An online self-organizing algorithm for feedforward neural network

Feedforward neural network (FNN) is the most popular network model, and the appropriate structure and learning algorithms are the key of its performance. This paper proposes an online self-organizing algorithm for feedforward neural network (OSNN) with a single hidden layer. The proposed OSNN optimizes the structure of FNN for time-varying system including structure design and parameter learning. In structure design, this paper measures the contribution ratios of hidden nodes by local sensitivity analysis based on differentiation method. OSNN merges hidden nodes with the others that have the highest correlation when their contribution ratios are almost zero and adds new hidden nodes by error reparation. For parameter learning, an improved online gradient method (OGM), called online gradient method with fixed memory (FMOGM), is proposed to improve the convergence speed and accuracy of OGM. In addition, this paper calculates the contribution ratios and the network error and estimates the local minima by using the fixed-sized training set of FMOGM instead of one sample at the current time, which can obtain more effective local information and a compact network structure. Finally, the proposed OSNN is verified using a number of benchmark problems and a practical problem for biochemical oxygen demand prediction in wastewater treatment. The experimental results show that OSNN has better convergence speed and accuracy than other algorithms.