In this paper, a brain-like neural network model is studied, which consists of basic networks and branch control networks. The branch control network can be used to determine which intermediate nodes of the basic network should be connected to the output node with a coefficient of relative strength ranging from zero to one. This determination will adjust the outputs of the intermediate nodes of the basic network. Therefore, by using such a construction, locally functions distributed networks can be realized depending on the values of the inputs of the network. This model is applied to a function approximating problem. The simulation results show that such brain-like model exhibits better performance than the conventional networks with comparable complexity.
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