{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:42:21Z","timestamp":1754156541058,"version":"3.41.2"},"reference-count":31,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2015,3,9]],"date-time":"2015-03-09T00:00:00Z","timestamp":1425859200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,3,9]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-heading\">Purpose<\/jats:title><jats:p>\u2013 The two main purposes of this paper are: first, the development of a new optimization algorithm called GHSACO by incorporating the global-best harmony search (GHS) which is a stochastic optimization algorithm recently developed, with the ant colony optimization (ACO) algorithm. Second, design of a new indirect adaptive recurrent fuzzy-neural controller (IARFNNC) for uncertain nonlinear systems using the developed optimization method (GHSACO) and the concept of the supervisory controller.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title><jats:p>\u2013 The novel optimization method introduces a novel improvization process, which is different from that of the GHS in the following aspects: a modified harmony memory representation and conception. The use of a global random switching mechanism to monitor the choice between the ACO and GHS. An additional memory consideration selection rule using the ACO random proportional transition rule with a pheromone trail update mechanism. The developed optimization method is applied for parametric optimization of all recurrent fuzzy neural networks adaptive controller parameters. In addition, in order to guarantee that the system states are confined to the safe region, a supervisory controller is incorporated into the IARFNNC global structure.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Findings<\/jats:title><jats:p>\u2013 First, to analyze the performance of GHSACO method and shows its effectiveness, some benchmark functions with different dimensions are used. Simulation results demonstrate that it can find significantly better solutions when compared with the Harmony Search (HS), GHS, improved HS (IHS) and conventional ACO algorithm. In addition, simulation results obtained using an example of nonlinear system shows clearly the feasibility and the applicability of the proposed control method and the superiority of the GHSACO method compared to the HS, its variants, particle swarm optimization, and genetic algorithms applied to the same problem.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title><jats:p>\u2013 The proposed new GHS algorithm is more efficient than the original HS method and its most known variants IHS and GHS. The proposed control method is applicable to any uncertain nonlinear system belongs in the class of systems treated in this paper.<\/jats:p><\/jats:sec>","DOI":"10.1108\/ijicc-05-2014-0028","type":"journal-article","created":{"date-parts":[[2015,3,16]],"date-time":"2015-03-16T10:52:20Z","timestamp":1426503140000},"page":"69-98","source":"Crossref","is-referenced-by-count":1,"title":["A novel global harmony search method based off-line tuning of RFNN for adaptive control of uncertain nonlinear systems"],"prefix":"10.1108","volume":"8","author":[{"given":"Fouad","family":"Allouani","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Djamel","family":"Boukhetala","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fares","family":"Boudjema","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gao","family":"Xiao-Zhi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"key2020122522255564200_b1","doi-asserted-by":"crossref","unstructured":"Allouani, F. Boukhetela, D. and Boudjema, F. (2012), \u201cDecentralized sliding mode controller design based on hybrid approach for interconnected uncertain non-linear systems\u201d, International Journal of Instrumentation Technology , Vol. 1 No. 2, pp. 155-187.","DOI":"10.1504\/IJIT.2012.053298"},{"key":"key2020122522255564200_b2","unstructured":"Allouani, F. , Boukhetela, D. and Boudjema, F. (2013), \u201cDecentralized sliding mode controller based on genetic algorithm and a hybrid approach for interconnected uncertain nonlinear systems\u201d, International Journal of Control and Automation , Vol. 6 No. 1, pp. 61-86."},{"key":"key2020122522255564200_b3","unstructured":"Chiang, S.J. and Lin, C.H. (2006), \u201cAdaptive back stepping RFNN control for synchronous reluctance motor drive\u201d, Proceedings of the 37th IEEE conference on Power Electronics Specialists, IEEE, pp. 1-6."