Catálogo de publicaciones - libros

Exploring the growing interest in extending the theory of probability and statistics to allow for more flexible modeling of uncertainty, ignorance, and fuzziness, the properties of fuzzy modeling are investigated for statistical signals, which benefit from the properties of fuzzy modeling. There is relatively research in the area, making explicit identification of statistical/stochastic fuzzy modeling properties, where statistical/stochastic signals are in play. This research makes explicit comparative investigations and positions fuzzy modeling in the statistical signal processing domain, next to nonlinear dynamic system modeling.

Based on the concept of fuzzy random variables, we propose an optimal representative value for fuzzy quality data by means of a combination of a random variable with a measure of fuzziness. Applying the classical Cumulative Sum (CUSUM) chart for these representative values, an univariate CUSUM control chart concerning -fuzzy data under independent observations is constructed.

Automatic segmentation of text strings, in particular entity names, into structured records is often needed for efficient information retrieval, analysis, mining, and integration. Hidden Markov Model (HMM) has been shown as the state of the art for this task. However, previous work did not take into account the synonymy of words and their abbreviations, or possibility of their misspelling. In this paper, we propose a fuzzy synset-based HMM for text segmentation, based on a semantic relation and an edit distance between words. The model is also to deal with texts written in a language like Vietnamese, where a meaningful word can be composed of more than one syllable. Experiments on Vietnamese company names are presented to demonstrate the performance of the model.

We present an example how fuzzy measures and discrete Choquet integrals can be used to model interactivities between trees within a stochastic fine root dispersal model.

We present a method for evaluating the discriminative power of compact feature combinations (blocks) using the distance-based scoring measure, yielding an algorithm for selecting feature blocks that significantly contribute to the outcome variation. To estimate classification performance with subset selection in a high dimensional framework we jointly evaluate both stages of the process: selection of significantly relevant blocks and classification. Classification power and performance properties of the classifier with the proposed subset selection technique has been studied on several simulation models and confirms the benefit of this approach.

interval random variables, queueing, long–tailed distributions, Laplace transform

Many complex systems have characteristics which vary over time. Consider for example, the problem of modelling a river as the seasons change or adjusting the setup of a machine as it ages, to enable it to stay within predefined tolerances. In such cases offline learning limits the capability of an algorithm to accurately capture a dynamic system, since it can only base predictions on events that were encountered during the learning process. Model updating is therefore required to allow the model to change over time and to adapt to previously unseen events. In the sequel we introduce an extended version of the fuzzy Bayesian prediction algorithm [6] which learns models incorporating both uncertainty and fuzziness. This extension allows an initial model to be updated as new data becomes available. The potential of this approach will be demonstrated on a real-time flood prediction problem for the River Severn in the UK.