2,979 publications from this institution
This study is devoted to the general identification methodology for fuzzy models. In contrast to numeric models, fuzzy models operate at a level of information granules — fuzzy sets and this brings up an important design requirement of transparency of the model. We propose a three-phase development framework by distinguishing between structural and parametric optimization processes. The underlying topology of the model dwells on fuzzy neural networks — architectures governed by fuzzy logic and equipped with parametric flexibility. Two general optimization mechanisms are explored: the structural optimization is realized via Genetic Programming whereas for the ensuing detailed parametric optimization we proceed with gradient-based learning. The main advantages of this approach are discussed in detail. The study is illustrated with the aid of a numeric example that provides a detailed insight into the performance of the fuzzy models.
With enormous amounts of data come opportunities of building models of real-world systems that are instrumental in realizing a plethora of control, prediction, and classification tasks. The interpretability facet of ensuing models becomes highly relevant in light of designing autonomous systems and those constructs supporting human-centric decision-making environments. To transform data to tangible and actionable pieces of knowledge and formulate a problem at hand at a suitable level of abstraction, a convenient way to proceed is to position the problem in the environment of Granular Computing.We advocate that a systematic way of capturing knowledge residing within acquired data and encapsulating such knowledge in the form of interpretable models is supported by a suitable level of abstraction at which the data are to be represented. In turn, we show that an abstraction mechanism is conveniently realized in the form of information granules. Information granules and Granular Computing delivers an operational and flexible setting in which granular models are built and analyzed. A formal characterization of information granules is introduced where they are concisely described as triple (G, I, R) capturing their underlying geometry in the data space (G), information content (I), and representation capabilities of the underlying experimental evidence (R).A suite of design methods transforming data into information granules being articulated in various formal settings (e.g., intervals, fuzzy sets, rough sets) is analyzed and an array of generalizations is discussed (including collaborative ways of building granules in the presence of some auxiliary domain knowledge).In the sequel, it is shown how information granules regarded as functional modules are efficiently used in the construction of a vast array of interpretable models, especially rule-based architectures.
Granular computing, as a new and rapidly growing paradigm of information processing, has attracted many researchers and practitioners. Granular computing is an umbrella term to cover any theories, methodologies, techniques, and tools that make use of information granules in complex problem solving. The aim of this paper is to review foundations and schools of research and to elaborate on current developments in granular computing research. We first review some basic notions of granular computing. Classification and descriptions of various schools of research in granular computing are given. We also present and identify some research directions in granular computing.
Classification, especially in the case of a small space of features, is prone to errors. This is more important when it is costly to gain data from samples to calculate the values for futures. We study what effect limiting the space of features has on the performance of built classifiers and how the quality of classification can be improved by rejecting misclassified elements.
