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3D surface compression is a selected method for reduction of required memory and effective triangular mesh data transfer in networks with low bandwidth. In this paper, an improved technique for compression of triangular surfaces is introduced. It is based on centroidal Voronoi tessellation with density function. By choosing appropriate density function with curvature feature and Lloyd relaxation, vertex density in compressed mesh tends toward details in rough surfaces and prevents vertex redundancy and non-necessary vertex concentration in smoother surface areas. In post-processing step, non-linear Nelder-Mead optimization is applied for better vertex localization and reducing compression error in the simplified mesh. Our proposed method is compared with classic and modern techniques in recent studies. Implementation results show improvements in compression and accuracy of our method compared with available techniques.

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In this study a model reference rough-radial basis function neural network controller with feedback error learning for control of a class of nonlinear systems subject to unknown bounded uncertainty is proposed. The proposed controller in hybrid form includes the classic controller and rough- radial basis function neural network controller. Because of using the classic controller with the neural network controller, it is expected that the transient response is bounded. The weights of the output layer of the neural network controller are interval variables. Using an appropriate Lyapunov function, stable adaptation laws for these weights according to the output of the classic controller and based on stability are derived. To show the efficacy of the proposed controller, results of simulation that is applied to Duffing Oscillator and Genesio- Tesi are shown and results are compared with the results when simple model reference radial basis function neural network is used as the controller. The results show that the proposed method is more robust against uncertainty when it is compared to model reference radial basis function neural network controller. Also, using the proposed controller, synchronization of chaotic systems is performed. The results verified the effectiveness of the proposed controller.