{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T06:15:11Z","timestamp":1782972911638,"version":"3.54.5"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2019,9,11]],"date-time":"2019-09-11T00:00:00Z","timestamp":1568160000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,9,11]],"date-time":"2019-09-11T00:00:00Z","timestamp":1568160000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2020,4]]},"DOI":"10.1007\/s00521-019-04494-1","type":"journal-article","created":{"date-parts":[[2019,9,11]],"date-time":"2019-09-11T15:02:56Z","timestamp":1568214176000},"page":"1959-1969","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["Research on radar signal recognition based on automatic machine learning"],"prefix":"10.1007","volume":"32","author":[{"given":"Peng","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,9,11]]},"reference":[{"issue":"02","key":"4494_CR1","first-page":"105","volume":"16","author":"T Long","year":"2019","unstructured":"Long T, Zeng T, Hu C, Dong X, Chen L, Liu Q, Xie Y, Ding Z, Li Y, Wang Y, Wang Y (2019) High resolution radar real-time signal and information processing. China Commun 16(02):105\u2013133","journal-title":"China Commun"},{"key":"4494_CR2","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.eswa.2018.12.057","volume":"122","author":"S Jazayeri","year":"2019","unstructured":"Jazayeri S, Saghafi A, Esmaeili S, Tsokos CP (2019) Automatic object detection using dynamic time warping on ground penetrating radar signals. Expert Syst Appl 122:102\u2013107","journal-title":"Expert Syst Appl"},{"issue":"1","key":"4494_CR3","first-page":"108","volume":"8","author":"H Rong","year":"2013","unstructured":"Rong H, Cheng J, Li Y (2013) Radar emitter signal analysis with estimation of distribution algorithms. J Netw 8(1):108","journal-title":"J Netw"},{"key":"4494_CR4","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.dsp.2018.09.003","volume":"83","author":"Z Zheng","year":"2018","unstructured":"Zheng Z, Lu J, Wang W-Q, Yang H, Zhang S (2018) An efficient method for angular parameter estimation of incoherently distributed sources via beamspace shift invariance. Digit Signal Process 83:261\u2013270","journal-title":"Digit Signal Process"},{"key":"4494_CR5","doi-asserted-by":"publisher","first-page":"2029","DOI":"10.1007\/s00521-018-3441-1","volume":"30","author":"H Hermessi","year":"2018","unstructured":"Hermessi H, Mourali O, Zagrouba E (2018) Convolutional neural network-based multimodal image fusion via similarity learning in the shearlet domain. Neural Comput Appl 30:2029. \nhttps:\/\/doi.org\/10.1007\/s00521-018-3441-1","journal-title":"Neural Comput Appl"},{"issue":"06","key":"4494_CR6","first-page":"1053","volume":"35","author":"R Cao","year":"2018","unstructured":"Cao R, Zhang X (2018) Computationally efficient MUSIC-based algorithm for joint direction of arrival (DOA) and Doppler frequency estimation in monostatic MIMO radar. Trans Nanjing Univ Aeronaut Astronaut 35(06):1053\u20131063","journal-title":"Trans Nanjing Univ Aeronaut Astronaut"},{"key":"4494_CR7","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.nima.2018.03.058","volume":"909","author":"S Ter-Avetisyan","year":"2018","unstructured":"Ter-Avetisyan S, Singh PK, Kakolee KF, Ahmed H, Jeong TW, Scullion C, Hadjisolomou P, Borghesi M, Bychenkov VY (2018) Ultrashort PW laser pulse interaction with target and ion acceleration. Nuclear Inst Methods Phys Res A 909:156\u2013159","journal-title":"Nuclear Inst Methods Phys Res A"},{"issue":"1","key":"4494_CR8","first-page":"339","volume":"47","author":"B Bayat","year":"2018","unstructured":"Bayat B, van der Tol C, Verhoef W (2018) Retrieval of land surface properties from an annual time series of Landsat TOA radiances during a drought episode using coupled radiative transfer models. Remote Sens Environ 47(1):339\u2013349","journal-title":"Remote Sens Environ"},{"key":"4494_CR9","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.sigpro.2018.09.016","volume":"154","author":"C Wen","year":"2018","unstructured":"Wen C, Tao M, Peng J, Wu J, Wang T (2018) Clutter suppression for airborne FDA-MIMO radar using multi-waveform adaptive processing and auxiliary channel STAP. Signal Process 154:280\u2013293","journal-title":"Signal Process"},{"key":"4494_CR10","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.comnet.2019.01.023","volume":"151","author":"KAP da Costa","year":"2019","unstructured":"da Costa KAP, Papa JP, Lisboa CO, Munoz R, de Albuquerque VHC (2019) Internet of things: a survey on machine learning-based intrusion detection approaches. Comput Netw 151:147\u2013157","journal-title":"Comput Netw"},{"key":"4494_CR11","doi-asserted-by":"publisher","first-page":"1006","DOI":"10.1016\/j.scitotenv.2018.06.389","volume":"644","author":"W Chen","year":"2018","unstructured":"Chen W, Zhang S, Li R, Shahabi H (2018) Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and na\u00efve Bayes tree for landslide susceptibility modeling. Sci Total Environ 644:1006\u20131018","journal-title":"Sci Total Environ"},{"key":"4494_CR12","doi-asserted-by":"crossref","unstructured":"Gong H (2018) Tibetan character recognition based on machine learning of K-means algorithm. In: Proceedings of 2018 international conference on computer modeling, simulation and algorithm (CMSA2018), vol 3. Advanced Science and Industry Research Center, Science and Engineering Research Center","DOI":"10.2991\/cmsa-18.2018.78"},{"key":"4494_CR13","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1016\/j.jocs.2018.04.006","volume":"27","author":"J Wu","year":"2018","unstructured":"Wu J (2018) A generalized tree augmented naive Bayes link prediction model. J