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In this paper, according to the triple integral definition and the characteristic of common network topology, a new model and learning algorithm is proposed for the triple integral. This method randomly selects certain nodes on each direction of integral domain in the initial, then the weight of the neural network is optimized, finally the quite precise integral result is obtained. Example is provided...
In algorithms design, one of the important aspects is to consider efficiency. Many algorithm design paradigms are existed and used in order to enhance algorithms' efficiency. Opposition-based Learning (OBL) paradigm was recently introduced as a new way of thinking during the design of algorithms. The concepts of opposition have already been used and applied in several applications. These applications...
Image alignment in the presence of non-rigid distortions is a challenging task. Typically, this involves estimating the parameters of a dense deformation field that warps a distorted image back to its undistorted template. Generative approaches based on parameter optimization such as Lucas-Kanade can get trapped within local minima. On the other hand, discriminative approaches like Nearest-Neighbor...
The PID control of RBF-NN is taken for the nonlinear system. Due to the low quality of clustering in the clustering algorithm of the traditional RBF-NN, the rate of convergence is directly influenced by the initial value. In this paper, the quality of the clustering has been raised and the initial value has been optimized through the improvements of the clustering algorithm by taking the K-means-algorithm...
This paper proposes a new global optimization technique in which combines population migration algorithm (PMA) and radial basis function (RBF) neural networks learning algorithm for training RBF neural network. Compared with the traditional RBF training algorithm, the simulation results show that the method has a higher accuracy in a stringency and works well in avoiding sticking in local minima.
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