An Intelligent Quick Prediction Algorithm With Applications in Industrial Control and Loading Problems

Yong Yu

doi:10.1109/TASE.2011.2173800

An Intelligent Quick Prediction Algorithm With Applications in Industrial Control and Loading Problems

2012-04-01

31

PRE

AI

DOI：10.1109/TASE.2011.2173800

原文链接

原文求助

摘要

En 中文

The Artificial Neural Network (ANN) and its variations have been well-studied for their applications in the prediction of industrial control and loading problems. Despite showing satisfactory performance in terms of accuracy, the ANN models are notorious for being slow compared to, e.g., the traditional statistical models. This substantially hinders ANN model's real-world applications in control and loading prediction problems. Recently a novel learning approach of ANN called Extreme Learning Machine (ELM) has emerged and it is proven to be very fast compared with the traditional ANN. In this paper, an Intelligent Quick Prediction Algorithm (IQPA), which employs an extended ELM (ELME) in producing fast, stable, and accurate prediction results for control and loading problems, is devised. This algorithm is versatile in which it can be used for short, medium to long-term predictions with both time series and non-time series data. Publicly available power plant operations and aircraft control data are employed for conducting analysis with this proposed novel model. Experimental results show that IQPA is effective and efficient, and can finish the prediction task with accurate results within a prespecified time limit. Note to Practitioners-Forecasting is a crucial part for many control and loading problems. Despite the fact that there is no perfect forecast, forecasting for highly structured data (e.g., the time series with high seasonality or trend) is known to be easy because there are many well-established models which provide the needed analytical formulations. However, for many real-life control applications, the data patterns are notorious for being highly volatile and it is very difficult to analytically learn about the underlining pattern and hence the well-established statistical methods will fail to make a sound prediction for them. As a result, recent advances of artificial intelligence (AI) technologies have offered an alternative way of providing precise and more accurate forecasting result. Although AI methods can produce highly accurate forecasting results, they suffer a major drawback in which they are slow. This shortcoming becomes a major barricade which hinders the application of AI methods for conducting forecasting for control problems in realworld. In this paper, an Intelligent Quick Prediction Algorithm (IQPA) is developed. With publicly available real datasets, we conducted computational experiments to show that the IQPA is versatile and it can finish the prediction task with accurate results within a prespecified time limit.

Keyword:

Hybrid model

quick intelligent prediction

An Intelligent Quick Prediction Algorithm With Applications in Industrial Control and Loading Problems

摘要

AI总结

期刊

机构

引用论文