{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:29:28Z","timestamp":1777703368383,"version":"3.51.4"},"reference-count":35,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2017,1,13]],"date-time":"2017-01-13T00:00:00Z","timestamp":1484265600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2017,1,13]]},"abstract":"<jats:p>Since the traditional probabilistic neural network (PNN) cannot systematically solve the difficulty of estimating probability function and the high space complexity, this paper introduces backpropagation (BP) algorithm into the classical PNN. By designing appropriate error function and BP algorithm based on the steepest descent, an improved BP-PNN is presented, with its algorithm and effectiveness deduced. Three synthetic datasets and ten benchmark problems have been tested, compared with Probabilistic Neural Networks (PNN), Multi-Layered Perceptron (MLP) and Support Vector Machine (SVM). The results prove that (1) the accuracy of classification of BP-PNN is much higher than PNN, and it has a significant advantage compared with MLP and SVM; (2) BP-PNN has strong capacity to identify the importance of input indicators; (3) BP-PNN is a new pattern classification method to estimate the probabilistic function, reduce the space complexity and identify the importance of the indicators.<\/jats:p>","DOI":"10.3233\/jifs-151415","type":"journal-article","created":{"date-parts":[[2017,1,17]],"date-time":"2017-01-17T13:09:41Z","timestamp":1484658581000},"page":"215-227","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["A new probabilistic neural network model based on backpropagation algorithm"],"prefix":"10.1177","volume":"32","author":[{"given":"Qian","family":"Sun","sequence":"first","affiliation":[{"name":"School of Management, Harbin Institute of Technology, Harbin, China"},{"name":"School of Economy, Heilongjiang Institute of Science and Technology, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chong","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Management, Harbin Institute of Technology, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong-li","family":"Li","sequence":"additional","affiliation":[{"name":"School of Management, Harbin Institute of Technology, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2017,1,13]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICNN.1988.23887"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(90)90049-Q"},{"key":"e_1_3_2_4_2","first-page":"887","volume-title":"International Joint Conference on Neural Networks","author":"Specht D.F.","year":"1991","unstructured":"SpechtD.F. and ShapiroP.D., Generalization accuracy of probabilistic neural networks compared with back-propagation networks, in: International Joint Conference on Neural Networks, Seattle, Vol. 1, 1991, pp. 887\u2013892."},{"key":"e_1_3_2_5_2","first-page":"761","volume-title":"International Joint Conference on Neural Networks","author":"Specht D.F.","year":"1992","unstructured":"SpechtD.F., Enhancements to probabilistic neural networks, in: International Joint Conference on Neural Networks, Baltimore, Vol. 1, 1992, pp. 761\u2013768."},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2009.05.003"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2007.891635"},{"issue":"7","key":"e_1_3_2_8_2","first-page":"1074","article-title":"Cost-sensitive probabilistic neural network with its application in fault diagnosis","volume":"25","author":"Tang M.Z.","year":"2010","unstructured":"TangM.Z., YangC.H. and GuiW.H., Cost-sensitive probabilistic neural network with its application in fault diagnosis, Control and Decision 25(7) (2010), 1074\u20131079.","journal-title":"Control and Decision"},{"issue":"25","key":"e_1_3_2_9_2","first-page":"1277","article-title":"Hybridization of the probabilistic neural networks with feed-forward neural networks for forecasting","author":"Mehdi K.","year":"2012","unstructured":"MehdiK. and MehdiB., Hybridization of the probabilistic neural networks with feed-forward neural networks for forecasting, Engineering Applications of Artificial Intelligence (25) (2012), 1277\u20131288.","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"5","key":"e_1_3_2_10_2","first-page":"43","article-title":"Study on credit scoring model and forecasting based on probabilistic neural network","volume":"25","author":"Pang S.L.","year":"2005","unstructured":"PangS.L., Study on credit scoring model and forecasting based on probabilistic neural network, Systems Engineering-Theory & Practice 25(5) (2005), 43\u201348.","journal-title":"Systems Engineering-Theory & Practice"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.3724\/SP.J.1087.2013.00353"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2013.06.003"},{"key":"e_1_3_2_13_2","volume-title":"Statistical decision theory and bayesian analysis","author":"James O.B.","year":"1985","unstructured":"JamesO.B., Statistical decision theory and bayesian analysis, Spring-Verlag, New York, 1985."