{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T09:43:18Z","timestamp":1749721398938},"reference-count":30,"publisher":"Elsevier BV","issue":"3","license":[{"start":{"date-parts":[[1998,4,1]],"date-time":"1998-04-01T00:00:00Z","timestamp":891388800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neural Networks"],"published-print":{"date-parts":[[1998,4]]},"DOI":"10.1016\/s0893-6080(97)00151-2","type":"journal-article","created":{"date-parts":[[2002,7,25]],"date-time":"2002-07-25T18:54:47Z","timestamp":1027623287000},"page":"535-547","source":"Crossref","is-referenced-by-count":9,"title":["Effective learning in recurrent max\u2013min neural networks"],"prefix":"10.1016","volume":"11","author":[{"given":"Loo-Nin","family":"Teow","sequence":"first","affiliation":[]},{"given":"Kia-Fock","family":"Loe","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/S0893-6080(97)00151-2_BIB1","unstructured":"Aleksander, I. & Morton, H.B. (1993). Neurons and symbols. London: Chapman and Hall."},{"issue":"2","key":"10.1016\/S0893-6080(97)00151-2_BIB2","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/72.279181","article-title":"Learning long-term dependencies with gradient descent is difficult","volume":"5","author":"Bengio","year":"1994","journal-title":"IEEE Transactions on Neural Networks"},{"key":"10.1016\/S0893-6080(97)00151-2_BIB3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0165-0114(94)90297-6","article-title":"Fuzzy neural networks: a survey","volume":"66","author":"Buckley","year":"1994","journal-title":"Fuzzy Sets and Systems"},{"key":"10.1016\/S0893-6080(97)00151-2_BIB4","unstructured":"Carroll, J. & Long, D. (1989). Theory of finite automata\u2014with an introduction to formal languages. London: Prentice-Hall."},{"issue":"6","key":"10.1016\/S0893-6080(97)00151-2_BIB5","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1162\/neco.1996.8.6.1135","article-title":"The dynamics of discrete-time computation, with application to recurrent neural networks and finite state machine extraction","volume":"8","author":"Casey","year":"1996","journal-title":"Neural Computation"},{"issue":"3","key":"10.1016\/S0893-6080(97)00151-2_BIB6","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1162\/neco.1989.1.3.372","article-title":"Finite state automata and simple recurrent neural networks","volume":"1","author":"Cleeremans","year":"1989","journal-title":"Neural Computations"},{"key":"10.1016\/S0893-6080(97)00151-2_BIB7","unstructured":"Das, S. & Mozer, M. (1994). A unified gradient-descent\/clustering architecture for finite state machine induction. In J. Cowan, G. Tesauro & J. Alspector (Eds.), Advances in neural information processing systems 6, pp. 19\u201326. San Mateo, CA: Morgan Kaufmann."},{"key":"10.1016\/S0893-6080(97)00151-2_BIB8","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1162\/neco.1995.7.5.923","article-title":"Learning the initial state of a second-order recurrent neural network during regular-language inference","volume":"7","author":"Forcada","year":"1995","journal-title":"Neural Computation"},{"key":"10.1016\/S0893-6080(97)00151-2_BIB9","unstructured":"Fulks, W. (1981). Advanced calculus\u2014an introduction to analysis, 3rd ed. New York: Wiley Trans-Edition."},{"issue":"9","key":"10.1016\/S0893-6080(97)00151-2_BIB10","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1016\/0893-6080(95)00041-0","article-title":"Learning a class of large finite state machines with a recurrent neural network","volume":"8","author":"Giles","year":"1995","journal-title":"Neural Networks"},{"key":"10.1016\/S0893-6080(97)00151-2_BIB11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0165-0114(94)90279-8","article-title":"On the principles of fuzzy neural networks","volume":"61","author":"Gupta","year":"1994","journal-title":"Fuzzy Sets and Systems"},{"issue":"2","key":"10.1016\/S0893-6080(97)00151-2_BIB12","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1109\/91.227388","article-title":"Neural networks that learn from fuzzy IF\u2013THEN rules","volume":"1","author":"Ishibuchi","year":"1993","journal-title":"IEEE Transactions on Fuzzy Systems"},{"key":"10.1016\/S0893-6080(97)00151-2_BIB13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0165-0114(92)90086-J","article-title":"Neural network implementation of fuzzy logic","volume":"45","author":"Keller","year":"1992","journal-title":"Fuzzy Sets and Systems"},{"key":"10.1016\/S0893-6080(97)00151-2_BIB14","doi-asserted-by":"crossref","unstructured":"Khan, E. & Unal, F. (1995). Recurrent fuzzy logic using neural networks. In T. Furuhashi (Ed.), Advances in fuzzy logic, neural networks, and genetic algorithms\u2014lecture notes in artificial intelligence. Berlin: Springer.","DOI":"10.1007\/3-540-60607-6_4"},{"key":"10.1016\/S0893-6080(97)00151-2_BIB15","unstructured":"Klir, G.J. & Folger, T.A. (1988). Fuzzy sets, uncertainty, and information. London: Prentice-Hall."},{"key":"10.1016\/S0893-6080(97)00151-2_BIB16","unstructured":"Lang, K. (1992). Random DFAs can be approximately learned from sparse uniform examples. In D. Haussler (Ed.), Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory (pp. 45\u201352). New York: ACM Press."},{"key":"10.1016\/S0893-6080(97)00151-2_BIB17","doi-asserted-by":"crossref","unstructured":"Miller, S. & Giles, C.L. (1993). Experimental comparison of the effect of order in recurrent neural networks. In I. Guyon, and P.S.P. Wang (Eds.), Advances in pattern recognition systems using neural network technologies (pp. 205\u2013228). Singapore: World Scientific.","DOI":"10.1142\/S0218001493000431"},{"issue":"2","key":"10.1016\/S0893-6080(97)00151-2_BIB18","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/0893-6080(94)90029-9","article-title":"Logical operation based fuzzy MLP for classification and rule generation","volume":"7","author":"Mitra","year":"1994","journal-title":"Neural Networks"},{"issue":"1","key":"10.1016\/S0893-6080(97)00151-2_BIB19","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/0893-6080(95)00086-0","article-title":"Extraction of rules from discrete-time recurrent neural network","volume":"9","author":"Omlin","year":"1995","journal-title":"Neural Networks"},{"issue":"6","key":"10.1016\/S0893-6080(97)00151-2_BIB20","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1145\/235809.235811","article-title":"Constructing deterministic finite-state automata in recurrent neural networks","volume":"43","author":"Omlin","year":"1996","journal-title":"Journal of the ACM"},{"key":"10.1016\/S0893-6080(97)00151-2_BIB21","doi-asserted-by":"crossref","unstructured":"Omlin, C.W., Thornber, K.K. & Giles, C.L. (1996). Representation of fuzzy finite state automata in continuous recurrent neural networks. In B.J. Shen (Ed.), Proceedings of the IEEE International Conference on Neural networks (pp. 1023\u20131028). Piscataway, NJ: IEEE Press.","DOI":"10.1109\/ICNN.1996.549038"},{"key":"10.1016\/S0893-6080(97)00151-2_BIB22","doi-asserted-by":"crossref","unstructured":"Omlin C.W., Thornber K.K., & Giles C.L. (1998). Fuzzy finite-state automata can be deterministically encoded into recurrent neural networks. IEEE Transactions on Fuzzy Systems (to be published).","DOI":"10.1109\/91.660809"},{"key":"10.1016\/S0893-6080(97)00151-2_BIB23","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1162\/neco.1989.1.2.263","article-title":"Learning state space trajectories in recurrent neural networks","volume":"1","author":"Pearlmutter","year":"1989","journal-title":"Neural Computation"},{"key":"10.1016\/S0893-6080(97)00151-2_BIB24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0165-0114(93)90181-G","article-title":"Fuzzy neural networks and neurocomputations","volume":"56","author":"Pedrycz","year":"1993","journal-title":"Fuzzy Sets and Systems"},{"key":"10.1016\/S0893-6080(97)00151-2_BIB25","unstructured":"Rumelhart, D.E., Hinton, G.E. & Williams, R.J. (1986). Learning internal representations by error propagation. In D.E. Rumelhart & J.L. McClelland (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Vol. 1: Foundations, pp. 318\u2013362. Cambridge, MA: MIT Press."},{"issue":"5","key":"10.1016\/S0893-6080(97)00151-2_BIB26","doi-asserted-by":"crossref","first-page":"776","DOI":"10.1109\/72.159066","article-title":"Fuzzy min\u2013max neural networks\u2014Part 1: classifications","volume":"3","author":"Simpson","year":"1992","journal-title":"IEEE Transactions on Neural Networks"},{"issue":"1","key":"10.1016\/S0893-6080(97)00151-2_BIB27","first-page":"32","article-title":"Fuzzy min\u2013max neural networks\u2014Part 2: clustering","volume":"4","author":"Simpson","year":"1993","journal-title":"IEEE Transactions on Neural Networks"},{"key":"10.1016\/S0893-6080(97)00151-2_BIB28","unstructured":"Teow, L. & Loe, K. (1997). An effective learning method for max\u2013min neural networks. In M.E. Pollack (Ed.), Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI-97), Vol. 2, pp. 1134\u20131139. Denver, CO: Professional Book Center."},{"key":"10.1016\/S0893-6080(97)00151-2_BIB29","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1162\/neco.1989.1.2.270","article-title":"A learning algorithm for continually running fully recurrent neural networks","volume":"1","author":"Williams","year":"1989","journal-title":"Neural Computation"},{"key":"10.1016\/S0893-6080(97)00151-2_BIB30","doi-asserted-by":"crossref","first-page":"976","DOI":"10.1162\/neco.1993.5.6.976","article-title":"Learning finite state machines with self-clustering recurrent networks","volume":"5","author":"Zeng","year":"1993","journal-title":"Neural Computation"}],"container-title":["Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608097001512?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608097001512?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2019,4,18]],"date-time":"2019-04-18T13:27:17Z","timestamp":1555594037000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0893608097001512"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[1998,4]]},"references-count":30,"journal-issue":{"issue":"3","published-print":{"date-parts":[[1998,4]]}},"alternative-id":["S0893608097001512"],"URL":"https:\/\/doi.org\/10.1016\/s0893-6080(97)00151-2","relation":{},"ISSN":["0893-6080"],"issn-type":[{"value":"0893-6080","type":"print"}],"subject":[],"published":{"date-parts":[[1998,4]]}}}