Stability Analysis for an Online Evolving Neuro‐Fuzzy Recurrent Network
José de Jesús Rubio · 2010
In this chapter, an online evolving neuro-fuzzy recurrent network (ENFRN) is proposed. The network is capable of perceiving the change in the actual system and adapting itself to the new situation. Both structure and parameters learning take place at the same time. The network generates a new hidden neuron if the smallest distance between the new data and all the existing hidden neurons is more than a predefined radius. A new pruning algorithm based on the population density is proposed to use a modified least-squares algorithm to train the parameters of the network. The major contributions of the chapter are: (1) The stability of the algorithm of the proposed evolving neuro-fuzzy recurrent network was proven; and (2) the bound of the average identification error was found. Three examples are provided to illustrate the effectiveness of the suggested algorithm based on simulations. Controlled Vocabulary Terms fuzzy neural nets; least squares approximations; stability