| Affiliation: | (1) Department of Production Control Technology, Division of Engineering Systems and Technology, Graduate School of Science and Engineering, Saga University, 1 Honjo-machi, Saga 840-8502, Japan;(2) Department of Advanced Systems Control Engineerig, Graduate School of Science and Engineering, Saga University, Saga, Japan;(3) Present address: Department of Mechanical Engineering, Mindanao State University, Marawi, Philippines |
| Abstract: | In this article, we investigate the viability of our proposed neural network-based extension of the  perception control concept introduced by Randløv and Alstrøm. In their work, each of the expectation elements is linearly acquired such that the expectation gives only the dominant information of the recent past. This handicap could become a serious problem when the perception process is applied to real physical systems. Such an approach has no capability to sense the trend or the dynamics in the information. Here, we introduce an extension of the perception control process by using a radial basis function feedforward neural network to learn the trend and the dynamics in the information queue. Through our simulations, we show that our neural network-based method is better than the conventional method.This work was presented, in part, at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003 |
