| タイトル | Machine parts recognition using a trinary associative memory |
| 著者（英） | Karim, Mohammad A.; Liu, Hua-Kuang; Awwal, Abdul Ahad S. |
| 著者所属（英） | Jet Propulsion Lab., California Inst. of Tech.|Dayton Univ. |
| 発行日 | 1989-05-01 |
| 言語 | eng |
| 内容記述 | The convergence mechanism of vectors in Hopfield's neural network in relation to recognition of partially known patterns is studied in terms of both inner products and Hamming distance. It has been shown that Hamming distance should not always be used in determining the convergence of vectors. Instead, inner product weighting coefficients play a more dominant role in certain data representations for determining the convergence mechanism. A trinary neuron representation for associative memory is found to be more effective for associative recall. Applications of the trinary associative memory to reconstruct machine part images that are partially missing are demonstrated by means of computer simulation as examples of the usefulness of this approach. |
| NASA分類 | CYBERNETICS |
| レポートNO | 89A41147 |
| 権利 | Copyright |
| URI | https://repository.exst.jaxa.jp/dspace/handle/a-is/363242 |
