In the present study, prediction of Alumina recovery efficiency (A.R.E), the amount of produced red mud (A.P.R), red mud settling rate (R.S.R) and bound-soda losses (B.S.L) in Bayer process red mud has been carried out for the first time in the field. These predictions are based on Lime to bauxite ratio and chemical analyses of bauxite and lime as Bayer process feed materials. Radial basis func...

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...

Bio-absorbent palm fiber was applied for removal of cationic violet methyl dye from water solution. For this purpose, a solid phase extraction method combined with the artificial neural network (ANN) was used for preconcentration and determination of removal level of violet methyl dye. This method is influenced by factors such as pH, the contact time, the rotation speed, and the adsorbent dosag...

In this contribution, human face as biometric [1] is considered. Original method of feature extraction from image data is introduced using MLP (multilayer perceptron) and PCA (principal component analysis). This method is used in human face recognition system and results are compared to face recognition system using PCA directly, to a system with direct classification of input images by MLP and...

A complex valued neural network is a neural network, which consists of complex valued input and/or weights and/or thresh olds and/or activation functions. Complex-valued neural networks have been widening the scope of applications not only in electronics and informatics, but also in social systems. One of the most important applications of the complex valued neural network is in image and visio...

Radial basis function (RBF) neural networks provide attractive possibilities for solving 9 signal processing and pattern classi!cation problems. Several algorithms have been proposed for choosing the RBF prototypes and training the network. The selection of the RBF 11 prototypes and the network weights can be viewed as a system identi!cation problem. As such, this paper proposes the use of the ...

In this paper, a new method of bandwidth estimation using variation of slot length on interdigital band pass filter has been presented using artificial neural networks for desired frequency range between 1.7—2.5 GHz. Interdigital filter is multifinger periodic structure which offers compact filter design space. An ANN model has been developed and tested for estimating the cut off frequency of b...

This paper presents a Radial Basis Function/Multilayer Perceptron (RBF/MLP) modular neural network, training with the Resilient Backpropagation (Rprop) algorithm which has been used for nonlinear device modeling in microwave band. The proposed modular configuration employs three or more neural networks, each one with a hidden layer of neurons, and aim to take advantage of the MLP and RBF networ...

A control scheme for dynamic tracking of mobile robots is presented, which integrates a velocity controller based on backstepping techniques and a torque controller based on improved RBF neural networks. The proposed torque control strategy derived from sliding modes depends on the dynamics of mobile robots. Because of the uncertainties in robot dynamics, the robustness of the system cannot be ...

The work deals with development and application of Radial basis functions neural networks (RBFNN) for spatial predictions. Geostatistical tools for spatial correlation analysis (variography) are used to qualify and quantify the estimation results. Geostatistical analysis is performed on the residuals obtained at the training and test sample locations. Variogram of residuals explores spatial cor...