### Abstract

Recently, Watanabe et al. proposed a back propagation algorithm via the extended Kalman filter, in which the learning rate was time-varying. In their algorithm the weights and biases are treated as independent variables. It is, however, natural that the weights and biases are not always independent, and generally have mutual correlation. In this paper, we improve the back propagation algorithm by considering that there is mutual correlation among the weights and bias directly connected to the unit. Through some numerical examples, our improved learning algorithm is compared with Watanabe et al.'s algorithm in learning ability. Furthermore, we consider demand forecasting as a kind of pattern recognition, and propose a demand forecasting method using layered neural networks with the improved learning algorithm. The effectiveness of this demand forecasting method is also discussed through some simulations.

Original language | English |
---|---|

Pages (from-to) | 473-482 |

Number of pages | 10 |

Journal | International Journal of Systems Science |

Volume | 27 |

Issue number | 5 |

Publication status | Published - 1996 |

Externally published | Yes |

### Fingerprint

### ASJC Scopus subject areas

- Computational Theory and Mathematics
- Management Science and Operations Research
- Control and Systems Engineering
- Theoretical Computer Science

### Cite this

*International Journal of Systems Science*,

*27*(5), 473-482.

**An improvement of a back propagation algorithm by the extended Kalman filter and demand forecasting by layered neural networks.** / Kimura, Aritoshi; Arizono, Ikuo; Ohta, Hiroshi.

Research output: Contribution to journal › Article

*International Journal of Systems Science*, vol. 27, no. 5, pp. 473-482.

}

TY - JOUR

T1 - An improvement of a back propagation algorithm by the extended Kalman filter and demand forecasting by layered neural networks

AU - Kimura, Aritoshi

AU - Arizono, Ikuo

AU - Ohta, Hiroshi

PY - 1996

Y1 - 1996

N2 - Recently, Watanabe et al. proposed a back propagation algorithm via the extended Kalman filter, in which the learning rate was time-varying. In their algorithm the weights and biases are treated as independent variables. It is, however, natural that the weights and biases are not always independent, and generally have mutual correlation. In this paper, we improve the back propagation algorithm by considering that there is mutual correlation among the weights and bias directly connected to the unit. Through some numerical examples, our improved learning algorithm is compared with Watanabe et al.'s algorithm in learning ability. Furthermore, we consider demand forecasting as a kind of pattern recognition, and propose a demand forecasting method using layered neural networks with the improved learning algorithm. The effectiveness of this demand forecasting method is also discussed through some simulations.

AB - Recently, Watanabe et al. proposed a back propagation algorithm via the extended Kalman filter, in which the learning rate was time-varying. In their algorithm the weights and biases are treated as independent variables. It is, however, natural that the weights and biases are not always independent, and generally have mutual correlation. In this paper, we improve the back propagation algorithm by considering that there is mutual correlation among the weights and bias directly connected to the unit. Through some numerical examples, our improved learning algorithm is compared with Watanabe et al.'s algorithm in learning ability. Furthermore, we consider demand forecasting as a kind of pattern recognition, and propose a demand forecasting method using layered neural networks with the improved learning algorithm. The effectiveness of this demand forecasting method is also discussed through some simulations.

UR - http://www.scopus.com/inward/record.url?scp=0030141962&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0030141962&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:0030141962

VL - 27

SP - 473

EP - 482

JO - International Journal of Systems Science

JF - International Journal of Systems Science

SN - 0020-7721

IS - 5

ER -