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Based on rough set and basic theory of data fusion, the data fusion algorithm combining rough set theory and BP neural network is studied. Since rough set theory can effectively simplify information, cut down the tagged dimension . This paper will be rough set theory and neural networks combined, using channel capacity of knowledge relative reduction algorithms to simplify the input information. Rough...
By proposing a computational algorithm, this paper gives the upper bound on the number of hidden neurons to realize multi-valued functions defined on N-points. The architecture of the network is three-layer feedforward neural network with one hidden layer. The network is composed of multi-valued multi-threshold neurons. This upper bound can help us to determine the size of network when we design learning...
Estimating the number of hidden neurons required for the implementation of an arbitrary function is a fundamental problem of neural networks. This paper presents the lower bound on the number of hidden neurons in three-layer multi-valued multi-threshold neural networks for implementation of an arbitrary q-valued function defined on a set of N-point with n-dimension (N??qn). This result can be applied...
Quantum artificial neural network (QANN) is one of the new paradigms built upon the combination of classical neural and quantum computations. It has great values for theoretic study and potentials to applications. In this paper, an intrinsic similarity of quantum theory and artificial neural network (ANN) theory has been highlighted and analyzed. The dynamic features of QANN are also discussed in...
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