Automated fuzzy knowledge base generation and tuning

Burkhardt, Dirk; Bonissone, Piero P.

doi:10.1109/fuzzy.1992.258615

Cited by 94 publications

(16 citation statements)

References 9 publications

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“…They first initialized the rule base according to intuitive heuristics, used GAs to generate better rule base, and finally tuned the membership functions of the best rule base. This order of the tuning process is similar to that typically used by self-organizing controllers [see Burkhardt and Bonissone (1992) ]. Lee and Takagi (1993) also tuned the rule base and the termsets.…”

Section: Fl Controller Tuned By Gasmentioning

confidence: 97%

Soft computing: the convergence of emerging reasoning technologies

Bonissone¹

1997

Soft Computing - A Fusion of Foundations, Methodologies and App

205

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The term Soft Computing (SC) represents the combination of emerging problem-solving technologies such as Fuzzy Logic (FL), Probabilistic Reasoning (PR), Neural Networks (NNs), and Genetic Algorithms (GAs). Each of these technologies provide us with complementary reasoning and searching methods to solve complex, real-world problems. After a brief description of each of these technologies, we will analyze some of their most useful combinations, such as the use of FL to control GAs and NNs parameters; the application of GAs to evolve NNs (topologies or weights) or to tune FL controllers; and the implementation of FL controllers as NNs tuned by backpropagation-type algorithms.

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Section: Fl Controller Tuned By Gasmentioning

confidence: 97%

Soft computing: the convergence of emerging reasoning technologies

Bonissone¹

1997

Soft Computing - A Fusion of Foundations, Methodologies and App

205

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“…With data mining techniques, we can extract useful knowledge from training data to solve problems. Many methods have been proposed to generate fuzzy rules from training instances [3], [5], [6], [15], [17], [18] based on the fuzzy set theory [19]. In [6], we have presented a method for generating fuzzy rules from relational database systems for estimating null values.…”

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confidence: 99%

Generating weighted fuzzy rules from relational database systems for estimating null values using genetic algorithms

Chen

Huang

2003

IEEE Trans. Fuzzy Syst.

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Abstract-In recent years, some methods have been proposed to estimate null values in relational database systems. However, the estimated accuracy of the existing methods are not good enough. In this paper, we present a new method to generate weighted fuzzy rules from relational database systems for estimating null values using genetic algorithms (GAs), where the attributes appearing in the antecedent part of generated fuzzy rules have different weights. After a predefined number of evolutions of the GA, the best chromosome contains the optimal weights of the attributes, and they can be translated into a set of rules to be used for estimating null values. The proposed method can get a higher average estimated accuracy rate than the methods we presented in two previous papers.

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“…Thus, both the structure and the system parameters may have to be estimated using only numerical data of the unknown relationship, which is usually available as the desired I/O pairs. Many fuzzy systems that automatically derive fuzzy IF-THEN rules from sample data have been proposed in the bibliography for the problem of function approximation [8]- [10], [4]. Tong [11] was one of the first to use numerical information to construct fuzzy systems.…”

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confidence: 99%

Self-organized fuzzy system generation from training examples

Rojas

Pomares

Ortega

et al. 2000

IEEE Trans. Fuzzy Syst.

129

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Abstract-In the synthesis of a fuzzy system two steps are generally employed: the identification of a structure and the optimization of the parameters defining it. This paper presents a methodology to automatically perform these two steps in conjunction using a three-phase approach to construct a fuzzy system from numerical data. Phase 1 outlines the membership functions and system rules for a specific structure, starting from a very simple initial topology. Phase 2 decides a new and more suitable topology with the information received from the previous step; it determines for which variable the number of fuzzy sets used to discretize the domain must be increased and where these new fuzzy sets should be located. This, in turn, decides in a dynamic way in which part of the input space the number of fuzzy rules should be increased. Phase 3 selects from the different structures obtained to construct a fuzzy system the one providing the best compromise between the accuracy of the approximation and the complexity of the rule set. The accuracy and complexity of the fuzzy system derived by the proposed self-organized fuzzy rule generation procedure (SOFRG) are studied for the problem of function approximation. Simulation results are compared with other methodologies such as artificial neural networks, neuro-fuzzy systems, and genetic algorithms.Index Terms-Function approximation, fuzzy system design, generation of membership functions and rules.

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Automated fuzzy knowledge base generation and tuning

Cited by 94 publications

References 9 publications

Soft computing: the convergence of emerging reasoning technologies

Soft computing: the convergence of emerging reasoning technologies

Generating weighted fuzzy rules from relational database systems for estimating null values using genetic algorithms

Self-organized fuzzy system generation from training examples

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