Hopfield proposed two types of neural networks; Discrete Hopfield Network(DHN) and Continuous Hopfield Network(CHN). Those have been used for solving the famous traveling salesman problem in a sense of optimization. DHN, a stochastic model is simple to implement and fast in computing, but it uses binary value for states of neurons resulting in an approximate solution. On the other hand, CHN gives a near-optimal solution. However, it takes too much time to simulate a differential equation which provides a main characteristic of CHN. A matching problem using a graph matching technique can be cast into an optimization problem. A new method for two-dimensional object recognition using a Hopfield neural network is proposed. A Hybrid Hopfield Network(HHN), which combines the merit of both the Continuous Hopfield Network and the Discrete Hopfield Network, is proposed and some of the advantages such as reliability and speed are shown in this paper. Stable states of neurons are analyzed and predicted based upon theory CHN after the convergence in DHN.
|Title of host publication||1992 IEEE International Symposium on Circuits and Systems, ISCAS 1992|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|Publication status||Published - 1992|
|Event||1992 IEEE International Symposium on Circuits and Systems, ISCAS 1992 - San Diego, United States|
Duration: 1992 May 10 → 1992 May 13
|Name||Proceedings - IEEE International Symposium on Circuits and Systems|
|Conference||1992 IEEE International Symposium on Circuits and Systems, ISCAS 1992|
|Period||92/5/10 → 92/5/13|
Bibliographical noteFunding Information:
Partially supported by the ARO grant no. DAAL03-90-0913
© 1992 IEEE.
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering