## Abstract

The limitations of the least squares based training algorithm is dominated by stalling problem and evaluation error by transformation matrix to obtain an unacceptable solution. This paper presents a new approach for the recurrent networks training algorithm based upon the Layer-by-Layer Least Squares based algorithm to overcome the aforementioned problems. In accordance with our proposed algorithm, all the weights are evaluated by the least squares method without the evaluation of transformation matrix to speed up the rate of convergence. A probabilistic mechanism, based upon the modified weights updated equations, is introduced to eliminate the stalling problem experienced by the pure least squares type computation. As a result, the merits of the proposed algorithm are capable of providing an ability of escaping from local minima to obtain a good optimal solution and still maintaining the characteristic of fast convergence.

Original language | English |
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Pages (from-to) | 15-25 |

Number of pages | 11 |

Journal | Neural Processing Letters |

Volume | 7 |

Issue number | 1 |

DOIs | |

Publication status | Published - 1998 |

Externally published | Yes |

## Keywords

- Convergence stalling
- Fast convergence speed
- Layer-by-Layer Least Squares algorithm
- Recurrent networks

## ASJC Scopus subject areas

- Software
- General Neuroscience
- Computer Networks and Communications
- Artificial Intelligence