951 resultados para Fuzzy probabilistic preference relation
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The study of limnology is important to understand ecosystem dynamics and the ecological basis for fish production in the Lake Victoria which is important for fisheries resources use, planning and management. Physical, chemical and biological parameters are important and known to influence fish population production. Energy fixed by primary producers, e.g. algae, is transfered to higher trophic levels, e.g fish. Factors which influence the dynamics of phytoplankton and zooplankton population, e.g nutrient availability and uptake, growth rate, species composition and biomass, ultimately affect fish production. The commercial fisheries of Lake Victoria consists mainly of piscivorous Lates niloticus (L>), algivorous Oreochromis niloticus (L.) and zooplanktivorous Rastrineobola argentea (Pellegrin)
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Minced fish is a significant component of a number of frozen fishery products like fish fingers, cakes and patties. Predominately minced fish is produced from gadoid species (Alaska pollack, cod, saithe, hake and others) possessing the enzyme trimethylamine oxide demethylase (TMAOase, E.C. 4.1.2.32) (Rehbein and Schreiber 1984). TMAOase catalyses the degradation of trimethylamine oxide (TMAO) to formaldehyde (FA) and dimethylamine (DMA), preferentially during frozen storage of products (Hultin 1992). In most gadoid species light muscle contains only low activity of TMAOase, the activity of red muscle and bellyflaps being somewhat higher. In contrast, the TMAOase activity in blood, kidney and other tissues, residues of which may contaminate minced fish flesh, may be higher for several orders of magnitude (Rehbein and Schreiber 1984).
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In this work, the development of a probabilistic approach to robust control is motivated by structural control applications in civil engineering. Often in civil structural applications, a system's performance is specified in terms of its reliability. In addition, the model and input uncertainty for the system may be described most appropriately using probabilistic or "soft" bounds on the model and input sets. The probabilistic robust control methodology contrasts with existing H∞/μ robust control methodologies that do not use probability information for the model and input uncertainty sets, yielding only the guaranteed (i.e., "worst-case") system performance, and no information about the system's probable performance which would be of interest to civil engineers.
The design objective for the probabilistic robust controller is to maximize the reliability of the uncertain structure/controller system for a probabilistically-described uncertain excitation. The robust performance is computed for a set of possible models by weighting the conditional performance probability for a particular model by the probability of that model, then integrating over the set of possible models. This integration is accomplished efficiently using an asymptotic approximation. The probable performance can be optimized numerically over the class of allowable controllers to find the optimal controller. Also, if structural response data becomes available from a controlled structure, its probable performance can easily be updated using Bayes's Theorem to update the probability distribution over the set of possible models. An updated optimal controller can then be produced, if desired, by following the original procedure. Thus, the probabilistic framework integrates system identification and robust control in a natural manner.
The probabilistic robust control methodology is applied to two systems in this thesis. The first is a high-fidelity computer model of a benchmark structural control laboratory experiment. For this application, uncertainty in the input model only is considered. The probabilistic control design minimizes the failure probability of the benchmark system while remaining robust with respect to the input model uncertainty. The performance of an optimal low-order controller compares favorably with higher-order controllers for the same benchmark system which are based on other approaches. The second application is to the Caltech Flexible Structure, which is a light-weight aluminum truss structure actuated by three voice coil actuators. A controller is designed to minimize the failure probability for a nominal model of this system. Furthermore, the method for updating the model-based performance calculation given new response data from the system is illustrated.
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In a probabilistic assessment of the performance of structures subjected to uncertain environmental loads such as earthquakes, an important problem is to determine the probability that the structural response exceeds some specified limits within a given duration of interest. This problem is known as the first excursion problem, and it has been a challenging problem in the theory of stochastic dynamics and reliability analysis. In spite of the enormous amount of attention the problem has received, there is no procedure available for its general solution, especially for engineering problems of interest where the complexity of the system is large and the failure probability is small.
The application of simulation methods to solving the first excursion problem is investigated in this dissertation, with the objective of assessing the probabilistic performance of structures subjected to uncertain earthquake excitations modeled by stochastic processes. From a simulation perspective, the major difficulty in the first excursion problem comes from the large number of uncertain parameters often encountered in the stochastic description of the excitation. Existing simulation tools are examined, with special regard to their applicability in problems with a large number of uncertain parameters. Two efficient simulation methods are developed to solve the first excursion problem. The first method is developed specifically for linear dynamical systems, and it is found to be extremely efficient compared to existing techniques. The second method is more robust to the type of problem, and it is applicable to general dynamical systems. It is efficient for estimating small failure probabilities because the computational effort grows at a much slower rate with decreasing failure probability than standard Monte Carlo simulation. The simulation methods are applied to assess the probabilistic performance of structures subjected to uncertain earthquake excitation. Failure analysis is also carried out using the samples generated during simulation, which provide insight into the probable scenarios that will occur given that a structure fails.
