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Fuzzy associative matrix

About: Fuzzy associative matrix is a research topic. Over the lifetime, 8027 publications have been published within this topic receiving 194790 citations.


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Journal ArticleDOI
TL;DR: Quantum information processing in neural structures results in an exponential increase of patterns storage capacity and can explain the extensive memorization and inferencing capabilities of humans.

43 citations

Proceedings ArticleDOI
08 Mar 1992
TL;DR: The authors introduce the concept of distributed representation of fuzzy rules and applies it to classification problems and proposes a fuzzy inference method using the generated fuzzy rules.
Abstract: The authors introduce the concept of distributed representation of fuzzy rules and apply it to classification problems. Distributed representation is implemented by superimposing many fuzzy rules corresponding to different fuzzy partitions of a pattern space. This means that many fuzzy rule tables are simultaneously employed, corresponding to different fuzzy partitions in fuzzy inference. To apply distributed representation of fuzzy rules to pattern classification problems, the authors first propose an algorithm to generate fuzzy rules from numerical data. Next they propose a fuzzy inference method using the generated fuzzy rules. The classification power of distributed representation was compared with that of ordinary fuzzy rules which can be viewed as a local representation. >

43 citations

Journal ArticleDOI
TL;DR: A methodology of stability analysis is proposed incorporating the use of the the HCTFS, providing the reader with another option of hierarchical fuzzy controller design upon stability concerns, and to verify and conclude the proposal, a mathematical example and simulations are provided.
Abstract: A new type of hierarchical fuzzy system (HFS), namely, hierarchical classifying-type fuzzy system (HCTFS), is developed and proposed in the paper. While the HCTFS enjoys the full benefits of a traditional HFS, one of which is to suppress the effects of the unwanted phenomenon, "the curse of dimensionality," it also offers one great advantage that all rule strengths are preserved when passing through subsystem layers. To demonstrate the potential of the HCTFS, computational complexity analysis will be conducted on the complete rule-base models of a conventional fuzzy system and the HCTFS. Furthermore, a methodology of stability analysis is proposed incorporating the use of the the HCTFS, providing the reader with another option of hierarchical fuzzy controller design upon stability concerns. To verify and conclude our proposal, a mathematical example and simulations are provided. In our simulated example, the the HCTFS controller incorporating the proposed stability analysis technique are applied to the active suspension system. The results obtained from the active suspension system are then discussed and compared with the results of the ideal and passive suspension systems.

43 citations

01 Jan 1999
TL;DR: The authors attempt to reduce the growth of the fuzzy rulebase by proposing a disjunctive form for fuzzy rules, but show that this approach is mathematically invalid, as illustrated by an example.
Abstract: The "curse of dimensionality" is one of the key problems facing fuzzy systems theory today. Fuzzy systems are based on a set of IF-THEN rules and the structure of these fuzzy rules causes an exponential growth in the number of rules when more inputs are added, resulting in unwieldy rulebases. Many strategies have been proposed to alleviate the curse of dimensionality. In (1), the authors attempt to reduce the growth of the fuzzy rulebase by proposing a disjunctive form for fuzzy rules. In the current paper, we comment on this approach and show that it is mathematically invalid, as illustrated by an example.

43 citations

Journal ArticleDOI
TL;DR: The results indicate that fuzzy segmentation‐based methods produce acceptable thematic accuracy and could represent a viable alternative to current crisp image segmentation approaches.
Abstract: This Letter proposes an object-based image classification procedure which is based on fuzzy image-regions instead of crisp image-objects. The approach has three stages: (a) fuzzification in which fuzzy image-regions are developed, resulting in a set of images whose digital values express the degree of membership of each pixel to target land-cover classes; (b) feature analysis in which contextual properties of fuzzy image-regions are quantified; and (c) defuzzification in which fuzzy image-regions are allocated to target land-cover classes. The proposed procedure is implemented using automated statistical techniques that require very little user interaction. The results indicate that fuzzy segmentation-based methods produce acceptable thematic accuracy and could represent a viable alternative to current crisp image segmentation approaches.

43 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20238
202216
20212
20201
20193
201825