Charnon Chupong, Boonyang Plangklang^*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5415-5427, 2022, DOI:10.32604/cmc.2022.028416 - 21 April 2022

Abstract This article presents hourly load forecasting by using an incremental learning model called Online Sequential Extreme Learning Machine (OS-ELM), which can learn and adapt automatically according to new arrival input. However, the use of OS-ELM requires a sufficient amount of initial training sample data, which makes OS-ELM inoperable if sufficiently accurate sample data cannot be obtained. To solve this problem, a synthesis of the initial training sample is proposed. The synthesis of the initial sample is achieved by taking the first data received at the start of working and adding random noises to that data More >

Yajie Sun^1,2,*, Yanqing Yuan², Qi Wang², Sai Ji^1,2, Lihua Wang³, Shaoen Wu⁴, Jie Chen², Qin Zhang²

Journal of Quantum Computing, Vol.1, No.1, pp. 1-8, 2019, DOI:10.32604/jqc.2019.05788

Abstract A method is proposed based on the transmissibility function and the Online Sequence Extreme Learning Machine (OS-ELM) algorithm, which is applied to the impact damage of composite materials. First of all, the transmissibility functions of the undamaged signals and the damage signals at different points are calculated. Secondly, the difference between them is taken as the damage index. Finally, principal component analysis (PCA) is used to reduce the noise feature. And then, input to the online sequence limit learning neural network classification to identify damage and confirm the damage location. Taking the amplitude of the More >