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In order to improve the nonlinear mapping capability and learning performance of extreme learning machine (ELM), a new learning algorithm called extreme learning machine based on calculating the output weight of partial robust M-regression is proposed. This algorithm introduces the partial robust M-regression into the extreme learning machine algorithm. Firstly, the hidden layer output matrix H is calculated by extreme learning machine. Secondly, the matrix H and vector Y are weighted by weighted strategy. Then, PRM algorithm is used to establish the regression model between the weighted matrix Hw and vector Yw, and calculate its regression coefficient, namely output weight of the ELM Algorithm. The proposed method predicts the Mackay’s robot arm regression and sediment concentration of the Yellow River Basin to verify the effectiveness of the method. The simulation results show that the proposed PRMELM algorithm is superior to the original extreme learning machine algorithm in prediction accuracy and generalization performance.

Boosting ensemble algorithm exhibits two fatal limitations: one is that it gives in advance the upper bound of weighted error on weak learning algorithm; the other one is that it is overdependent on data and weak learning machine, and it is too sensitive to data noising. Aimed at limitation of Boosting ensemble application in extreme learning machine, this paper proposes a new algorithm: evolutionary extreme learning machine based on dynamic Adaboost ensemble，which regards the evolutionary extreme learning machine as weak learning machine, dynamic Adaboost ensemble algorithm is used to integrate the outputs of weak learning machines, and makes use of fuzzy activation function as activation function of evolutionary extreme learning machine because of low computational burden and easy implementation in hardware. Proposed algorithm has been successfully applied to problem of function approximation and classification application. Experimental results show that the algorithm increases the training speed greatly when dealing with large dataset and has better generalization performance compared to extreme learning machine, evolutionary extreme learning machine and Boosting ensemble extreme learning machine with quasi-Newton algorithms.

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