The learning process of RBF networks is similar to the learning process of BP networks. The main difference between them is that they use different function functions. In the BP network layer using the Sigmoid function, the range of values in the input space within the infinite is nonzero, which is a global approximation of neural network; and the function of RBF network is the Gauss function, its value in the limited range of input space is nonzero, because the network is RBF the neural network of local approximation. RBF network is a 3 layer feedforward network, from input to output mapping is nonlinear, and the hidden layer space to the output space mapping is linear, and the

第10章 模糊逼近算法.ppt

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