{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T06:05:17Z","timestamp":1747893917380},"reference-count":0,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[1992,1]]},"abstract":"<jats:p> Suggested here is a neural net algorithm for the n-queens problem. The net is basically a Hopfield net but with one major difference: every unit is allowed to inhibit itself. This distinctive characteristic enables the net to escape efficiently from all local minima. The net\u2019s dynamics then can be described as a travel in paths of low-level energy spaces until it finds a solution (global minimum). The paper explains why standard Hopfield nets have failed to solve the queens problem and proofs that the self-inhibiting net (NQ2 algorithm in the text) never stabilizes in local minima and relaxes when it falls into a global minimum are provided. The experimental results supported by theoretical explanation indicate that the net never continually oscillates but relaxes into a solution in polynomial time. In addition, it appears that the net solves the queens problem regardless of the dimension n or the initialized values. The net uses only few parameters to fix the weights; all globally determined as a function of n. <\/jats:p>","DOI":"10.1142\/s0129065792000206","type":"journal-article","created":{"date-parts":[[2004,11,24]],"date-time":"2004-11-24T03:29:42Z","timestamp":1101266982000},"page":"249-252","source":"Crossref","is-referenced-by-count":8,"title":["A NEURAL NET WITH SELF-INHIBITING UNITS FOR THE N-QUEENS PROBLEM"],"prefix":"10.1142","volume":"03","author":[{"given":"ORON","family":"SHAGRIR","sequence":"first","affiliation":[{"name":"Philosophy Department and Cognitive Science Program, University of California, San Diego, USA"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"container-title":["International Journal of Neural Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0129065792000206","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T01:53:55Z","timestamp":1565142835000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0129065792000206"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[1992,1]]},"references-count":0,"journal-issue":{"issue":"03","published-online":{"date-parts":[[2011,11,21]]},"published-print":{"date-parts":[[1992,1]]}},"alternative-id":["10.1142\/S0129065792000206"],"URL":"https:\/\/doi.org\/10.1142\/s0129065792000206","relation":{},"ISSN":["0129-0657","1793-6462"],"issn-type":[{"value":"0129-0657","type":"print"},{"value":"1793-6462","type":"electronic"}],"subject":[],"published":{"date-parts":[[1992,1]]}}}