This paper proposes a neuro-fuzzy approach for optimizing injection molding parameter settings. The approach consists of design of experiments and neuro-fuzzy systems. Experimental data shows that the proposed approach performs better than the traditional trial and error practices usually involved in the injection molding process.

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...

We propose a novel approach for neuro-fuzzy system modeling. A neuro-fuzzy system for a given set of input-output data is obtained in two steps. First, the data set is partitioned automatically into a set of clusters based on input-similarity and output-similarity tests. Membership functions associated with each cluster are defined according to statistical means and variances of the data points...

This paper introduces the application of the hybrid approach Adaptive Neuro-Fuzzy Inference System (ANFIS) for fault classification and diagnosis in industrial actuator. The ANFIS can be viewed either as a fuzzy inference system, a neural network or fuzzy neural network (FNN). This paper integrates the learning capabilities of neural network to the robustness of fuzzy systems in the sense that ...

in big cities, air pollution has become a great environmental issue nowadays. in city of tehran, 90% of air pollutants are generated from traffic, among which carbon monoxide (co) is the most important one because it constitutes more than 75% by weight of total air pollutants. this study aims to predict daily co concentration of the urban area of tehran using a hybrid forward selection- anfis (...

Fuzzy logic and fuzzy systems have recently been receiving a lot of attention, both from the media and scientific community, yet the basic techniques were originally developed in the mid-sixties. Fuzzy logic provides a formalism for implementing expert or heuristic rules on computers, and while this is the main goal in the field of expert or knowledge-based systems, fuzzy systems have had consi...

In order to characterize the behavior of nonlinear dynamic systems many different approaches have been proposed in recent years. One of the best black-box models employed to deal with system nonlinearities is the combination of artificial neural network (ANN) and fuzzy logic system (FLS), which is known as neuro-fuzzy system. However, the gradient-based nature of this combination causes some de...

The composition of simple local models for approximating complex nonlinear mappings is a common practice in recent modeling and control literature. This paper presents a comparative analysis of two di,erent local approaches: the neuro-fuzzy inference system and the lazy learning approach. Neuro-fuzzy is a hybrid representation which combines the linguistic description typical of fuzzy inference...

In this paper, an adaptive control method for hybrid position/force control of robot manipulators, based on neuro-fuzzy modeling, is presented. Since the force control involves applying certain amount of force on the surface of an object, it is important to consider the friction force between the end-effector and the surface into account. In order to compensate this friction force, a robust and...

A neuro-fuzzy modeling for forecasting the future dynamical behavior in vibration testing during satellite qualification is proposed in this paper. Vibration testing is employed for emulating vibrations present during the lifetime launching. There are different levels of excitation during vibration testing in order to verify and assure that the satellite and their sub-systems will support the e...