We propose a simple and effective method to build a meta-level Statistical Machine Translation (SMT), called meta-SMT, for system combination. Our approach is based on the framework of Stacked Generalization, also known as Stacking, which is an ensemble learning algorithm, widely used in machine learning tasks. First, a collection of base-level SMTs is generated for obtaining a meta-level corpu...

The scheduling and operation of power system becomes prominently complex and uncertain, especially with the penetration of distributed power. Load forecasting matters to the effective operation of power system. This paper proposes a novel deep learning framework to forecast the short-term grid load. First, the load data is processed by Box-Cox transformation, and two parameters (electricity pri...

Very important breakthroughs in data-centric machine learning algorithms led to impressive performance in ‘transactional’ point applications such as detecting anger in speech, alerts from a Face Recognition system, or EKG interpretation. Nontransactional applications, e.g. medical diagnosis beyond the EKG results, require AI algorithms that integrate deeper and broader knowledge in their proble...

Abstract Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. In this paper, we hope to present comprehensive review on ELM. Firstly, will focus the theoretical analysis including universal approximation theory generalization. Then, various improvem...

This paper proposes a data-driven approach for optimal power flow (OPF) based on the stacked extreme learning machine (SELM) framework. SELM has fast training speed and does not require time-consuming parameter tuning process compared with deep algorithms. However, direct application of OPF is tractable due to complicated relationship between system operating status solutions. To this end, regr...

Aiming at the problem that the three-phase APF’s dynamic model is a multi-variable, nonlinear and strong coupling system, an internal model controller for three-phase APF based on LS-Extreme Learning Machine is studied in this paper. As a novel single hidden layer feed-forward neural networks, extreme learning machine (ELM) has several advantages: simple net structural, fast learning speed, goo...