## Abstract

Universal Learning Networks(ULNs) which are super set of supervised learning networks have been already proposed. They consist of a number of inter-connected nodes where the nodes may have any continuously differentiable nonlinear functions in them. Most of the functions used are sigmoidal functions. Disadvantages of exiting ULNs mainly lie in that long training time, a large number of nodes in hidden layers, and so on. In this paper, special ULNs with multiplication neurons(M neuron) are proposed, which have M neurons in the hidden layer and normal neurons with sigmoidal functions in the output layer. The computational power of networks models with multiplication neurons is compared with that of ULNs with existing neurons. In particular it is proved that ULNs with multiplication neurons are, with regard to the number of neurons that are needed, computationally more powerful than ULNs with normal sigmodial functions.

Original language | English |
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Pages | 150-155 |

Number of pages | 6 |

Publication status | Published - 2001 Jan 1 |

Externally published | Yes |

Event | International Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States Duration: 2001 Jul 15 → 2001 Jul 19 |

### Conference

Conference | International Joint Conference on Neural Networks (IJCNN'01) |
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Country/Territory | United States |

City | Washington, DC |

Period | 01/7/15 → 01/7/19 |

## ASJC Scopus subject areas

- Software
- Artificial Intelligence