Displaying similar documents to “A methodology for constructing fuzzy rule-based classification systems.”

Neural methods for obtaining fuzzy rules.

José Manuel Benítez, Armando Blanco, Miguel Delgado, Ignacio Requena (1996)

Mathware and Soft Computing

Similarity:

In previous papers, we presented an empirical methodology based on Neural Networks for obtaining fuzzy rules which allow a system to be described, using a set of examples with the corresponding inputs and outputs. Now that the previous results have been completed, we present another procedure for obtaining fuzzy rules, also based on Neural Networks with Backpropagation, with no need to establish beforehand the labels or values of the variables that govern the system.

A methodology for developing knowledge-based systems.

Juan Luis Castro, José Jesús Castro-Sánchez, Antonio Espin, José Manuel Zurita (1998)

Mathware and Soft Computing

Similarity:

This paper presents a methodology for developing fuzzy knowledge based systems (KBS), which permits a complete automatization. This methodology will be useful for approaching more complex problems that those in which machine learning from examples are successful.

Evolution-fuzzy rule based system with parameterized consequences

Piotr Czekalski (2006)

International Journal of Applied Mathematics and Computer Science

Similarity:

While using automated learning methods, the lack of accuracy and poor knowledge generalization are both typical problems for a rule-based system obtained on a given data set. This paper introduces a new method capable of generating an accurate rule-based fuzzy inference system with parameterized consequences using an automated, off-line learning process based on multi-phase evolutionary computing and a training data covering algorithm. The presented method consists of the following steps:...

A defuzzification based new algorithm for the design of Mamdani-type fuzzy controllers

Jean Jamil Saade (2000)

Mathware and Soft Computing

Similarity:

This paper presents a new learning algorithm for the design of Mamdani- type or fully-linguistic fuzzy controllers based on available input-output data. It relies on the use of a previously introduced parametrized defuzzification strategy. The learning scheme is supported by an investigated property of the defuzzification method. In addition, the algorithm is tested by considering a typical non-linear function that has been adopted in a number of published research articles. The test...

Fuzzy neural network approach to fuzzy polynomials.

Saeid Abbasbandy, M. Otadi (2006)

Mathware and Soft Computing

Similarity:

In this paper, an architecture of fuzzy neural networks is proposed to find a real root of a dual fuzzy polynomial (if exists) by introducing a learning algorithm. We proposed a learning algorithm from the cost function for adjusting of crisp weights. According to fuzzy arithmetic, dual fuzzy polynomials can not be replaced by a fuzzy polynomials, directly. Finally, we illustrate our approach by numerical examples.

Neuro-fuzzy modelling based on a deterministic annealing approach

Robert Czabański (2005)

International Journal of Applied Mathematics and Computer Science

Similarity:

This paper introduces a new learning algorithm for artificial neural networks, based on a fuzzy inference system ANBLIR. It is a computationally effective neuro-fuzzy system with parametrized fuzzy sets in the consequent parts of fuzzy if-then rules, which uses a conjunctive as well as a logical interpretation of those rules. In the original approach, the estimation of unknown system parameters was made by means of a combination of both gradient and least-squares methods. The novelty...