Evolving fuzzy systems

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Every segmentation algorithm has parameters that
need to be adjusted in order to achieve good results. Evolving
fuzzy systems for adjustment of segmentation parameters have
been proposed recently (Evolving fuzzy image segmentation –
EFIS [1]). However, similar to any other algorithm, EFIS too
suffers from a few limitations when used in practice. As a major
drawback, EFIS depends on detection of the object of interest for feature calculation, a task that is highly application-dependent.
In this paper, a new version of EFIS is proposed to overcome
these limitations. The new EFIS, called self-configuring EFIS
(SC-EFIS), uses available training data to auto-configure the
parameters that are fixed in EFIS. As well, the proposed SCEFIS
relies on a feature selection process that does not require
the detection of a region of interest (ROI).

A methodology for the development of linguistically interpretable fuzzy models from data is presented. The implementation of the model is conducted through the training of a neuro-fuzzy network, i.e., a neural net architecture capable of representing a fuzzy system. In the first phase, the structure of the model is obtained by means of subtractive clustering, which allows the extraction of a set of relevant rules based on a set of representative input–output data samples. In the second phase, the parameters of the model are tuned via the training of a neural network through backpropagation. In order to attain interpretability goals, the method proposed imposes some constraints on the tuning of the parameters and performs membership function merging. In this way, it will be easy to assign linguistic labels to each of the membership functions obtained, after training. Therefore, the model obtained for the system under analysis will be described by a set of linguistic rules, easily interpretable.

The efficiency of a fuzzy logic-based system is catalyzed by the system design. Fuzzy sets generalize classical crisp sets by incorporating concepts of membership for a fuzzy variable. Each fuzzy set is associated with linguistic concepts that are germane to a particular application. This paper presents an approach for evaluating the region of certainty and uncertainty represented in design of fuzzy linguistic variables. Fuzzy Resembler (FuzR) attempts to capture the goodness of a fuzzy system design using a geometric approach; it can be used for evaluating the design of fuzzy membership space. FuzR is the ratio between region of certainty to region of uncertainty. From the results, it can be inferred that FuzR presents meaningful observations of a fuzzy variable, characterized by trapezoidal, triangular, and gaussian membership functions. FuzR can be used as a design evaluation parameter for evolving fuzzy systems. Knowledge engineers can use it to optimize design of fuzzy systems in the absence of domain experts. Moreover, the level of abstraction provided by FuzR makes it an intuitive design parameter. The significance of this work lies more in its point-of-view than voracious results; the theory and formulation are still young and much more is yet to be conceptualized and tested.

In this paper, we investigate on-line fuzzy modeling for predicting the prices of residential premises using the concept of evolving fuzzy models. These combine the aspects of incrementally updating the parameters and expanding the inner structure on demand with the concepts of uncertainty modeling in a possibilistic and linguistic manner (achieved through fuzzy sets and fuzzy rule bases). We use the FLEXFIS and eTS approaches as learning engines for evolving fuzzy (regression) models and compared them with a property valuating method employed by professional appraisers in reality as well as with a classical genetic fuzzy system. The comparison was based on a real-world data set taken from a cadastral system including prices of residential premises within the years 1998 to 2008.

We developed two GA-based schemes for the design of fuzzy rule-based classification systems. One is genetic rule selection and the other is genetics-based machine learning (GBML). In our genetic rule selection scheme, first a large number of promising fuzzy rules are extracted from numerical data in a heuristic manner as candidate rules. Then a genetic algorithm is used to select a small number of fuzzy rules. A rule set is represented by a binary string whose length is equal to the number of candidate rules. On ...

An approach to on-line design of fuzzy controllers of Takagi-Sugeno type with gradually evolving structure is treated in the paper. Fuzzy rules, representing the structure of the controller are generated based on data collected during the process of control using newly introduced technique for on-line identification of Takagi-Sugeno systems. The output of the plant under control (including its dynamic) and the respective control signal has been memorised and stored in on-line mode. These data has been used to train in a non-iterative, recursive way the fuzzy controller. The indirect adaptive control approach has been used in combination with the novel on-line identification technique. This approach exploits the quasi-linear nature of Takagi-Sugeno models and builds-up the control rule-base structure and adapts it in on-line mode with recursive, non-iterative learning. The method is illustrated with an example from airconditioning systems, though it has wider potential applications.