This paper presents a new approach to acoustic noise identification, by introducing fuzzy modeling techniques. Fuzzy identification is compared to conventional linear identification techniques, using as the system to model a new device called ElectroMechanical Film (EMF), developed by VTT [1]. This device can be used either as an acoustic sensor or actuator. The obtained model represents the be...

A novel fuzzy-neural tree (FNT) is presented, where each tree node uses a Gaussian as a fuzzy membership or possibility distribution in place of sigmoidal function in conventional neural networks. Although neural networks with Gaussian activation functions as well as different types of cooperative neuro-fuzzy systems have been extensively described in the literature, the FNT presented in this p...

This paper presents a new fuzzy modeling method that can be classified as a grid partitioning method, in which the domain space is partitioned by the fuzzy equalization method one dimension at a time, followed by the computation of rule weights according to the max-min composition. Five datasets were selected for testing. Among them, three datasets are high-dimensional; for these datasets only ...

Recent technological advances in high-throughput data collection give biologists the ability to study increasingly complex systems. A new methodology is needed to develop and test biological models based on experimental observations and predict the effect of perturbations of the network (e.g. genetic engineering, pharmaceuticals, gene therapy). Diverse modeling approaches have been proposed, in...

This article discusses a rule-based fuzzy model for the identi cation of nonlinear MISO (multiple input, single output) systems. The dis cussed method of fuzzy modeling consists of two parts: structure modeling, i.e. determing the num ber of rules and input variables involved respec tively, and parameter optimization, i.e. optimizing the location and form of the curves which describe the fuz...

—In the above paper, the so-called accurate linguistic modeling (ALM) method was proposed to improve the accuracy of linguistic fuzzy models. A number of examples are given to demonstrate the benefits of the approach. We show that: 1) these examples are not suitable as benchmarks or demonstrators of nonlinear modeling techniques and 2) better results can be obtained by using both standard regre...

The Open Geospatial Consortium (OGC) Geography Markup Language (GML) explicitly represents geographical spatial knowledge in text mode. All kinds of fuzzy problems will inevitably be encountered in spatial knowledge expression. Especially for those expressions in text mode, this fuzziness will be broader. Describing and representing fuzziness in GML seems necessary. Three kinds of fuzziness in ...

Sometime in modeling applied problems there may be a degree of uncertainty in the parameters used in the model or some measurements may be imprecise. Due to such features, we are tempted to consider the study of functional equations in the fuzzy setting. The notion of fuzzy sets was first introduced by Zadeh [31] in 1965 which is a powerful hand set for modeling uncertainty and vagueness in var...

Neuro-fuzzy modeling may be qualified as a grey-box technique, since it combines the transparency of rule-based fuzzy systems with the learning capability of neural networks. The main problem in the identification of non-linear processes is the lack of complete information. Certain variables are, either immeasurable or difficult to measure, the soft sensors are the necessary tools to solve the ...

This article describes an approach to automatically build a Takagi-Sugeno fuzzy model (TSK-model) based on a set of input-output data (system identification). Identifying rule-based fuzzy models consists of two parts: structure modeling, i. e. determining the number of rules and input variables involved respectively, and parameter optimization, i. e. optimizing the rules consequences and the lo...