Identification of Fuzzy Rules from Learning Data

Gradus, 2021

A sparse fuzzy rule base provides low complexity and low memory requirements for the fuzzy system. Automatic fuzzy model generation from sample data involves two main tasks. These are structure determination and parameter identification. In this paper, we present a new approach that initially generates two rules, then gradually adds new rules to the rule base, and then finds the quasi-optimal values of the parameters using particle swarm optimization.

IEEE Transactions on Fuzzy Systems, 2002

The identification of a model is one of the key issues in the field of fuzzy system modeling and function approximation theory. There are numerous approaches to the issue of parameter optimization within a fixed fuzzy system structure but no reliable method to obtain the optimal topology of the fuzzy system from a set of input-output data. This paper presents a reliable method to obtain the structure of a complete rule-based fuzzy system for a specific approximation accuracy of the training data, i.e., it can decide which input variables must be taken into account in the fuzzy system and how many membership functions (MFs) are needed in every selected input variable in order to reach the approximation target with the minimum number of parameters.

1995

Inductive learning algorithms try to obtain the knowledge of a system from a set of examples. One of the most di cult problems in machine learning consists in getting the structure of this knowledge. We propose an algorithm able to manage with fuzzy information and able to learn the structure of the rules that represent the system. The algorithm gives a reasonable small set of fuzzy rules that represent the original set of examples.

1994

ABSTRACT The objective of this work is to describe a numerical technique to identify parameters of a fuzzy model. When this model and the membership functions of the input variables are continuously differentiable, we show that the estimation of parameters can be performed with a two-levels hierarchical algorithm, comprising the estimation of the model parameters and the estimation of the membership functions parameters. The proposed algorithm is then applied to an example describing a non-linear system.

Fuzzy Sets and Systems, 1998

The purpose of this paper is to present a genetic learning process for learning fuzzy control rules from examples. It is developed in three stages: the rst one is a fuzzy rule genetic generating process based on a rule learning iterative approach, the second one combines two kinds of rules, experts rules if there are and the previously generated fuzzy control rules, removing the redundant fuzzy rules, and the third one is a tuning process for adjusting the membership functions of the fuzzy rules. The three components of the learning process are developed formulating suitable Genetic Algorithms.

1998

Fuzzy systems are currently being used in a wide field of industrial and scientific applications. Therefore, it is necessary to have algorithms which construct and optimize such systems automatically. Since the idea of learning is being studied in other research areas like machine learning and data mining, some of the developed methods have been made available and optimized for the learning process in fuzzy systems. In this paper, we present a short survey of these methods and take a closer look at a special learning approach, the neuro-fuzzy systems.

IEEE Transactions on …, 2008

Genetic algorithms and soft computing, Physica- …, 1996

This paper describes a fuzzy rule learning system called SLAVE (Structural Learning Algorithm in Vague Environment) which learns a set of fuzzy rules from a set of examples. SLAVE has been developed for working with noise-a ected systems where the application of some conditions of classical learning theory do not produce good descriptions. This learning system allows the structure of the rule to be obtained, i.e., it can determine those that are relevant for describing the system from all the variables proposed (feature selection). Finally, we test the behaviour of this learning algorithm in a control problem and we compare its results with other learning algorithms for control.

International Journal of Systems Science, 2000

This paper presents an algorithm for incorporating a priori knowledge into data-driven identification of dynamic fuzzy models of the Takagi-Sugeno type. Knowledge about the modelled process such as its stability, minimal or maximal static gain, or the settling time of its step response can be translated into inequality constraints on the consequent parameters. By using input-output data, optimal parameter values are then found by means of quadratic programming. The proposed approach has been applied to the identification of a laboratory liquid level process. The obtained fuzzy model has been used in model-based predictive control. Real-time control results show that when the proposed identification algorithm is applied, not only physically justified models are obtained, but also the performance of the model-based controller improves with regard to the case where no prior knowledge is involved.

IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2001

Most methods of fuzzy rule-based system identification (SI) either ignore feature analysis or do it in a separate phase. This paper proposes a novel neuro-fuzzy system that can simultaneously do feature analysis and SI in an integrated manner. It is a five-layered feed-forward network for realizing a fuzzy rule-based system. The second layer of the net is the most important one, which along with fuzzification of the input also learns a modulator function for each input feature. This enables online selection of important features by the network. The system is so designed that learning maintains the nonnegative characteristic of certainty factors of rules. The proposed network is tested on both synthetic and real data sets and the performance is found to be quite satisfactory. To get an "optimal" network architecture and to eliminate conflicting rules, nodes and links are pruned and then the structure is retrained. The pruned network retains almost the same level of performance as that of the original one.