The goal of this work was to develop and validate a neuro-fuzzy intelligent system (LOLIMOT) for rectal temperature prediction of broiler chickens. The neuro-fuzzy network was developed using SCILAB 4.1, on the ground of three Departamento de Engenharia, Universidade Federal de Lavras (UFLA), Caixa Postal 3037, Lavras/MG, Brasil [email protected] [email protected] [email protected] Des...

The need to update the calibration of Function Point (FP) complexity weights is discussed, whose aims are to fit specific software application, to reflect software industry trend, and to improve cost estimation. Neuro-Fuzzy is a technique that incorporates the learning ability from neural network and the ability to capture human knowledge from fuzzy logic. The empirical validation using ISBSG d...

The concepts of calibrating Function Points are discussed, whose aims are to fit specific software application, to reflect software industry trend, and to improve cost estimation. Neuro-Fuzzy is a technique which incorporates the learning ability from neural network and the ability to capture human knowledge from fuzzy logic. The empirical validation using ISBSG data repository Release 8 shows ...

This paper proposes the neural network solution to the indirect vector control of three phase induction motor including an adaptive neuro fuzzy controller. The basic equations and elements of the indirect vector control scheme are given. The proposed control scheme is realized by an adaptive neuro-fuzzy controller and two feed forward neural network. The neuro-fuzzy controller incorporates fuzz...

In a real time system contained various type of scheduling algorithms. They are used for determine which processes should be executed by the CPU when there are different processes to be executed. Neuro fuzzy logic approaches are very effective for scheduling real time task. This paper presents a review on scheduling algorithm of real time task. Then, discuss the limitations of EDF algorithm and...

The motivation behind mathematically modeling the human operator is to help explain the response characteristics of the complex dynamical system including the human manual controller. In this paper, we present two different fuzzy logic strategies for human operator and sport modeling: fixed fuzzy–logic inference control and adaptive fuzzy–logic control, including neuro–fuzzy–fractal control. As...

Neuro-fuzzy system has been shown to provide a good performance on chromosome classification but does not offer a simple method to obtain the accurate parameter values required to yield the best recognition rate. This paper presents a neuro-fuzzy system where its parameters can be automatically adjusted using genetic algorithms. The approach combines the advantages of fuzzy logic theory, neural...

This paper reviews the use of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for vector-controlled induction motor drives. While conventional schemes do not deal well with the highly nonlinear nature of motor control, fuzzy logic with its adjustability and neural networks with their adaptability have been shown to be excellent alternatives. ANFIS combines the advantages of fuzzy logic and neura...

Fuzzy Logic, introduced by Zadeh along with his introduction of fuzzy sets, is a continuous multi-valued logic system. Hence, it is a generalization of the classical logic and the classical discrete multi-valued logic (e.g. Łukasiewicz’ three/many-valued logic). Throughout the years Zadeh and other researches have introduced extensions to the theory of fuzzy setts and fuzzy logic. Notable exten...

This work introduces an embedded small indoor helicopter heading controller based on fuzzy logic. The work addresses the problem of system identification when implementing a Takagi-Sugeno-Kang type fuzzy logic controller. Instead of identifying the system formally beforehand, the fuzzy controllers consequent parameters are learned using a Neuro-Fuzzy Inference System with data collected from an...