Online condition prediction of avionic devices based on sparse kernel incremental extreme learning machine

In order to achieve the online condition prediction for avionic devices, a sparse kernel incremental extreme learning machine (ELM) algorithm is presented. For the problem of Gram matrix expansion in kernel online learning algorithms, a novel sparsification rule is presented by measuring the instantaneous learnable information contained on a data sample for dictionary selection. The proposed sparsification method combines the constructive strategy and the pruning strategy in two stages. By minimizing the redundancy of dictionary in the constructive phase and maximizing the instantaneous conditional self-information of dictionary atoms in the pruning phase, a compact dictionary with predefined size can be selected adaptively. For the kernel weight updating of kernel based incremental ELM, an improved decremental learning algorithm is proposed by using matrix elementary transformation and block matrix inversion formula, which effectively moderate the computational complexity at each iteration.In proposed algorithm, the inverse matrix of Gram matrix of the other samples can be directly updated after one sample is deleted from previous dictionary. The experimental results of the aero-engine condition prediction show that the proposed method can make the whole average error rate reduce to 2.18% when the prediction step is equal to 20. Compared with three well-known kernel ELM online learning algorithms, the prediction accuracy is improved by 0.72%, 0.14% and 0.13% respectively.