{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T03:46:44Z","timestamp":1769831204977,"version":"3.49.0"},"reference-count":30,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,7,21]]},"abstract":"<jats:p>Aiming at the problem that fuzzy neural network (FNN) is difficult to be adjusted automatically its structure when there is no the threshold of loss function, as well as the problem that the neuron number of the regularization layer of FNN is adjusted by self-organizing algorithm when the structure of FNN is not stable yet, a structural design strategy of self-organizing recursive FNN based on the Boston matrix (SORFNN-BOSTON) is proposed. Compared with other self-organizing algorithms, the method used in this paper does not need to set the threshold of loss function. In addition to the indicators representing the importance of neurons in most self-organizing algorithms, the change rate is used to represent the change of the parameters of the neural network. The change rate is used to determine when the relevant parameters are stable, which further improves the reliability of the neuron adjustment process. Through the simulation of predicting Mackey-Glass time sequence, the final number of neurons in the hidden layer and the testing error are 6 and 0.110 respectively. Comparisons with other self-organizing algorithms show that the testing error decreased by 76.6% at most and 13.3% at least, which proves the practicability of the method.<\/jats:p>","DOI":"10.3233\/jifs-213461","type":"journal-article","created":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T12:18:07Z","timestamp":1652185087000},"page":"3239-3249","source":"Crossref","is-referenced-by-count":0,"title":["Improving self-organizing recursive fuzzy neural network\u2019s performance with Boston matrix"],"prefix":"10.1177","volume":"43","author":[{"given":"Shuaishuai","family":"Yang","sequence":"first","affiliation":[{"name":"School of Information and Control Engineering, Liaoning Petrochemical University, Fushun, China"}]},{"given":"Qiumei","family":"Cong","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, Liaoning Petrochemical University, Fushun, 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1999."},{"issue":"6","key":"10.3233\/JIFS-213461_ref3","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1162\/neco.1993.5.6.954","article-title":"A function estimation approach to sequential learning with neural networks[J]","volume":"5","author":"Kadirkamanathan","year":"1993","journal-title":"Neural computation"},{"issue":"2","key":"10.3233\/JIFS-213461_ref4","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1162\/neco.1991.3.2.213","article-title":"A resource-allocating network for function interpolation[J]","volume":"3","author":"Platt","year":"1991","journal-title":"Neural Computation"},{"issue":"16-18","key":"10.3233\/JIFS-213461_ref5","doi-asserted-by":"crossref","first-page":"3818","DOI":"10.1016\/j.neucom.2009.05.006","article-title":"A fast and accurate online self-organizing scheme for parsimonious fuzzy neural networks[J]","volume":"72","author":"Wang","year":"2009","journal-title":"Neurocomputing"},{"issue":"1","key":"10.3233\/JIFS-213461_ref6","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1016\/j.asoc.2008.01.013","article-title":"Enhancing the generalization ability of neural networks through controlling the hidden layers[J]","volume":"9","author":"Wan","year":"2009","journal-title":"Applied Soft Computing"},{"issue":"2","key":"10.3233\/JIFS-213461_ref7","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/0925-2312(94)90055-8","article-title":"A simple and effective method for removal of hidden units and weights[J]","volume":"6","author":"Hagiwara","year":"1994","journal-title":"Neurocomputing"},{"key":"10.3233\/JIFS-213461_ref8","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1109\/ICNN.1988.23864","article-title":"Neural net pruning-why and how[C]","volume":"1","author":"Sietsma","year":"1988","journal-title":"Proceedings of International Conference on Neural Networks"},{"key":"10.3233\/JIFS-213461_ref9","unstructured":"Li H. , Kadav A. , Durdanovic I. , et al., Pruning filters for efficient convnets[J], arXiv preprint arXiv:1608.08710, 2016."},{"key":"10.3233\/JIFS-213461_ref10","unstructured":"He Y. , Kang G. , Dong X. , et al., Soft filter pruning for accelerating deep convolutional neural networks[J], arXiv preprint arXiv:1808.06866."},{"key":"10.3233\/JIFS-213461_ref11","unstructured":"Li H. , Kadav A. , Durdanovic I. , et al., Pruning filters for efficient convnets[J], arXiv preprint arXiv:1608.08710, 2016."},{"issue":"6","key":"10.3233\/JIFS-213461_ref12","doi-asserted-by":"crossref","first-page":"1296","DOI":"10.1109\/TFUZZ.2009.2029569","article-title":"SOFMLS: online self-organizing fuzzy modified least-squares network[J]","volume":"17","author":"de Jes\u00fas Rubio","year":"2009","journal-title":"IEEE Transactions on Fuzzy 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Computing"},{"issue":"5","key":"10.3233\/JIFS-213461_ref16","doi-asserted-by":"crossref","first-page":"998","DOI":"10.1109\/TFUZZ.2007.894980","article-title":"A self-organizing TS-type fuzzy network with support vector learning and its application to classification problems[J]","volume":"15","author":"Juang","year":"2007","journal-title":"IEEE Transactions on Fuzzy Systems"},{"issue":"16-18","key":"10.3233\/JIFS-213461_ref17","doi-asserted-by":"crossref","first-page":"3409","DOI":"10.1016\/j.neucom.2007.11.007","article-title":"Using self-organizing fuzzy network with support vector learning for face detection in color images[J]","volume":"71","author":"Juang","year":"2008","journal-title":"Neurocomputing"},{"issue":"1","key":"10.3233\/JIFS-213461_ref18","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1109\/TFUZZ.2005.861604","article-title":"Support-vector-based fuzzy neural network for pattern 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self-organising fuzzy neural network[J]","volume":"150","author":"Leng","year":"2005","journal-title":"Fuzzy Sets and Systems"},{"issue":"16-18","key":"10.3233\/JIFS-213461_ref22","doi-asserted-by":"crossref","first-page":"3818","DOI":"10.1016\/j.neucom.2009.05.006","article-title":"A fast and accurate online self-organizing scheme for parsimonious fuzzy neural networks[J]","volume":"72","author":"Wang","year":"2009","journal-title":"Neurocomputing"},{"key":"10.3233\/JIFS-213461_ref23","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.neucom.2013.02.009","article-title":"A self-organizing neuro-fuzzy network based on first order effect sensitivity analysis[J]","volume":"118","author":"Chen","year":"2013","journal-title":"Neurocomputing"},{"issue":"4","key":"10.3233\/JIFS-213461_ref24","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1109\/TCYB.2013.2260537","article-title":"Nonlinear systems modeling based on self-organizing fuzzy-neural-network with adaptive 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