A novel neural architecture for prediction in industrial control: the 'Double Recurrent Radial Basis Function network' (R2RBF) is introduced for dynamic monitoring and prognosis of industrial processes. Three applications of the R2RBF network on the prediction values confirmed that the proposed architecture minimizes the prediction error. The proposed R2RBF is excited by the recurrence of the output looped neurons on the input layer which produces a dynamic memory on both the input and output layers. Given the learning complexity of neural networks with the use of the back-propagation training method, a simple architecture is proposed consisting of two simple Recurrent Radial Basis Function networks (RRBF). Each RRBF only has the input layer with looped neurons using the sigmoid activation function. The output of the first RRBF also presents an additional input for the second RRBF. An unsupervised learning algorithm is proposed to determine the parameters of the Radial Basis Function (RBF) nodes. The K-means unsupervised learning algorithm used for the hidden layer is enhanced by the initialization of these input parameters by the output parameters of the RCE algorithm.

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<p>This paper is a comparisation study between an experimental data and Matlab simulation of output PV characteristic affected by the orientation and the tilt angle of a photovoltaic solar module with inclined plane and by the dimension of the panel. The PV panel was rotated towards the east, south and west and positioned for the angles 0°, 30°, 45°, 60° and 90°. In this position, the values of current, voltage and power are measured. In the other side, using the mathematical model to calculate the solar radiation incident on an inclined surface as a function of the tilt angle was developed in MATLAB/SIMULINK model. The optimum angles were determined as positions in which maximum values of solar irradiation and maximum power were registered to characterize the P-V and V-I photovoltaic panel.</p>

Abstract-This paper is concerned with the synthesis of dynamic model of the redundant manipulator robot based on Linear Parameter Varying approach. To evaluate its behavior and in presence of external disturbance several motions profiles are developed using a new algorithm which produce smooth trajectories in optimal time. The main advantages of this proposed approach are its robustness and its simplicity with respect to the flexibility structure, to the motion profile and mass load variations. Numerical simulations with several tasks show that in presence of mass load variation the desired trajectory is more efficiently followed by the LPV model than the dynamic model of the studied mechanism. Its performances are ensured using the smoothest trajectory designed by the Eighth-degree polynomial profile than the Fifth-degree polynomial one and the trapezoidal one.

To solve the problems of low accuracy and poor stability due to uncertainties, external disturbances and unknown load, which exist in the position control of rigid joint robot manipulator, this article is to propose Non-Singular Fast Terminal Sliding Mode Control strategy with Wavelet neural networks observer (NSFTSMCW). The wavelet observer is designed using the online approximation capability of the neural network, which is used to online estimate the modeling error, external disturbances and uncertainties generated by the dynamic surface control of the joint robot online. Combining the above strategies, the robot manipulators position controller is designed. The stability of this control strategy is demonstrated by stability analysis using the Lyapunov criterion. Simulations on the 2-Link Rigid Joint (2LRJ) robot show that the control strategy can overcome the chattering phenomena ensures the accuracy and stability of the joint robot position control.

Abstract-The purpose of this paper is to develop a dynamic model of a rigid-flexible manipulator robot with a load on its endpoint using Euler-Lagrange formulation. In order to test the performance of the studied system, several mathematical functions are used as motion profile. It choice is very important because it affects the robot's performance. Different factors intervene in this choice. However, the most important is the torque's continuity and the movement's smoothness. Numerical simulations show the robustness of the dynamic model of the studied system for several motions profiles.

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Traditional neural networks are very diverse and have been used during the last decades in the fields of data classification. These networks like MLP, back propagation neural networks (BPNN) and feed forward network have shown inability to scale with problem size and with the slow convergence rate. So in order to overcome these numbers of drawbacks, the use of higher order neural networks (HONNs) becomes the solution by adding input units along with a stronger functioning of other neural units in the network and transforms easily these input units to hidden layers. In this paper, a new metaheuristic method, Firefly (FFA), is applied to calculate the optimal weights of the Functional Link Artificial Neural Network (FLANN) by using the flashing behavior of fireflies in order to classify ISA-Radar target. The average classification result of FLANN-FFA which reached 96% shows the efficiency of the process compared to other tested methods.