},{"key":"key2020122522255564200_b4","doi-asserted-by":"crossref","unstructured":"Dorigo, M. , Maniezzo, V. and Colorni, A. (1996), \u201cAnt system: optimization by a colony of cooperating agents\u201d, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics , Vol. 26 No. 1, pp. 29-41.","DOI":"10.1109\/3477.484436"},{"key":"key2020122522255564200_b5","doi-asserted-by":"crossref","unstructured":"Eberhart, R.C. and Kennedy, J. (1995), \u201cA new optimizer using particle swarm theory\u201d, Proceedings of the 6th International Symposium on Micro Machine and Human Science, Nagoya, IEEE, pp. 39-43.","DOI":"10.1109\/MHS.1995.494215"},{"key":"key2020122522255564200_b6","doi-asserted-by":"crossref","unstructured":"Frikha, S. , Djemel, M. and Derbel, N. (2010), \u201cNeural network adaptive control scheme for nonlinear systems with lyapunov approach and sliding mode\u201d, International Journal of Intelligent Computing and Cybernetics , Vol. 3 No. 3, pp. 495-513.","DOI":"10.1108\/17563781011066747"},{"key":"key2020122522255564200_b7","doi-asserted-by":"crossref","unstructured":"Geem, Z.W. , Kim, J.H. and Loganathan, G.V. (2001), \u201cA new heuristic optimization algorithm: harmony search\u201d, Simulation , Vol. 76 No. 2, pp. 60-68.","DOI":"10.1177\/003754970107600201"},{"key":"key2020122522255564200_b8","doi-asserted-by":"crossref","unstructured":"Guian, Z. and Jennie, S. (1998), \u201cAdvanced neural network training algorithm with reduced complexity based on Jacobian deficiency\u201d, IEEE Transactions on Neural Networks , Vol. 9 No. 3, pp. 448-453.","DOI":"10.1109\/72.668886"},{"key":"key2020122522255564200_b9","doi-asserted-by":"crossref","unstructured":"Isidori, A. (1995), Nonlinear Control Systems , Springer-Verlag, Berlin.","DOI":"10.1007\/978-1-84628-615-5"},{"key":"key2020122522255564200_b10","doi-asserted-by":"crossref","unstructured":"Johansson, E.M. , Dowla, F.U. and Goodman, D.M. (1991), \u201cBackpropagation learning for multilayer feed-forward neural networks using the conjugate gradient method\u201d, International Journal of Neural Systems , Vol. 2 No. 1, pp. 291-301.","DOI":"10.1142\/S0129065791000261"},{"key":"key2020122522255564200_b11","unstructured":"Kremer, S.C. and Kolen, J.F. (2001), A Field Guide to Dynamical Recurrent Networks , Wiley-Blackwell, New York, NY."},{"key":"key2020122522255564200_b12","doi-asserted-by":"crossref","unstructured":"Lee, C.H. and Teng, C.C. (2000), \u201cIdentification and control of dynamic systems using recurrent fuzzy neural networks\u201d, IEEE Transactions on Fuzzy Systems , Vol. 8 No 4, pp. 349-366.","DOI":"10.1109\/91.868943"},{"key":"key2020122522255564200_b13","unstructured":"Lee, C.H. and Lin, Y.C. (2004), \u201clearning algorithm for fuzzy neuro-systems\u201d, Proceedings of the Fuzz-IEEE, pp. 691-696."},{"key":"key2020122522255564200_b14","doi-asserted-by":"crossref","unstructured":"Lin, C.H. , Chiang, S.J. and Lin, M.K. (2004a), \u201cAdaptive RFNN control for synchronous reluctance motor drive\u201d, Proceedings of the 35th Annual IEEE Conference on Power Electronics Specialists, Adzen, IEEE, pp. 3272-3277.","DOI":"10.1109\/PESC.2004.1355053"},{"key":"key2020122522255564200_b15","unstructured":"Lin, C.H. , Chiang, S.J. and Lee, T.S. (2004b), \u201cAdaptive H\u221e recurrent fuzzy neural network control for synchronous reluctance motor drive\u201d, Proceedings of the 30th Annual IEEE conference of Industrial Electronics Society, IEEE, pp. 2279-2284."},{"key":"key2020122522255564200_b16","doi-asserted-by":"crossref","unstructured":"Lin, C.J. and Xu, Y.J. (2006), \u201cA novel evolution learning for recurrent wavelet-based neuro-fuzzy networks\u201d, Soft Computing , Vol. 10 No. 3, pp. 193-205.","DOI":"10.1007\/s00500-004-0455-7"},{"key":"key2020122522255564200_b18","doi-asserted-by":"crossref","unstructured":"Lin, C.J. , Lee, J.H. and Lee, C.Y. (2008), \u201cA novel hybrid learning algorithm for parametric fuzzy CMAC networks and its classification applications\u201d, Expert Systems with Applications , Vol. 35 No. 4, pp. 1711-1721.","DOI":"10.1016\/j.eswa.2007.08.086"},{"key":"key2020122522255564200_b17","doi-asserted-by":"crossref","unstructured":"Lin, F.J. , Shieh, H.J. , Huang, P.K. and Teng, L.T. (2006), \u201cAdaptive control with hysteresis estimation and compensation using RFNN for piezo-actuator\u201d, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, Vol. 53 No. 9, pp. 