Fuzzy systems and neural networks XML schemas for soft computing

This article presents an XML[2] based language for the specification of objects in the Soft Computing area. The design promotes reuse and takes a compositional approach in which more complex constructs are built from simpler ones; it is also independent of implementation details as the definition of the language only states the expected behaviour of every possible implementation. Here the basic structures for the specification of concepts in the Fuzzy Logic area are described and a simple construct for a generic neural network model is introduced

Stepwise selection of functional covariates in forecasting peak levels of olive pollen

High levels of airborne olive pollen represent a problem for a large proportion of the population because of the many allergies it causes. Many attempts have been made to forecast the concentration of airborne olive pollen, using methods such as time series, linear regression, neural networks, a combination of fuzzy systems and neural networks, and functional models. This paper presents a functional logistic regression model used to study the relationship between olive pollen concentration and different climatic factors, and on this basis to predict the probability of high (and possibly extreme) levels of airborne pollen, selecting the best subset of functional climatic variables by means of a stepwise method based on the conditional likelihood ratio test.Projects MTM2010-20502 from Dirección General de Investigación del MEC, Spain and FQM-307 from Consejería de Innovación, Ciencia y Empresa de la Junta de Andalucía Spai

SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN JURUSAN SMK MENGGUNAKAN NEURO FUZZY

ABSTRACT Many available majors at the level of vocational schools make students dificult to determine the appropriate majors with his ability. Most students just followed their friends to choose the majors that can cause students feel does not match after entering the majors. Therefore we need a decision support system that can perform calculation of values, abilities and interests owned by the students to help determine the appropriate majors and SMK. This system implements Neuro-Fuzzy method (integration of fuzzy systems and neural networks). Neuro-Fuzzy is a method that uses neural networks to implement fuzzy inference systems. The system requires some form of input values, abilities and interests of students. Results from the system is recommendation value which is consistent within student�s value, abilities and interests

SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN JURUSAN SMK MENGGUNAKAN NEURO FUZZY

ABSTRACT Many available majors at the level of vocational schools make students dificult to determine the appropriate majors with his ability. Most students just followed their friends to choose the majors that can cause students feel does not match after entering the majors. Therefore we need a decision support system that can perform calculation of values, abilities and interests owned by the students to help determine the appropriate majors and SMK. This system implements Neuro-Fuzzy method (integration of fuzzy systems and neural networks). Neuro-Fuzzy is a method that uses neural networks to implement fuzzy inference systems. The system requires some form of input values, abilities and interests of students. Results from the system is recommendation value which is consistent within student�s value, abilities and interests

Data-driven techniques for the fault diagnosis of a wind turbine benchmark

This paper deals with the fault diagnosis of wind turbines and investigates viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator, i.e., the fault estimate, involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, together with noise and disturbances. In particular, the proposed data-driven solutions rely on fuzzy systems and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive models with exogenous input, as they can represent the dynamic evolution of the system along time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model that simulates the normal and the faulty behaviour of a wind turbine. The achieved performances are also compared with those of other model-based strategies from the related literature. Finally, a Monte-Carlo analysis validates the robustness and the reliability of the proposed solutions against typical parameter uncertainties and disturbances.This paper deals with the fault diagnosis of wind turbines and investigates viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator, i.e., the fault estimate, involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, together with noise and disturbances. In particular, the proposed data-driven solutions rely on fuzzy systems and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive models with exogenous input, as they can represent the dynamic evolution of the system along time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model that simulates the normal and the faulty behaviour of a wind turbine. The achieved performances are also compared with those of other model-based strategies from the related literature. Finally, a Monte-Carlo analysis validates the robustness and the reliability of the proposed solutions against typical parameter uncertainties and disturbances

A survey of fuzzy control for stabilized platforms

This paper focusses on the application of fuzzy control techniques (fuzzy type-1 and type-2) and their hybrid forms (Hybrid adaptive fuzzy controller and fuzzy-PID controller) in the area of stabilized platforms. It represents an attempt to cover the basic principles and concepts of fuzzy control in stabilization and position control, with an outline of a number of recent applications used in advanced control of stabilized platform. Overall, in this survey we will make some comparisons with the classical control techniques such us PID control to demonstrate the advantages and disadvantages of the application of fuzzy control techniques