RestrictedBoltzmannBased Extreme Learning Machine for Gas Path Fault Diagnosis of Turbofan Engine Extreme learning machine (ELM) owns the advantages of less computational efforts and simple topology with singlehidden layer structure. However, the performance of plain ELM is sensitive to the input weights, bias, and the number of hidden neurons; and the former two are randomly generated. This paper develops a restricted Boltzmann strategy combined with Moorexe2x80x93Penrose generalized inverse to learn topological parameters in both input and output layers. A novel extreme learning model based on the restricted Boltzmann ELM, constructs a feature mapping and recursively tune the weights between input neurons and hidden neurons. The contribution of this paper is to provide a simple ELM topological network to handle low dimensionality problem with the merit of better accuracy and stability. The proposed methodology is evaluated on University of California Irvine (UCI) benchmark datasets for classification issue, and then extended to gas path fault diagnosis for a turbofan engine. The experimental results confirm the superiority to plain ELM.

Constrained StatePreserved Extreme Learning Machine Reducing the training time for neural networks is a primary focus of research in the field of machine learning. Currently, the LevenbergMarquardt (LM) method is one of the fastest backpropagation methods. A recently popular alternative to LM backpropagation is the Extreme Learning Machine (ELM), which produces a closed form optimization of a Single Layer Feed Forward Network for an initially randomized input weight matrix. In this study, we further extend the performance of an ELM by incrementally building on the state of the art, the StatePreserved ELM (SPELM), to produce a Constrained SPELM (CSPELM). To do so, we introduce a constraint, , which randomly perturbs the input weight matrix after each training cycle, providing a honing mechanism during the search for a better local optimum. We evaluated CSPELM against 13 benchmark datasets, both categorical and continuous. For 8 of the 13 benchmark datasets, CSPELM outperformed, with respect to average accuracy and RMSE, the ELM, SPELM, and LM methods. Further, the results show that in 8 of the 13 benchmark datasets used, CSPELM was the best performing model and only reached a maximum of 195.10 seconds total training time in one example. The results show a more consistent and higher accuracy than the ELM and SPELM and competitive or better results with LM with training time being only approximately 10% of traditional LM backpropagation training time.