Bernd Moeller - Böcker

Bernd Moellers Reichsstadt und Reformation (1962) gehört zu den klassischen Texten der protestantischen Kirchengeschichte des 20. Jahrhunderts. Die Studie gilt als Initial einer sozialhistorischen Reformationsgeschichtsschreibung und hat die wissenschaftliche Diskussion bis in die 1980er Jahre wesentlich stimuliert und begleitet. Moeller untersucht den Zusammenhang zwischen städtischen Rechtsvorstellungen und Mentalitäten des späten Mittelalters und theologischen Leitvorstellungen der oberdeutschen und der schweizerischen Reformation und arbeitet bemerkenswerte Affinitäten heraus. Von diesen Analysen ergeben sich typologische Perspektiven zur Unterscheidung zwischen lutherischer und oberdeutsch-reformierter Reformation, die die weitere Diskussion bestimmt haben. In einer Bilanz der Forschungsdebatte hat Moeller 1987 die lebhaften Kontroversen diskutiert.Dem Buch ist ein Essay vorangestellt, in dem Thomas Kaufmann die wissenschaftsgeschichtliche und werkbiographische Bedeutung von "Reichsstadt und Reformation" aus der Retrospektive eines weiteren Vierteljahrhunderts seit Erscheinen der zweiten Ausgabe reflektiert.

Forecasting is fascinating. Who wouldn’t like to cast a glimpse into the future? Far removed from metaphysics, mathematical methods such as time-lapse techniques, time series or arti?cial neural netwoks o?er a rational means of achieving this. A precondition for the latter is the availability of a sequence of observed values from the past whose temporal classi?cation permits the deduction of attributes necessary for forecasting purposes. The subject matter of this book is uncertain forecasting using time series and neural networks based on uncertain observed data. ‘Uncertain’ data - plies information exhibiting inaccuracy, uncertainty and questionability. The uncertainty of individual observations is modeled in this book by fuzziness. Sequences of uncertain observations hence constitute fuzzy time series. By means of new discretization techniques for uncertain data it is now possible to correctly and completely retain data uncertainty in forecasting work. The book presents numerical methods which permit successful forecasting not only in engineering but also in many other ?elds such as environmental science or economics, assuming of course that a suitable sequence of observed data is available. By taking account of data uncertainty, the indiscriminate reduction of uncertain observations to real numbers is avoided. The larger information content described by uncertainty is retained, and compared with real data, provides a deeper insight into causal relationships. This in turn has practical consequences as far as the full?lment of technical requirements in engineering applications is concerned.

The subject of the book is the comprehensive consideration of uncertainty in the numerical analysis, the safety assessment, and the design of structures. Stochastic as well as non-stochastic uncertainty is treated on the basis of the superordinated uncertainty model fuzzy randomness. This new uncertainty model contains the special cases of real valued random variables and fuzzy variables and permits to take account of both uncertainty characteristics simultaneously. The book introduces to the problem of uncertainty and provides a current survey of relevant uncertainty models and their application in civil engineering. The necessary, special mathematical basics of the fuzzy set theory and the theory of fuzzy random variables are explained in an engineering manner and illustrated by way of examples. Basic ideas and methods for appropriately quantifying uncertain structural parameters are presented and demonstrated by means of characteristic examples.For processing uncertainty in structural analysis, safety assessment, and structural design completely new algorithms are introduced and described in detail as fuzzy structural analysis, fuzzy probabilistic safety assessment, and fuzzy cluster design. The application of the new methods is demonstrated for selected examples from civil engineering, their essential advantages are emphasized. For the first time this represents a coherent, overall concept for considering uncertainty in civil engineering. The book in particular addresses to civil engineers and requires a university degree as well as basic knowledge in stochastics. But also for mechanical engineers, colleagues from applied mathematics, and other people who are interested in uncertainty problems the book represents a suitable introduction to the problem of uncertainty modeling and provides general solutions and algorithms, which may also be applied to problems from other fields beyond engineering.

Forecasting is fascinating. Who wouldn’t like to cast a glimpse into the future? Far removed from metaphysics, mathematical methods such as time-lapse techniques, time series or arti?cial neural netwoks o?er a rational means of achieving this. A precondition for the latter is the availability of a sequence of observed values from the past whose temporal classi?cation permits the deduction of attributes necessary for forecasting purposes. The subject matter of this book is uncertain forecasting using time series and neural networks based on uncertain observed data. ‘Uncertain’ data - plies information exhibiting inaccuracy, uncertainty and questionability. The uncertainty of individual observations is modeled in this book by fuzziness. Sequences of uncertain observations hence constitute fuzzy time series. By means of new discretization techniques for uncertain data it is now possible to correctly and completely retain data uncertainty in forecasting work. The book presents numerical methods which permit successful forecasting not only in engineering but also in many other ?elds such as environmental science or economics, assuming of course that a suitable sequence of observed data is available. By taking account of data uncertainty, the indiscriminate reduction of uncertain observations to real numbers is avoided. The larger information content described by uncertainty is retained, and compared with real data, provides a deeper insight into causal relationships. This in turn has practical consequences as far as the full?lment of technical requirements in engineering applications is concerned.

The subject of the book is the comprehensive consideration of uncertainty in the numerical analysis, the safety assessment, and the design of structures. Stochastic as well as non-stochastic uncertainty is treated on the basis of the superordinated uncertainty model fuzzy randomness. This new uncertainty model contains the special cases of real valued random variables and fuzzy variables and permits to take account of both uncertainty characteristics simultaneously. The book introduces to the problem of uncertainty and provides a current survey of relevant uncertainty models and their application in civil engineering. The necessary, special mathematical basics of the fuzzy set theory and the theory of fuzzy random variables are explained in an engineering manner and illustrated by way of examples. Basic ideas and methods for appropriately quantifying uncertain structural parameters are presented and demonstrated by means of characteristic examples.For processing uncertainty in structural analysis, safety assessment, and structural design completely new algorithms are introduced and described in detail as fuzzy structural analysis, fuzzy probabilistic safety assessment, and fuzzy cluster design. The application of the new methods is demonstrated for selected examples from civil engineering, their essential advantages are emphasized. For the first time this represents a coherent, overall concept for considering uncertainty in civil engineering. The book in particular addresses to civil engineers and requires a university degree as well as basic knowledge in stochastics. But also for mechanical engineers, colleagues from applied mathematics, and other people who are interested in uncertainty problems the book represents a suitable introduction to the problem of uncertainty modeling and provides general solutions and algorithms, which may also be applied to problems from other fields beyond engineering.