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In this work, computationally efficient approximate methods are developed for analyzing uncertain dynamical systems. Uncertainties in both the excitation and the modeling are considered and examples are presented illustrating the accuracy of the proposed approximations.
For nonlinear systems under uncertain excitation, methods are developed to approximate the stationary probability density function and statistical quantities of interest. The methods are based on approximating solutions to the Fokker-Planck equation for the system and differ from traditional methods in which approximate solutions to stochastic differential equations are found. The new methods require little computational effort and examples are presented for which the accuracy of the proposed approximations compare favorably to results obtained by existing methods. The most significant improvements are made in approximating quantities related to the extreme values of the response, such as expected outcrossing rates, which are crucial for evaluating the reliability of the system.
Laplace's method of asymptotic approximation is applied to approximate the probability integrals which arise when analyzing systems with modeling uncertainty. The asymptotic approximation reduces the problem of evaluating a multidimensional integral to solving a minimization problem and the results become asymptotically exact as the uncertainty in the modeling goes to zero. The method is found to provide good approximations for the moments and outcrossing rates for systems with uncertain parameters under stochastic excitation, even when there is a large amount of uncertainty in the parameters. The method is also applied to classical reliability integrals, providing approximations in both the transformed (independently, normally distributed) variables and the original variables. In the transformed variables, the asymptotic approximation yields a very simple formula for approximating the value of SORM integrals. In many cases, it may be computationally expensive to transform the variables, and an approximation is also developed in the original variables. Examples are presented illustrating the accuracy of the approximations and results are compared with existing approximations.
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A Bayesian probabilistic methodology for on-line structural health monitoring which addresses the issue of parameter uncertainty inherent in problem is presented. The method uses modal parameters for a limited number of modes identified from measurements taken at a restricted number of degrees of freedom of a structure as the measured structural data. The application presented uses a linear structural model whose stiffness matrix is parameterized to develop a class of possible models. Within the Bayesian framework, a joint probability density function (PDF) for the model stiffness parameters given the measured modal data is determined. Using this PDF, the marginal PDF of the stiffness parameter for each substructure given the data can be calculated.
Monitoring the health of a structure using these marginal PDFs involves two steps. First, the marginal PDF for each model parameter given modal data from the undamaged structure is found. The structure is then periodically monitored and updated marginal PDFs are determined. A measure of the difference between the calibrated and current marginal PDFs is used as a means to characterize the health of the structure. A procedure for interpreting the measure for use by an expert system in on-line monitoring is also introduced.
The probabilistic framework is developed in order to address the model parameter uncertainty issue inherent in the health monitoring problem. To illustrate this issue, consider a very simplified deterministic structural health monitoring method. In such an approach, the model parameters which minimize an error measure between the measured and model modal values would be used as the "best" model of the structure. Changes between the model parameters identified using modal data from the undamaged structure and subsequent modal data would be used to find the existence, location and degree of damage. Due to measurement noise, limited modal information, and model error, the "best" model parameters might vary from one modal dataset to the next without any damage present in the structure. Thus, difficulties would arise in separating normal variations in the identified model parameters based on limitations of the identification method and variations due to true change in the structure. The Bayesian framework described in this work provides a means to handle this parametric uncertainty.
The probabilistic health monitoring method is applied to simulated data and laboratory data. The results of these tests are presented.
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We examine voting situations in which individuals have incomplete information over each others' true preferences. In many respects, this work is motivated by a desire to provide a more complete understanding of so-called probabilistic voting.
Chapter 2 examines the similarities and differences between the incentives faced by politicians who seek to maximize expected vote share, expected plurality, or probability of victory in single member: single vote, simple plurality electoral systems. We find that, in general, the candidates' optimal policies in such an electoral system vary greatly depending on their objective function. We provide several examples, as well as a genericity result which states that almost all such electoral systems (with respect to the distributions of voter behavior) will exhibit different incentives for candidates who seek to maximize expected vote share and those who seek to maximize probability of victory.
In Chapter 3, we adopt a random utility maximizing framework in which individuals' preferences are subject to action-specific exogenous shocks. We show that Nash equilibria exist in voting games possessing such an information structure and in which voters and candidates are each aware that every voter's preferences are subject to such shocks. A special case of our framework is that in which voters are playing a Quantal Response Equilibrium (McKelvey and Palfrey (1995), (1998)). We then examine candidate competition in such games and show that, for sufficiently large electorates, regardless of the dimensionality of the policy space or the number of candidates, there exists a strict equilibrium at the social welfare optimum (i.e., the point which maximizes the sum of voters' utility functions). In two candidate contests we find that this equilibrium is unique.