},{"issue":"9","key":"e_1_3_2_14_2","first-page":"737","article-title":"A K-nearest neighbor classification rule based on Dempster-Shafer theory","volume":"21","author":"Thierry D.","year":"2008","unstructured":"ThierryD., A K-nearest neighbor classification rule based on Dempster-Shafer theory, Studies in Fuzziness and Soft Computing 21(9) (2008), 737\u2013760.","journal-title":"Studies in Fuzziness and Soft Computing"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0148-2963(97)00242-7"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0925-2312(97)00063-5"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2007.01.017"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRD.2007.911125"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11235-008-9138-5"},{"issue":"1","key":"e_1_3_2_20_2","first-page":"141","article-title":"PCA-based probability neural network structure optimization","volume":"48","author":"Xing J.","year":"2008","unstructured":"XingJ. and XiaoD.Y., PCA-based probability neural network structure optimization, Journal of Tsinghua University (Science and Technology) 48(1) (2008), 141\u2013144.","journal-title":"Journal of Tsinghua University (Science and Technology)"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2003.12.105"},{"key":"e_1_3_2_22_2","first-page":"1581","volume-title":"IEEE International Conference on Systems, Man, and Cybernetics","author":"Lee D.C.","year":"1991","unstructured":"LeeD.C., KimB.J. and YunJ.M., A combined method for improving backpropagation algorithm, in: IEEE International Conference on Systems, Man, and Cybernetics, Charlottesville, Vol. 1-3 1991, pp. 1581\u20131586."},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0925-2312(01)00700-7"},{"issue":"2","key":"e_1_3_2_24_2","first-page":"171","article-title":"Improving the back-propagation algorithm Using Evolutionary Strategy","volume":"52","author":"Sammy S.","year":"2007","unstructured":"SammyS., YangS.S. and LeeC.M., Improving the back-propagation algorithm Using Evolutionary Strategy, IEEE Transactions on Circuits and Systems II-Express Briefs 52(2) (2007), 171\u2013175in.","journal-title":"IEEE Transactions on Circuits and Systems II-Express Briefs"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-013-1166-8"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.220.4598.671"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/72.737492"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0925-2312(99)00127-7"},{"issue":"3","key":"e_1_3_2_29_2","first-page":"375","article-title":"Bottom hole pressure estimation using evolved neural networks by real coded ant colony optimization and genetic algorithm","volume":"77","author":"Rahman A.","year":"2011","unstructured":"RahmanA. and JamshidM., Bottom hole pressure estimation using evolved neural networks by real coded ant colony optimization and genetic algorithm, Journal of Petroleum Science and Engineering 77(3) (2011), 375\u2013385.","journal-title":"Journal of Petroleum Science and Engineering"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2012.10.008"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrefrig.2013.06.014"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2010.06.016"},{"key":"e_1_3_2_33_2","volume-title":"32nd International Conference on Machine Learning","author":"Hern\u00e1ndez-Lobato J.M.","year":"2015","unstructured":"Hern\u00e1ndez-LobatoJ.M. and RyanP.A., Probabilistic backpropagation for scalable learning of bayesian neural networks, in: 32nd International Conference on Machine Learning, Lille, France, JMLR: W&CP, Vol. 37, 2015."},{"key":"e_1_3_2_34_2","volume-title":"Optimization Theory and Method","author":"Xue J.Q.","year":"2003","unstructured":"XueJ.Q., Optimization Theory and Method, Metallurgy Industry Press, Beijing, 2003."},{"key":"e_1_3_2_35_2","volume-title":"Principles of Neurocomputing for Science & Engineering","author":"Fredric M.H.","year":"2003","unstructured":"FredricM.H. and IvicaK., Principles of Neurocomputing for Science & Engineering, McGraw-Hill Companies, New York, 2003."},{"key":"e_1_3_2_36_2","unstructured":"FrankA. and AsuncionA. UCI machine learning repository [http:\/\/archive.ics.uci.edu\/ml] School of Information and Computer Science University of California Irvine CA 2010."}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-151415","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-151415","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-151415","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:38:34Z","timestamp":1777455514000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-151415"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,1,13]]},"references-count":35,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2017,1,13]]}},"alternative-id":["10.3233\/JIFS-151415"],"URL":"https:\/\/doi.org\/10.3233\/jifs-151415","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,1,13]]}}}