in this paper, an adaptive neuro fuzzy inference system (anfis) based control is proposed for the tracking of a micro-electro mechanical systems (mems) gyroscope sensor. the anfis is used to train parameters of the controller for tracking a desired trajectory. numerical simulations for a mems gyroscope are looked into to check the effectiveness of the anfis control scheme. it proves that the sy...

This paper studies traffic variable estimation, and presents a method of estimation for the number of vehicle waiting for queue (NVWQ) based on neuro-fuzzy at urban intersection. we present results of training the neural network for a detectorized intersection in Changsha City. The accuracy of NVWQ estimation using the fuzzy neural networks approaches is more than 90%. The fuzzy neural networks...

This paper presents a fuzzy filtered neural network approach as an application to handwritten numerical representation. A multilayer feedforward adaptive network is used for training the model and for application of the fuzzy filters. Fuzzy filters are integrated with the neural nets for processing the physical data of the images available for the handwritten digits. The use of the fuzzy filter...

this work proposes a neural-fuzzy sliding mode control scheme for a hydro-turbine speed governor system. considering the assumption of elastic water hammer, a nonlinear mode of the hydro-turbine governor system is established. by linearizing this mode, a sliding mode controller is designed. the linearized mode is subject to uncertainties. the uncertainties are generated in the process of linear...

In this paper, the edge detection using fuzzy neural network is described. The input features are fuzzy sets and a learning algorithm employs fuzzified delta rule. To increase the efficiency during the training, the varied learning rate and the momentum is applied instead of fixed values. In addition, instead of pixel-based inputs, the texture-based inputs are fed into the fuzzy neural network ...

The forecast of fluctuations and prices is the major concern in financial markets. Thus, developing an accurate and robust forecasting decision model is critically favorable to the investors. As gold has shown a special capability to smooth inflation fluctuations, governors use gold as a price controlling lever. Thus, more information about future gold price trends will help to make the firm de...

this study presents the effects of project uncertainties on nonlinear time-cost tradeoff (tct) profile of real life engineering projects by the fusion of fuzzy logic and artificial neural network (ann) models with hybrid meta-heuristic (hmh) technique, abridged as fuzzy-ann-hmh. nonlinear time-cost relationship of project activities is dealt with ann models. ann models are then integrated with ...

Because simulation turntable servo system is highly nonlinear and uncertainty plants, a fuzzy neural network PID controller is proposed based on the Radial Basis Function (RBF). Up to now, various kinds of nonlinear PID controllers have been designed in order to satisfactorily control this system and some of them applied in actual systems with different degrees. Given this background, the step ...

The purpose of this study was to evaluate the ability of fuzzy neural network model to predict the likelihood of coronary heart disease for individuals based on knowledge of their biomarkers, risk habits and demographic profiles. The prediction performance of fuzzy neural network models were measured in terms of percentage accuracies and compared with the prediction performance of logistic regr...