Aiming at the problem of classification and recognition of power quality disturbance signals, this paper proposes an artificial bee colony algorithm to optimize the classifier model of the kernel extreme learning machine. First, the wavelet transform is used to extract the features of the power qualit...
Complex power quality disturbances (CPQDs) classification can be regarded as a typical application of multi-label (ML) learning. In this study, we propose a new recognition method for CPQDs based on S-transform (ST) and a hybrid kernel function-based extreme learning machine (ELM) for ML ...
In addition, CCGBFO was also used to tune the key parameters of kernel extreme learning machine for dealing with the real-world problems. The experimental results show that the proposed CCGBFO significantly outperforms the original BFO in terms of both convergence speed and solution accuracy....
Fast kernel extreme learning machine for ordinal regression Given a training set {(xi,ti)}i=1N, where xi=[xi1,xi2,…,xiD]⊤∈RD represents the ith input vector, ti∈T is the corresponding label, D is the number of input features, and T={C1,C2,…,Cq,…,CQ} is the ordered labe...
“Kernel incremental extreme learning machine”. The concepts of ICOA and IBSO were explained in detail in sections “Gaussian global best-growing operator” and “Tent mapping inverse learning. The implementation of the proposed hybrid intelligent optimization method was summed up in section “Dynamic...
A kernel-based extreme learning machine (KELM) model is developed in this study using MATLAB software in order to predict the performance, combustion and emission characteristics of the engine. To acquire the data for training and testing the KELM model, the engine speed was selected as the ...
Methods/Statistical analysis: Machine learning methods namely Kernel Logistic Regression (KLR) and Class... PB Thai,P Indra - 《Indian Journal of Science & Technology》 被引量: 2发表: 2018年 Bed load prediction in gravel-bed rivers using wavelet kernel extreme learning machine and meta-heuristic...
extreme speed on both small and large data sets, Bindings forR,Python,MATLAB / Octave,Java, andSpark, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, ...
7 as follows, which are obtained in seconds by MATLAB 2014a, 6 GB RAM and a 2.50 GHz CPU. Fig. 7 Running time with different algorithms (s). a Running time on the first five datasets (s). b Running time on the last four datasets (s) Full size image It can be seen in Fig. ...
Furthermore, previous research on machine learning or federated learning-based CE methods cannot account for the true label (channel matrix). In our work, we have addressed two main challenges in massive THz CE, i.e., the massive dual beamforming problem, (1) In the case of an ultra-...