1649-1661.","DOI":"10.1109\/TUFFC.2006.1678193"},{"key":"key2020122522255564200_b19","doi-asserted-by":"crossref","unstructured":"Lu, C.H. and Tsai, C.C. (2008), \u201cAdaptive predictive control with recurrent neural network for industrial processes: An application to temperature control of a variable-frequency oil-cooling machine\u201d, IEEE Transactions on Industrial Electronics , Vol. 55 No. 3, pp. 1366-1375.","DOI":"10.1109\/TIE.2007.896492"},{"key":"key2020122522255564200_b20","doi-asserted-by":"crossref","unstructured":"Mahdavi, M. , Fesanghary, M. and Damangir, E. (2007), \u201cAn improved harmony search algorithm for solving optimization problems\u201d, Applied Mathematics and Computution , Vol. 188 No. 2, pp. 1567-1579.","DOI":"10.1016\/j.amc.2006.11.033"},{"key":"key2020122522255564200_b21","unstructured":"Mendes, J. , Sousa, N. and Araujo, R. (2004), \u201cAdaptive predictive control with recurrent fuzzy neural network for industrial\u201d, Proceedings of the 16th IEEE Conference on Emerging Technologies and Factory Automation, Toulouse, IEEE, pp. 1-8."},{"key":"key2020122522255564200_b100","unstructured":"Molga, M. and Smutnicki, C. (2005), \u201cTest functions for optimization needs\u201d, available at: www.zsd.ict.pwr.wroc.pl\/files\/docs\/functions.pdf"},{"key":"key2020122522255564200_b23","doi-asserted-by":"crossref","unstructured":"Roshtkhari, M.J. , Arami, A. and Lucas, C. (2010), \u201cImitative learning based emotional controller for unknown systems with unstable equilibrium\u201d, International Journal of Intelligent Computing and Cybernetics , Vol. 3 No 2, pp. 334-359.","DOI":"10.1108\/17563781011049232"},{"key":"key2020122522255564200_b24","doi-asserted-by":"crossref","unstructured":"Omran, M.G.H. and Mahdavi, M. (2008), \u201cGlobal-best harmony search\u201d, Applied Mathematics and Computution , Vol. 198 No. 2, pp. 643-656.","DOI":"10.1016\/j.amc.2007.09.004"},{"key":"key2020122522255564200_b26","unstructured":"Sastry, S. and Bodson, M. (1989), Adaptive Control: Stability, Convergence, and Robustness , Prentice-Hall, Englewood Cliffs, NJ."},{"key":"key2020122522255564200_b27","unstructured":"Slotine, J.J.E. and Li, W. (1991), Applied Nonlinear Control , Prentice-Hall Inc., Englewood Cliffs, NJ."},{"key":"key2020122522255564200_b28","unstructured":"Wang, L.X. (1994), \u201cA supervisory controller for fuzzy control systems that guarantees stability\u201d, Proceedings of the 3rd IEEE World Congress on Computational Intelligence, IEEE, Orlando, FL, pp. 1035-1039."},{"key":"key2020122522255564200_b29","unstructured":"Wei, S. and Yaonan, W. (2005), \u201cAn adaptive control for AC servo system using recurrent fuzzy neural network\u201d, Advances in Natural Computation Lecture Notes in Computer Science , Vol. 36 No 11, pp. 190-195."},{"key":"key2020122522255564200_b30","doi-asserted-by":"crossref","unstructured":"Werbos, P.J. (1990), \u201cBackpropagation through time: what it does and how to do it\u201d, Proceedings of IEEE, Vol. 78 No. 10, pp. 1550-1560.","DOI":"10.1109\/5.58337"},{"key":"key2020122522255564200_b31","doi-asserted-by":"crossref","unstructured":"Yao, X. , Liu, Y. and Lin, G. (1999), \u201cEvolutionary programming made faster\u201d, IEEE Transactions on Evolutionary Computation , Vol. 3 No. 2, pp. 82-102.","DOI":"10.1109\/4235.771163"},{"key":"key2020122522255564200_frd2","doi-asserted-by":"crossref","unstructured":"Puskorius, G.V. and Feldkamp, L.A. (1994), \u201cNeuro-control of nonlinear dynamical systems with kalman filter trained recurrent networks\u201d, IEEE Transactions on Neural Networks , Vol. 5 No. 2, pp. 279-297.","DOI":"10.1109\/72.279191"}],"container-title":["International Journal of Intelligent Computing and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.emeraldinsight.com\/doi\/full-xml\/10.1108\/IJICC-05-2014-0028","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJICC-05-2014-0028\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJICC-05-2014-0028\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:54:18Z","timestamp":1753397658000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/ijicc\/article\/8\/1\/69-98\/137244"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,3,9]]},"references-count":31,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2015,3,9]]}},"alternative-id":["10.1108\/IJICC-05-2014-0028"],"URL":"https:\/\/doi.org\/10.1108\/ijicc-05-2014-0028","relation":{},"ISSN":["1756-378X"],"issn-type":[{"type":"print","value":"1756-378X"}],"subject":[],"published":{"date-parts":[[2015,3,9]]}}}