This paper presents an online architecture design algorithm for radical basis function(RBF) neural network based
on online subtractive clustering algorithm aiming at designing the minimal RBF neural network structure. The algorithm
combines the characteristics that the online substractive clustering can track the real-time condition with the parameters
learning process of the RBF neural network, which makes the RBF neural network adapt to the change of real-time condition
dynamics while maintaining a compact network architecture. Therefore, this method can effectively solve the problem of
self-organizing structure design of the RBF neural network. Only the kernel function whose Euclidean distance is nearest to
the real-time conditions is adjusted, which greatly improves the learning speed of the network. The results of experiments
on the typical function approximation and the chaotic time series prediction show that the algorithm owns favorable dynamic
character response and approximating ability.