## Abstract

In this paper, a new learning law for one-dimensional topology preserving neural networks is presented in which the output weights of the neural network converge to a set that produces a predefined winning neuron coordinate probability distribution, when the probability density function of the input signal is unknown and not necessarily uniform. The learning algorithm also produces an orientation preserving homeomorphic function from the known neural coordinate domain to the unknown input signal space, which maps a predefined neural coordinate probability density function into the unknown probability density function of the input signal. The convergence properties of the proposed learning algorithm are analyzed using the ODE approach and verified by a simulation study.

Original language | English |
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Article number | WeC06.4 |

Pages (from-to) | 1343-1350 |

Number of pages | 8 |

Journal | Proceedings of the American Control Conference |

Volume | 2 |

Publication status | Published - 2005 |

Event | 2005 American Control Conference, ACC - Portland, OR, United States Duration: 2005 Jun 8 → 2005 Jun 10 |

## All Science Journal Classification (ASJC) codes

- Electrical and Electronic Engineering