### Abstract

In this paper, we propose a new method for inverting feedforward neural networks. Inversion of neural networks means to find the inputs which produce given outputs. In general, this is an ill-posed problem whose solution isn't unique. Inversion using iterative optimization method (for example gradient descent, quasi-Newton method) is useful to this problem and it is called "iterative inversion". We propose a new iterative inversion using a Bottleneck Neural Network with Hidden layer's input units (BNNH), which we design on the basis of Bottleneck Neural Network (BNN). Compressing input space by BNNH, we reduce the dimension of search space, or input space to be searched with iterative inversion. With reduction of the search space's dimension, performance about computation time and accuracy is expected to become better. In experiments, the proposed method is applied to some examples. These results show the effectively of the proposed method.

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

Pages (from-to) | 1612-1617 |

Number of pages | 6 |

Journal | Proceedings of the IEEE International Conference on Systems, Man and Cybernetics |

Volume | 2 |

Publication status | Published - 2003 Nov 24 |

Event | System Security and Assurance - Washington, DC, United States Duration: 2003 Oct 5 → 2003 Oct 8 |

### Keywords

- Bottleneck neural networks
- Ill-posed problem
- Inverse problem
- Iterative optimization method

### ASJC Scopus subject areas

- Control and Systems Engineering
- Hardware and Architecture

## Fingerprint Dive into the research topics of 'A new method for inverting feedforward neural networks'. Together they form a unique fingerprint.

## Cite this

*Proceedings of the IEEE International Conference on Systems, Man and Cybernetics*,

*2*, 1612-1617.