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.

An Efficient Method for Extracting Fuzzy Classification Rules from High Dimensional Data

Abstract: We present an efficient method for extracting fuzzy classification rules from high dimensional data. A cluster estimation method called subtractive clustering is used to efficiently extract rules from a high dimensional feature space. A complementary search method can quickly identify the important input features from the resultant high dimensional fuzzy classifier, and thus provides the ability to quickly generate a simpler, more robust fuzzy classifier that uses a minimal number of input features. These methods are illustrated through the benchmark iris data and through two aerospace applications.

A new approach to connective generation in the framework of expert systems using fuzzy logic

Abstract: A technique for modeling uncertainty in expert systems is presented. Operators are defined using linguistic terms to avoid any numerical representation. These operators consider linguistic term set ordering, constraints that are the counterpart of properties fulfilled by triangular norms in fuzzy logic, and additional restrictions created by the expert's procedure to combining certainty. The method avoids the usual problems that arise in other treatments of certainty linguistic terms, where they are represented as fuzzy numbers or fuzzy truth labels. One of the most significant problems is the lack of consensus in the representation of each term by a group of experts, due to the necessity of representing the terms in a pseudonumerical scale. >

Hierarchical Structured Sparse Representation for T–S Fuzzy Systems Identification

Abstract: “The curse of dimensionality” has become a significant bottleneck for fuzzy system identification and approximation. In this paper, we cast the Takagi-Sugeno (T-S) fuzzy system identification into a hierarchical sparse representation problem, where our goal is to establish a T-S fuzzy system with a minimal number of fuzzy rules, which simultaneously have a minimal number of nonzero consequent parameters. The proposed method, which is called hierarchical sparse fuzzy inference systems ( H-sparseFIS), explicitly takes into account the block-structured information that exists in the T-S fuzzy model and works in an intuitive way: First, initial fuzzy rule antecedent part is extracted automatically by an iterative vector quantization clustering method; then, with block-structured sparse representation, the main important fuzzy rules are selected, and the redundant ones are eliminated for better model accuracy and generalization performance; moreover, we simplify the selected fuzzy rules consequent with sparse regularization such that more consequent parameters can approximate to zero. This algorithm is very efficient and shows good performance in well-known benchmark datasets and real-world problems.

A real-time decision-making of maintenance using fuzzy agent

Abstract: In many manufacturing processes, real-time information can be obtained from process control computers and other monitoring devices. However, production control problems are frequently accompanied by certain and uncertain conditions. Problems with uncertainty conditions generally include difficulty in identifying an optimal solution in real-time using conventional mathematical approaches. This study presents a fuzzy logic approach for decision-making of maintenance. Some linguistic variables and rules-of-thumb are used to form the fuzzy logic models, based on the domain experts' experiences in production line and maintenance department. The historical production data are used to train and tune the fuzzy models. The tuned fuzzy models are then embedded into an internet-based and event-oriented information system as fuzzy agent. The production controller can easily make suitable production control decisions based on the inference results of fuzzy agents to satisfy the quick response requirement.

Flexible Takagi-Sugeno fuzzy systems

Abstract: In the paper, a new class of Takagi-Sugeno fuzzy systems is derived. Various parameters and weights are incorporated into construction of such systems. The approach presented in the paper introduces more flexibility to the structure and design of neuro-fuzzy systems.