Comput Sci 27:206\u2013217","journal-title":"J Comput Sci"},{"issue":"7","key":"4494_CR14","doi-asserted-by":"publisher","first-page":"1129","DOI":"10.1108\/K-07-2015-0180","volume":"45","author":"PA Sarvari","year":"2016","unstructured":"Sarvari PA, Ustundag A, Takci H (2016) Performance evaluation of different customer segmentation approaches based on RFM and demographics analysis. Kybernetes 45(7):1129\u20131157","journal-title":"Kybernetes"},{"key":"4494_CR15","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/j.compag.2018.10.024","volume":"155","author":"D Elavarasan","year":"2018","unstructured":"Elavarasan D, Vincent DR, Sharma V, Zomaya AY, Srinivasan K (2018) Forecasting yield by integrating agrarian factors and machine learning models: a survey. Comput Electron Agric 155:257\u2013282","journal-title":"Comput Electron Agric"},{"issue":"1","key":"4494_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13634-019-0603-y","volume":"2019","author":"J Wan","year":"2019","unstructured":"Wan J, Chen B, Xu B, Liu H, Jin L (2019) Convolutional neural networks for radar HRRP target recognition and rejection. EURASIP J Adv Signal Process 2019(1):1\u201317","journal-title":"EURASIP J Adv Signal Process"},{"key":"4494_CR17","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.measurement.2018.07.092","volume":"130","author":"AK Panda","year":"2018","unstructured":"Panda AK, Rapur JS, Tiwari R (2018) Prediction of flow blockages and impending cavitation in centrifugal pumps using support vector machine (SVM) algorithms based on vibration measurements. Measurement 130:44\u201356","journal-title":"Measurement"},{"key":"4494_CR18","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.apgeog.2018.12.011","volume":"102","author":"MN Uddin","year":"2019","unstructured":"Uddin MN, Islam AKMS, Bala SK, Islam GMT, Adhikary S, Saha D, Haque S, Fahad MGR, Akter R (2019) Mapping of climate vulnerability of the coastal region of Bangladesh using principal component analysis. Appl Geogr 102:47\u201357","journal-title":"Appl Geogr"},{"key":"4494_CR19","unstructured":"Song Z (2018) Study on automatic identification technology of greenhouse tomato pests and diseases based on machine learning. In: Proceedings of 2018 2nd international conference on systems, computing, and applications (SYSTCA 2018), vol 4. International Information and Engineering Association, Computer Science and Electronic Technology International Society"},{"key":"4494_CR20","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.automatica.2018.08.012","volume":"97","author":"RP Guan","year":"2018","unstructured":"Guan RP, Ristic B, Wang L, Evans R (2018) Monte Carlo localisation of a mobile robot using a Doppler\u2013Azimuth radar. Automatica 97:161\u2013166","journal-title":"Automatica"},{"issue":"4","key":"4494_CR21","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1364\/OL.43.000783","volume":"43","author":"A Curcio","year":"2018","unstructured":"Curcio A, Dolci V, Lupi S, Petrarca M (2018) Terahertz-based retrieval of the spectral phase and amplitude of ultrashort laser pulses. Opt Lett 43(4):783\u2013786","journal-title":"Opt Lett"},{"key":"4494_CR22","doi-asserted-by":"crossref","unstructured":"Panigrahi PK, Ghosh S, Parhi DR (2014) A novel intelligent mobile robot navigation technique for avoiding obstacles using RBF neural network. In: Proceedings of the 2014 international conference on control, instrumentation, energy and communication (CIEC). IEEE","DOI":"10.1109\/CIEC.2014.6959038"},{"key":"4494_CR23","doi-asserted-by":"crossref","unstructured":"Kun Q, Tian-zhen W, Tian-hao T, Claramunt C (2014) A novel local BP neural network model and application in parameter identification of power system. In: 33rd Chinese control conference (CCC)","DOI":"10.1109\/ChiCC.2014.6896115"},{"key":"4494_CR24","doi-asserted-by":"crossref","unstructured":"Li X (2014) Study on traffic flow base on RBF neural network. In: 2014 sixth international conference on measuring technology and mechatronics automation (ICMTMA)","DOI":"10.1109\/ICMTMA.2014.159"},{"key":"4494_CR25","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.ins.2018.10.043","volume":"477","author":"BA Pimentel","year":"2019","unstructured":"Pimentel BA, de Carvalho ACPLF (2019) A new data characterization for selecting clustering algorithms using meta-learning. Inf Sci 477:203\u2013219","journal-title":"Inf Sci"},{"key":"4494_CR26","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.ins.2018.10.013","volume":"476","author":"X Chu","year":"2018","unstructured":"Chu X, Cai F, Cui C, Hu M, Li L, Qin Q (2018) Adaptive recommendation model using meta-learning for population-based algorithms. Inf Sci 476:192\u2013210","journal-title":"Inf Sci"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04494-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-019-04494-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04494-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,9,10]],"date-time":"2020-09-10T16:37:45Z","timestamp":1599755865000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-019-04494-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,11]]},"references-count":26,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2020,4]]}},"alternative-id":["4494"],"URL":"https:\/\/doi.org\/10.1007\/s00521-019-04494-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,11]]},"assertion":[{"value":"20 April 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 August 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 September 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"There are no conflicts of interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}