Displaying similar documents to “Decomposition of the fuzzy inference system for implementation in the FPGA structure”

An architecture for making judgments using computing with words

Jerry Mendel (2002)

International Journal of Applied Mathematics and Computer Science

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Our thesis is that computing with words needs to account for the uncertainties associated with the meanings of words, and that these uncertainties require using type-2 fuzzy sets. Doing this leads to a proposed architecture for making it judgments by means of computing with words, i.e., to a perceptual computer-the Per-C. The Per-C includes an encoder, a type-2 rule-based fuzzy logic system, and a decoder. It lets all human-computer interactions be performed using words. In this paper,...

Systolic architectures for fuzzy processing and their simulation.

Luis de Salvador, Marcos García, Julio Gutiérrez (1994)

Mathware and Soft Computing

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This paper details the study of systolic architectures for fuzzy rules processing made at the Hardware and Advanced Control Laboratory - INTA. The theoretical basis of these architectures is described and analysed. Likewise, the resultant schematics are simulated using a hardware description language (VHDL) with standard cells from ES2. This gives us a very accurate assessment of their real performance. In this way we can detect the inherent shortcomings in this class of systems and...

Interpretability of linguistic variables: a formal account

Ulrich Bodenhofer, Peter Bauer (2005)

Kybernetika

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This contribution is concerned with the interpretability of fuzzy rule-based systems. While this property is widely considered to be a crucial one in fuzzy rule-based modeling, a more detailed formal investigation of what “interpretability” actually means is not available. So far, interpretability has most often been associated with rather heuristic assumptions about shape and mutual overlapping of fuzzy membership functions. In this paper, we attempt to approach this problem from a...

On classification with missing data using rough-neuro-fuzzy systems

Robert K. Nowicki (2010)

International Journal of Applied Mathematics and Computer Science

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The paper presents a new approach to fuzzy classification in the case of missing data. Rough-fuzzy sets are incorporated into logical type neuro-fuzzy structures and a rough-neuro-fuzzy classifier is derived. Theorems which allow determining the structure of the rough-neuro-fuzzy classifier are given. Several experiments illustrating the performance of the roughneuro-fuzzy classifier working in the case of missing features are described.