in this study, a new adaptive controller is proposed for position control of pneumatic systems. difficulties associated with the mathematical model of the system in addition to the instability caused by pulse width modulation (pwm) in the learning-based controllers using gradient descent, motivate the development of a new approach for pwm pneumatics. in this study, two modified feedback error l...

. This paper devoted to an iris recognition system (IRS) designed using 2D-Discrete Cosine Transform (DCT) features and Self Organizing Map (SOM) and Radial Basis Function (RBF) which are an Artificial Neural Network (ANN) used as classifier. DCT is used for feature extraction to capture essential details. SOM and RBF are applied for classification with different functional paradigms. With resp...

In this study the impact of a Radio-over-Fiber (RoF) subsystem on the performance of Orthogonal Frequency Division Multiplexing (OFDM) system is evaluated. The study investigates the use of Multi-Layered Perceptron (MLP) and Radial Basis Function (RBF) neural networks to compensate for the optical subsystem nonlinearities in terms of bit error rate, error vector magnitude, and computational com...

This paper proposes a novel neural-network approach to blind source separation in nonlinear mixture. The approach utilizes a radial basis function (RBF) neural-network to approximate the inverse of the nonlinear mixing mapping which is assumed to exist and able to be approximated using an RBF network. A contrast function which consists of the mutual information and partial moments of the output...

In this paper a novel technique is proposed for the estimation of resonant frequency of coaxial feed equilateral triangular microstrip patch antenna. The major advantage of the proposed approach is that, after proper training, proposed neural model completely bypasses the repeated use of complex i terative process for calculation of resonant frequency, thus resulting in an extremely fast soluti...

Classical Radial Basis Function (RBF) neural network controller designs typically fix the number of basis functions and tune only the weights. In this paper we present a backstepping neural network controller algorithm in which all RBF parameters, including centers, variances and weight matrices are tuned online. By using a Lyapunov approach, tuning rules for updating the RBF parameters are der...

This paper proposes a hybrid neural network model using a possible combination of different transfer projection functions (sigmoidal unit, SU, product unit, PU) and kernel functions (radial basis function, RBF) in the hidden layer of a feed-forward neural network. An evolutionary algorithm is adapted to this model and applied for learning the architecture, weights and node typology. Three diffe...

Most Artificial neural networks (ANN) models used in the remote sensing classification are based on the multilayer perceptron (MLP) with back-propagation (BP) training algorithm. Compared to conventional statistical classifiers, MLP classifiers are non-parametric and distribution-free and is thus less restrictive in approximation, especially when distributions of features are strongly non-Gauss...

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 study, a reliability-based RBF neural network ensemble forecasting model is proposed to overcome the shortcomings of the existing neural ensemble methods and ameliorate forecasting performance. In this model, the ensemble weights are determined by the reliability measure of RBF network output. For testing purposes, we compare the new ensemble model’s performance with some existing netwo...