kernel extreme learning machineartificial intelligenceIn this study, four kernels extreme learning machines (KELM): radial basis function (RBELM), polynomial (POELM), wavelet (WKELM) and linear (LNELM) extreme learning machines, were compared for modelling monthly pan evaporation from Algerian dams ...
Keywords Classification Kernel Learning Semi-supervised Learning Multi-Kernel Learning Extreme Learning Machine View PDFReferences 1 Guang-Bin Huang, Qin-Yu Zhu and Chee-Kheong Siew, “Extreme Learning Machine: A New Learning Scheme of Feedforward Neural Networks”, IEEE 2004. Google Scholar 2 Akusok...
In this paper, we propose a kernel extreme learning machine (KELM) flatness recognition model based on particle swarm optimization (PSO). Compared with the extreme learning machine (ELM), the KELM has fewer initial parameters and better recognition performance. Next, the PSO algorithm can serve to...
Characterized by multi-labels cognitive learning, fast response, concept drifting and huge solution space, the multi-label learning becomes more complicated under data stream environment. To overcome the problems above, this paper proposes a multi-label algorithm based on kernel extreme learning machine...
To solve these problems, a new deep learning algorithm called deep kernel extreme learning machine (DKELM) is proposed by combining the extreme learning machine auto-encoder (ELM-AE) and kernel ELM (KELM). In the new DKELM architecture with M + 1 hidden layers, ELM-AEs are employed by ...
Extreme learning machine, which has recently lead to gain popularity of single hidden layer feed-forward neural networks, provides a key solution for non-linear problems with least norm and least square solutions at a very low run time. In this work, it is intended to increase the success of...
Therefore, this study constructs the dung beetle optimization algorithm to optimize the kernel extreme learning machine model. This paper addresses the issues of long iteration time and mismatches in kernel function mapping in data-driven models. To improve the model's generality, an adaptive learning...
Kernel extreme learning machineRandom forestEnsembleAutomatic heartbeat classification technology based on the ECG plays an important role in assisting doctors with arrhythmia diagnosis. While many heartbeat classification studies can achieve good performance under the intra-patient paradigm, they still cannot...
Given the fact that voltage instability is a local problem, the proposed approach combines the identification of voltage control areas (VCAs) with a Kernel Extreme Learning Machine (KELM) that is used to estimate the stability margin in each VCA using the magnitude of the voltage phasors. ...
2.2. Kernel Extreme Learning Machine KELM is a new variant of the well-regraded extreme learning machine (ELM) developed for the first time by Huang et al. [56]. The KELM integrates the kernel function with the structure of ELM to guarantee that the resulting network can reveal an acceptabl...