A self-organizing fuzzy neural network and its applications to function approximation and forecast modeling

To solve the problem of conventional input–output space partitioning, a new learning algorithm for creating self-organizing fuzzy neural networks (SOFNN) is proposed, which automates structure and parameter identification simultaneously based on input-target samples. First, a self-organizing clustering approach is used to establish the structure of the network and obtain the initial values of its parameters, then a supervised learning method to optimize these parameters. Two specific implementations of the algorithm, including function approximation and forecast modeling of the wastewater treatment system, are developed, comprehensive comparisons are made with other approaches in both of the examples. Simulation studies demonstrate the presented algorithm is superior in terms of compact structure and learning efficiency.