Topic
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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01 Jan 2008
TL;DR: This paper presents a meta-modelling perspective on individual-based Ecological Modeling with Mobile Fuzzy Agents for Spatial Dynamics that combines Directional and Topological Relationship Information from 2D Concave Objects with a Similarity-based approach.
Abstract: Reasoning About Regions, Relations, and Fields.- Fuzzy Reasoning about Geographic Regions.- Combined Extraction of Directional and Topological Relationship Information from 2D Concave Objects.- Field Based Methods for the Modeling of Fuzzy Spatial Data.- Modeling Localities with Fuzzy Sets and GIS.- Fuzzy Classification.- Mining Weather Data Using Fuzzy Cluster Analysis.- Modelling the Fuzzy Spatial Extent of Geographical Entities.- Multi-Dimensional Interpolations with Fuzzy Sets.- Talking Space - A Social & Fuzzy Logical GIS Perspective On Modelling Spatial Dynamics.- A Valuation of the Reliability of a GIS Based on the Fuzzy Logic in a Concrete Case Study.- Fuzzy Representations of Landscape Features.- Fuzziness and Ambiguity in Multi-Scale Analysis of Landscape Morphometry.- Fuzzy Representation of Special Terrain Features Using a Similarity-based Approach.- Decision Making with GIS and Fuzzy Sets.- Spatial Decision-Making Using Fuzzy Decision Tables: Theory, Application and Limitations.- Spatial Decision Making Using Fuzzy GIS.- Spatially Explicit Individual-Based Ecological Modeling with Mobile Fuzzy Agents.
119 citations
TL;DR: The fuzzy concept is used to design a two-step color image filter cascading a fuzzy rule-based switching impulse noise filter by a fuzzy average filtering over the same fuzzypeer , which leads to computational savings.
Abstract: The peer group of an image pixel is a pixel similarity-based concept which has been successfully used to devise image denoising methods. However, since it is difficult to define the pixel similarity in a crisp way, we propose to represent this similarity in fuzzy terms. In this paper, we introduce the fuzzy peer group concept, which extends the peer group concept in the fuzzy setting. A fuzzy peer group will be defined as a fuzzy set that takes a peer group as support set and where the membership degree of each peer group member will be given by its fuzzy similarity with respect to the pixel under processing. The fuzzy peer group of each image pixel will be determined by means of a novel fuzzy logic-based procedure. We use the fuzzy peer group concept to design a two-step color image filter cascading a fuzzy rule-based switching impulse noise filter by a fuzzy average filtering over the fuzzy peer group. Both steps use the same fuzzy peer group, which leads to computational savings. The proposed filter is able to efficiently suppress both Gaussian noise and impulse noise, as well as mixed Gaussian-impulse noise. Experimental results are provided to show that the proposed filter achieves a promising performance.
119 citations
TL;DR: An extension of the method presented by Benitez et al (1997) for extracting fuzzy rules from an artificial neural network (ANN) that express exactly its behavior is presented.
Abstract: This paper presents an extension of the method presented by Benitez et al (1997) for extracting fuzzy rules from an artificial neural network (ANN) that express exactly its behavior. The extraction process provides an interpretation of the ANN in terms of fuzzy rules. The fuzzy rules presented are in accordance with the domain of the input variables. These rules use a new operator in the antecedent. The properties and intuitive meaning of this operator are studied. Next, the role of the biases in the fuzzy rule-based systems is analyzed. Several examples are presented to comment on the obtained fuzzy rule-based systems. Finally, the interpretation of ANNs with two or more hidden layers is also studied.
119 citations
08 Mar 1992
TL;DR: The author shows that proportional-integral-derivative (PID) controllers can be realized by fuzzy control methods based on the product-sum-gravity method and the simplified fuzzy reasoning method.
Abstract: The author shows that proportional-integral-derivative (PID) controllers can be realized by fuzzy control methods based on the product-sum-gravity method and the simplified fuzzy reasoning method. PID controllers, however, cannot be constructed by the min-max gravity method known as the Mamdani's fuzzy reasoning method. Extrapolative reasoning can be executed by the product-sum-gravity method and the simplified fuzzy reasoning method by extending membership functions of antecedent parts of fuzzy rules. >
118 citations
TL;DR: The stability analysis and systematic design techniques of Takagi–Sugeno (T–S) fuzzy control systems are discussed, the stability of both the input-free T–S fuzzy systems and the closed-loop T.–S fuzzy control system are studied in detail with the extended Lyapunov theory, and two theorems to check the stability are proposed.
118 citations