Preliminary note The main goal of each technologist is the prediction of technological parameters by fulfilling the set design and technological demands. The work of the technologist is made easier by acquired knowledge and previous experience. A plan of input-output data was made by using the hybrid system of modelling ANFIS (Adaptive Neuro-Fuzzy Inference System) based on the results of seam ...

Accurate and robust estimation of applied forces in Robotic-Assisted Minimally Invasive Surgery is a very challenging task. Many vision-based solutions attempt to estimate the force by measuring the surface deformation after contacting the surgical tool. However, visual uncertainty, due to tool occlusion, is a major concern and can highly affect the results’ precision. In this paper, a novel de...

This paper describes combined approaches of data preparation, neural network analysis, and fuzzy inferencing techniques (which we collectively call neuro-fuzzy engineering) to the problem of environmental modelling. The overall neuro-fuzzy architecture is presented, and specific issues associated with environmental modelling are discussed. A case study that shows how these techniques can be com...

A new hybrid filtering technique is proposed to improving denoising process on digital images. This technique is performed in two steps. In the first step, gaussian noise and impulse noise is eliminated using decision based algorithm (DBA). Image denoising process is further improved by an appropriately combining DBA with Adaptive Neuro Fuzzy Inference System (ANFIS) at the removal of gaussian ...

Models based on neural and neuro-fuzzy structures are developed to represent knowledge about a large data set containing chemical descriptors of organic compounds, commonly used in industrial processes. The neuro-fuzzy models here proposed include both, QSARs and original numerical values. The developed approaches use various techniques to insert knowledge by training, and to map rules in neuro...

Neuro-fuzzy (NF) decision-making technology is designed and implemented to obtain the optimal daily currency trading rule. We find that a non-linear artificial neural network (ANN) exchange rate microstructure model combined with a fuzzy logic controller (FLC) generates a set of trading strategies that, on average, earn a higher rate of return compared to the simple buy-and-hold strategy. We al...

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In this paper a fuzzy model based on Adaptive Neuro-Fuzzy Inference System (ANFIS) is introduced for calculating the level of risk in managerial problems. In this model, affecting factors on the level of risk are considered as inputs and the level of risk as the output. Using the fuzzy model, the risky condition changes smoothly in a fuzzy environment as it is the case in the real world; while ...

Fuzzy logic controls and neuro-fuzzy controls are accustomed to increase the performance of air conditioning system. In this paper, we are trying to provide the new design air conditioning system by exploitation two logics, namely fuzzy logic and neuro-fuzzy management. This paper proposes a set of rule and uses 2 inputs specifically temperature and humidness and 4 outputs specifically compress...

Although neuro-fuzzy models and other similar modeis have great flexibility they also have drawbacks, especially for systems which have high uncertainty associated . This paper points out which are the major drawbacks of neuro-fuzzy models and proposes a methodology to design fault detectors in uncertain systems using neuro-fuzzy models. The coolíng coil of an air-conditioning system is used as...