Finally, in Chapter 4, we attempt the first steps towards a theory of equilibrium in games possessing both continuous action spaces and action-specific preference shocks. Our notion of equilibrium, Variational Response Equilibrium, is shown to exist in all games with continuous payoff functions. We discuss the similarities and differences between this notion of equilibrium and the notion of Quantal Response Equilibrium and offer possible extensions of our framework.
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This article is an attempt to devise a method of using certain species of Corixidae as a basis for the assessment of general water quality in lakes. An empirical graphical representation of the distribution of populations or communities of Corixidae in relation to conductivity, based mainly on English and Welsh lakes, is used as a predictive monitoring model to establish the "expected" normal community at a given conductivity, representing the total ionic concentration of the water body. A test sample from another lake of known conductivity is then compared with "expected" community. The "goodness of fit" is examined visually or by calculation of indices of similarity based on the relative proportions of the constituent species of each community. A computer programme has been devised for this purpose.
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Complexity in the earthquake rupture process can result from many factors. This study investigates the origin of such complexity by examining several recent, large earthquakes in detail. In each case the local tectonic environment plays an important role in understanding the source of the complexity.
Several large shallow earthquakes (Ms > 7.0) along the Middle American Trench have similarities and differences between them that may lead to a better understanding of fracture and subduction processes. They are predominantly thrust events consistent with the known subduction of the Cocos plate beneath N. America. Two events occurring along this subduction zone close to triple junctions show considerable complexity. This may be attributable to a more heterogeneous stress environment in these regions and as such has implications for other subduction zone boundaries.
An event which looks complex but is actually rather simple is the 1978 Bermuda earthquake (Ms ~ 6). It is located predominantly in the mantle. Its mechanism is one of pure thrust faulting with a strike N 20°W and dip 42°NE. Its apparent complexity is caused by local crustal structure. This is an important event in terms of understanding and estimating seismic hazard on the eastern seaboard of N. America.
A study of several large strike-slip continental earthquakes identifies characteristics which are common to them and may be useful in determining what to expect from the next great earthquake on the San Andreas fault. The events are the 1976 Guatemala earthquake on the Motagua fault and two events on the Anatolian fault in Turkey (the 1967, Mudurnu Valley and 1976, E. Turkey events). An attempt to model the complex P-waveforms of these events results in good synthetic fits for the Guatemala and Mudurnu Valley events. However, the E. Turkey event proves to be too complex as it may have associated thrust or normal faulting. Several individual sources occurring at intervals of between 5 and 20 seconds characterize the Guatemala and Mudurnu Valley events. The maximum size of an individual source appears to be bounded at about 5 x 1026 dyne-cm. A detailed source study including directivity is performed on the Guatemala event. The source time history of the Mudurnu Valley event illustrates its significance in modeling strong ground motion in the near field. The complex source time series of the 1967 event produces amplitudes greater by a factor of 2.5 than a uniform model scaled to the same size for a station 20 km from the fault.
Three large and important earthquakes demonstrate an important type of complexity --- multiple-fault complexity. The first, the 1976 Philippine earthquake, an oblique thrust event, represents the first seismological evidence for a northeast dipping subduction zone beneath the island of Mindanao. A large event, following the mainshock by 12 hours, occurred outside the aftershock area and apparently resulted from motion on a subsidiary fault since the event had a strike-slip mechanism.
An aftershock of the great 1960 Chilean earthquake on June 6, 1960, proved to be an interesting discovery. It appears to be a large strike-slip event at the main rupture's southern boundary. It most likely occurred on the landward extension of the Chile Rise transform fault, in the subducting plate. The results for this event suggest that a small event triggered a series of slow events; the duration of the whole sequence being longer than 1 hour. This is indeed a "slow earthquake".
Perhaps one of the most complex of events is the recent Tangshan, China event. It began as a large strike-slip event. Within several seconds of the mainshock it may have triggered thrust faulting to the south of the epicenter. There is no doubt, however, that it triggered a large oblique normal event to the northeast, 15 hours after the mainshock. This event certainly contributed to the great loss of life-sustained as a result of the Tangshan earthquake sequence.
What has been learned from these studies has been applied to predict what one might expect from the next great earthquake on the San Andreas. The expectation from this study is that such an event would be a large complex event, not unlike, but perhaps larger than, the Guatemala or Mudurnu Valley events. That is to say, it will most likely consist of a series of individual events in sequence. It is also quite possible that the event could trigger associated faulting on neighboring fault systems such as those occurring in the Transverse Ranges. This has important bearing on the earthquake hazard estimation for the region.
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Catchability and activity of Perca fluviatilis in relation to temperature is examined. The number of fish caught and water temperature during the 3 summer months was used the assess the numbers of hours of activity of perch. Parallel to the research on activity, large-scale marking was carried out to establish the periods of growth during the year.