optimization of machining parameters is very important and the main goal in every machining process. surface finishing prediction is a pre-requirement to establish a center for automatic machining operations. in this research, a neuro-fuzzy approach is used in order to model and predict the surface roughness in dry turning. this approach has both the learning capability of neural network and li...

Neuro{fuzzy combination are considered for several years already. However, the term \neuro{fuzzy" still lacks of proper deenition, and it has the avor of a \buzz word". In this paper we try to give it a meaning in the context of fuzzy classiication systems. From our point of view \neuro{fuzzy" means the employment of heuristic learning strategies derived from the domain of neural network theory...

A new model-based neuro-fuzzy controller for non-linear systems is proposed. The neural network provides a non-linear model of the process being controlled. The nonlinear space of the network is divided into small linear regions. For each of these regions linear controllers can be designed automatically. To ensure a smooth transition between different regions fuzzy membership functions are used...

Hybrid neuro-fuzzy systems have been in evidence during the past few years, due to its attractive combination of the learning capacity of arti2cial neural networks with the interpretability of the fuzzy systems. This article proposes a new hybrid neuro-fuzzy model, named hierarchical neuro-fuzzy quadtree (HNFQ), which is based on a recursive partitioning method of the input space named quadtree...

rainfall-runoff is one of complex hydrological processes that is affected by a variety of physical and hydrological factors. in this study statistical method armax model, neural network, neuro-fuzzy (anfis subtractive clustering and grid partition) and two hybrid models of this methods were used to simulate rainfall-runoff and prediction of streamflow. in each method optimum structure was deter...

This work proposes a Neuro-Fuzzy Intelligent System – ANFIS (Adaptive Network based Fuzzy Inference System) for the annual forecast of greenhouse gases emissions (GHG) into the atmosphere. The purpose of this work is to apply a Neuro-Fuzzy System for annual GHG forecasting based on existing emissions data including the last 37 years in Brazil. Such emissions concern tCO2 (tons of carbon dioxide...

The neural network's performance can be measured by efficiency and accuracy. The major disadvantages of neural network approach are that the generalization capability of neural networks is often significantly low, and it may take a very long time to tune the weights in the net to generate an accurate model for a highly complex and nonlinear systems. This paper presents a novel Neuro-fuzzy archi...

In this paper, a Neuro-Predictive (NP) controller is designed and implemented on a highly non-linear system, a model helicopter in a constrained situation. It is observed that the closed loop system with the NP controller has a significant overshoot and a long settling time in comparison to the same system with an existing fuzzy controller. In order to improve the undesired system performance, ...

Soft computing is a general term for algorithms that learn from human knowledge and mimic human skills. Example of such algorithms are fuzzy inference systems and neural networks. Many applications, especially in control engineering , have demonstrated their appropriate-ness in building intelligent systems that are exible and robust. Although recent research have shown that certain class of neu...