Ground-level ozone is a dangerous pollutant for which the prediction of the concentration could be of great importance. In this paper, we present and compare three fuzzy models aiming the forecasting of ground-level ozone concentration. The models apply Takagi-Sugeno, respective LESFRI fuzzy inference techniques and were generated using the ANFIS method of the Matlab’s Fuzzy Logic ToolBox, resp...

Wave height as well as water depth at the breaking point are two basic parameters which are necessary for studying coastal processes. In this study, the application of soft computing-based methods such as artificial neural network (ANN), fuzzy inference system (FIS), adaptive neuro fuzzy inference system (ANFIS) and semi-empirical models for prediction of these parameters are investigated. Th...

Many researchers have been interested in approximation properties of fuzzy logic systems (FLS), which like neural networks can be seen as approximation schemes. Almost all of them tackled Mamdani fuzzy model, which was shown to have many interesting features. This paper aims to present alternatives for traditional inference mechanisms and CRI method. The most attractive advantage of these new m...

In this paper, we propose a genetic fuzzy image filtering based on rank-ordered absolute differences (ROAD) and median of the absolute deviations from the median (MAD). The proposed method which is consisted of three components, including fuzzy noise detection system, fuzzy switching scheme filtering, and fuzzy parameters optimization use genetic algorithms (GA) to perform efficient and effecti...

Fuzzy models to recognize consumer preferences were developed as part of an automated inspection system for biscuits. Digital images were used to estimate physical features of chocolate chip cookies including size, shape, baked dough color, and fraction of top surface area that was chocolate chips. Polls were conducted to determine consumer ratings of cookies. Four fuzzy models were developed t...

Abstract: In this paper an application of the adaptive neuro-fuzzy inference system has been introduced to predict the behavior of a chaotic robot. The chaotic mobile robot implies a mobile robot with a controller that ensures chaotic motions. Chaotic motion is characterized by the topological transitivity and the sensitive dependence on initial conditions. We have used the controller such that...

Since the single input rule modules connected fuzzy inference model (SIRMs model) is proposed by Yubazaki, Yi et al., many researches on the extension of the SIRMs model have been reported. Moreover, the fuzzy functional SIRMs inference model, in which the consequent parts of the functional-type SIRMs model are generalized to fuzzy function, has proposed as one of various extension SIRMs models...

The paper presents a study upon the possibility to use adaptive-network-based fuzzy inference method (ANFIS) in the identification of distributed parameter systems, implementing a distributed sensor network in the system. Some main properties of different identification methods are presented with possible application. The fuzzy systems, implemented using rule bases, fuzzy values, membership fun...

The integration of neural networks and fuzzy inference systems could be formulated into three main categories: cooperative, concurrent and integrated neuro-fuzzy models. We present three different types of cooperative neuro-fuzzy models namely fuzzy associative memories, fuzzy rule extraction using self-organizing maps and systems capable of learning fuzzy set parameters. Different Mamdani and ...