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Review and performance analysis of single hidden layer sequential learning algorithms of feed-forward neural networks

The single hidden layer feed-forward neural networks learning algorithms with good performance are divided into batch learning and sequential (on-line) learning algorithms. Compared with batch learning algorithms, the sequential learning algorithms of feed-forward neural networks are suitable for problems of real-time processing, and are more adaptable for general industrial applications. This paper summarizes different sequential learning algorithms of single hidden layer feed-forward neural networks and analyzes the advantages and disadvantages of various algorithms. The performance including the stability, learning speed, approximation and generalization ability of different sequential learning algorithms are compared in detail in terms of different chaotic time series prediction problems. The simulation results provide the theoretic guidance on real applications of sequential learning algorithms of feed-forward neural networks.