciences | Year: 2014

Clustering is one of the most widely used method in data mining with applications in virtually any domain. Its main objective is to group similar objects into the same cluster, while dissimilar objects should belong to different clusters. In particular k-means clustering, as member of the partitioning clustering family, has obtained great popularity. The classic (hard) k-means assigns an object unambiguously to one and only one cluster. To address uncertainty soft clustering has been introduced using concepts like fuzziness, possibility or roughness. A decade ago Lingras and West introduced a k-means approach based on the interval interpretation of rough sets theory. In the past years their rough k-means has gained increasing attention. In our paper, we propose a refined rough k-means algorithm that utilizes Laplace's principle of indifference to calculate the means. As we will discuss this provides a sounder justification for the impacts of the objects in the approximations in comparison to established rough k-means algorithms. Furthermore, the weighting in the mean function is based on individual objects rather than on aggregated sub-means. In experiments, we compare the refined algorithm to related approaches. © 2014 Elsevier Inc. All rights reserved. Source


Klug F.,Munich University of Applied Sciences
International Journal of Production Research | Year: 2013

The paper describes variance amplification of orders in a car manufacturing context. Demand fluctuation in inter-company supply chains has been well described in literature over many years. The specific contribution of this paper is to examine the cyclical boom-and-bust behaviour, commonly known as the bullwhip effect, inside the plant boundaries of a single company with the help of system dynamics modelling. To gain insight into this phenomenon we will first perform an extensive literature review on the subject of factory relevant internal bullwhip. Based on these results we illustrate through a real-life car assembly operation the existence of internal bullwhip. Finally we conclude with a discussion of our results and future research opportunities. © 2013 Copyright Taylor and Francis Group, LLC. Source

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