The relationships between the fatigue crack growth rate ( d a / d N ) and stress intensity factor range ( Δ K ) are not always linear even in the Paris region. The stress ratio effects on fatigue crack growth rate are diverse in different materials. However, most existing fatigue crack growth models cannot handle these nonlinearities appropriately. The machine learning method provides a flexibl...

BACKGROUND Elastin is a signature protein of the arteries and lungs, thus it was hypothesized that elastin is subject to enzymatic degradation during cardiovascular and pulmonary diseases. The aim was to investigate if different fragments of the same protein entail different information associated to two different diseases and if these fragments have the potential of being diagnostic biomarkers...

Extreme Learning Machine (ELM) algorithm assigns the input weights and biases in a “one-time stamp” fashion, this method makes to be ill-conditioned reduces its classification accuracy. The contribution of work is enhancement performance ELM with Moth-Flame Optimization (MFO) improve A hybrid (MFO-ELM) implemented MATLAB. MFO ensures concurrent simulation exploration exploitation search space s...

This paper proposes a method which is the advanced modification of the original Extreme Learning Machine with a new tool to solve the missing data problem. It uses a cascade of L1 penalty (LARS) and L2 penalty (Tikhonov regularization) on ELM to regularize the matrix computations and hence make the MSE computation more reliable, and on the other hand, it estimates the expected pairwise distance...

in this paper, a neural network model reference adaptive system speed observer is designed, which can be used in speed control of linear induction motors (lims). dynamical equations of lim have been considered accurate. in other words, the end effect and the electrical losses of the motor have been included in the motor equivalent circuit. then equations of the reference model and adaptive mode...

This paper investigates the construction of linear-in-the-parameters (LITP) models for multi-output regression problems. Most existing stepwise forward algorithms choose the regressor terms one by one, each time maximizing the model error reduction ratio. The drawback is that such procedures cannot guarantee a sparse model, especially under highly noisy learning conditions. The main objective o...

This paper proposes a learning framework for single-hidden layer feedforward neural networks (SLFN) called optimized extreme learning machine (O-ELM). In O-ELM, the structure and the parameters of the SLFN are determined using an optimization method. The output weights, like in the batch ELM, are obtained by a least squares algorithm, but using Tikhonov’s regularization in order to improve the ...