نتایج جستجو برای: neural computing
تعداد نتایج: 711680 فیلتر نتایج به سال:
Classification of texture patterns is one of the most important problems in pattern recognition. In this paper, we present a classification method based on the Discrete Cosine Transform (DCT) coefficients of texture images. As DCT works on gray level images, the color scheme of each image is transformed into gray levels. For classifying the images using DCT, we used two popular soft computing t...
Classification of texture pattern is one of the most important problems in pattern recognition. In this paper, we present a classification method based on the Discrete Cosine Transform (DCT) coefficients of texture image. As the DCT works on gray level image, the color scheme of each image is transformed into gray levels. For classifying the images with DCT we used two popular soft computing te...
Neural network, as a fundamental classification algorithm, is widely used in many image classification issues. With the rapid development of high performance computing device and parallel computing devices, convolutional neural network also draws increasingly more attention from many researchers in this area. In this project, we deduced the theory behind back-propagation neural network and impl...
Classification of texture pattern is one of the most important problems in pattern recognition. In this paper, we present a classification method based on the Discrete Cosine Transform (DCT) coefficients of texture image. As DCT works on gray level image, the color scheme of each image is transformed into gray levels. For classifying the images using DCT we used two popular soft computing techn...
Deep neural networks have demonstrated their great potential in recent years, exceeding the performance of human experts a wide range applications. Due to large sizes, however, compression techniques such as weight quantization and pruning are usually applied before they can be accommodated on edge. It is generally believed that leads degradation, plenty existing works explored strategies aimin...
We show that artificial neural networks (ANNs) can, to high accuracy, determine the topological invariant of a disordered system given its two-dimensional real-space Hamiltonian. Furthermore, we describe ``renormalization-group'' (RG) network, an ANN which converts Hamiltonian on large lattice another small while preserving invariant. By iteratively applying RG network ``base'' computes Chern n...
This paper presents a new perspective of Artificial Intelligence (AI). Although, number of attempts has been made to make an artifact intelligent, including evolution theory, neural network etc and a number of problems have been solved using these concepts but each of this theory covers only some aspect of human intelligence. Still there is a large gap between artificial intelligence agent and ...
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