{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T02:51:24Z","timestamp":1775530284374,"version":"3.50.1"},"publisher-location":"Cham","reference-count":43,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030357429","type":"print"},{"value":"9783030357436","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,12,12]],"date-time":"2019-12-12T00:00:00Z","timestamp":1576108800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-35743-6_3","type":"book-chapter","created":{"date-parts":[[2019,12,11]],"date-time":"2019-12-11T03:05:54Z","timestamp":1576033554000},"page":"21-44","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Overview of Artificial Neural Networks"],"prefix":"10.1007","author":[{"given":"Jo\u00e3o P. S.","family":"Rosa","sequence":"first","affiliation":[]},{"given":"Daniel J. D.","family":"Guerra","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1687-1447","authenticated-orcid":false,"given":"Nuno C. G.","family":"Horta","sequence":"additional","affiliation":[]},{"given":"Ricardo M. F.","family":"Martins","sequence":"additional","affiliation":[]},{"given":"Nuno C. C.","family":"Louren\u00e7o","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,12,12]]},"reference":[{"key":"3_CR1","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/s10994-011-5242-y","volume":"82","author":"P Langley","year":"2011","unstructured":"P. Langley, The changing science of machine learning. Mach. Learn. 82, 275\u2013279 (2011)","journal-title":"Mach. Learn."},{"key":"3_CR2","doi-asserted-by":"publisher","unstructured":"T. Bayes, An essay towards solving a problem in the doctrine of chances. Phil. Trans. 53, 370\u2013418 (1763), https:\/\/doi.org\/10.1098\/rstl.1763.0053","DOI":"10.1098\/rstl.1763.0053"},{"key":"3_CR3","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1090\/S0273-0979-08-01238-X","volume":"46","author":"P Diaconis","year":"2009","unstructured":"P. Diaconis, The Markov chain Monte Carlo revolution. Bull. Am. Math. Soc. 46, 179\u2013205 (2009)","journal-title":"Bull. Am. Math. Soc."},{"issue":"6","key":"3_CR4","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1037\/h0042519","volume":"65","author":"F Rosenblatt","year":"1958","unstructured":"F. Rosenblatt, The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65(6), 386\u2013408 (1958)","journal-title":"Psychol. Rev."},{"issue":"6088","key":"3_CR5","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"David E. Rumelhart","year":"1986","unstructured":"D. Rumelhart, G. Hinton, R. William, Learning representations by back-propagating errors Nature 323(9), 533\u2013536 (1986)","journal-title":"Nature"},{"issue":"6245","key":"3_CR6","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1126\/science.aaa8415","volume":"349","author":"M. I. Jordan","year":"2015","unstructured":"M.I. Jordan, T.M. Mitchell, Machine learning: trends, perspectives and prospects. Science 349, 255\u2013260 (2015)","journal-title":"Science"},{"key":"3_CR7","unstructured":"J. VanderPlas, Machine learning, in Python data science handbook essential tools for working with data (O\u2019Reilly Media, 2016), p. 541"},{"key":"3_CR8","unstructured":"A. G\u00e9ron, Hands-on Machine Learning with Scikit-Learn & TensorFlow (O\u2019Reilly, 2017)"},{"key":"3_CR9","unstructured":"AlphaGo Zero: Learning from Scratch, 2017. https:\/\/deepmind.com\/blog\/alphago-zero-learningscratch\/ . Accessed 4 Oct 2019"},{"key":"3_CR10","unstructured":"M. Banko, E. Brill, Scaling to very very large corpora for natural language disambiguation, in Proceedings of the 39th Annual Meeting on Association for Computational Linguistics\u2014ACL \u201901 (2001), pp. 26\u201333"},{"key":"3_CR11","volume-title":"The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World","author":"P Domingos","year":"2015","unstructured":"P. Domingos, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (Basic Books, New York, 2015)"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"S. Karamizadeh, S.M. Abdullah, M. Halimi, J. Shayan, M.J. Rajabi, Advantage and drawback of support vector machine functionality, in International Conference on Computer, Communication, and Control Technology (2014)","DOI":"10.1109\/I4CT.2014.6914146"},{"issue":"3","key":"3_CR13","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1073\/pnas.0610471104","volume":"104","author":"Jasper A. Vrugt","year":"2007","unstructured":"J.A. Vrugt, B.A. Robinson, Improved evolutionary optimization from genetically adaptive multimethod search. PNAS 104, 708\u2013711 (2006)","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"D.B. Fogel, The Advantages of Evolutionary Computation (Natural Selection Inc, 1997)","DOI":"10.1201\/9781420050387"},{"issue":"6","key":"3_CR15","doi-asserted-by":"publisher","first-page":"989","DOI":"10.1109\/TCAD.2018.2834394","volume":"38","author":"R Martins","year":"2019","unstructured":"R. Martins, N. Louren\u00e7o, F. Passos, R. P\u00f3voa, A. Canelas, E. Roca, R. Castro-L\u00f3pez, J. Sieiro, F.V. Fern\u00e1ndez, N. Horta, Two-step RF IC block synthesis with pre-optimized inductors and full layout generation in-the-loop. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. (TCAD) 38(6), 989\u20131002 (2019)","journal-title":"IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. (TCAD)"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"N. Louren\u00e7o, R. Martins, N. Horta, Automatic Analog IC Sizing and Optimization Constrained with PVT Corners and Layout Effects (Springer, 2017)","DOI":"10.1007\/978-3-319-42037-0"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"R. Martins, N. Lourenco, A. Canelas, N. Horta, Electromigration-aware and IR-drop avoidance routing in analog multiport terminal structures, in Design, automation & test in Europe conference (DATE) (Dresden, Germany, 2014), pp. 1\u20136","DOI":"10.7873\/DATE.2014.023"},{"issue":"1","key":"3_CR18","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1109\/TVLSI.2018.2872410","volume":"27","author":"Ricardo Martins","year":"2019","unstructured":"R. Martins, N. Louren\u00e7o, N. Horta, J. Yin, P. Mak, R. Martins, Many-objective sizing optimization of a class-C\/D VCO for ultralow-power IoT and ultralow- phase-noise cellular applications. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 27(1), 69\u201382 (2019)","journal-title":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems"},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"F. De Bernardinis, M. I. Jordan, A. SangiovanniVincentelli, Support vector machines for analog circuit performance representation, in Proceedings 2003. Design Automation Conference (IEEE Cat. No. 03CH37451) (Anaheim, CA, 2003)","DOI":"10.1145\/775832.776074"},{"key":"3_CR20","doi-asserted-by":"crossref","unstructured":"N. Louren\u00e7o, R. Martins, M. Barros, N. Horta, Chapter in analog\/RF and mixed-signal circuit systematic design, in Analog Circuit Design based on Robust POFs using an Enhanced MOEA with SVM Models, ed. by M. Fakhfakh, E. Tielo-Cuautle, R. Castro-Lopez (Springer, 2013), pp 149\u2013167","DOI":"10.1007\/978-3-642-36329-0_7"},{"key":"3_CR21","volume-title":"Machine Learning in VLSI Computer-Aided Design","author":"J Tao","year":"2019","unstructured":"J. Tao et al., Large-scale circuit performance modeling by bayesian model fusion, in Machine Learning in VLSI Computer-Aided Design, ed. by I. Elfadel, D. Boning, X. Li (Springer, Cham, 2019)"},{"key":"3_CR22","doi-asserted-by":"crossref","unstructured":"W. Lyu, F. Yang, C. Yan, D. Zhou, X. Zeng, Multi-objective Bayesian optimization for analog\/RF circuit synthesis, in 2018 55th ACM\/ESDA\/IEEE Design Automation Conference (DAC) (San Francisco, CA, 2018)","DOI":"10.1109\/DAC.2018.8465872"},{"key":"3_CR23","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"J. Schmidhuber, Deep learning in neural networks: an overview. Neural Netw. 61, 85\u2013117 (2015)","journal-title":"Neural Netw."},{"issue":"5","key":"3_CR24","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","volume":"2","author":"K Hornik","year":"1989","unstructured":"K. Hornik, M. Stinchcombe, H. White, Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359\u2013366 (1989)","journal-title":"Neural Netw."},{"issue":"7553","key":"3_CR25","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Las Vegas, NV, 2016), pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"3_CR27","unstructured":"R. Eldan, O. Shamir, The power of depth for feedforward neural networks, in Conference on Learning Theory (2016), pp 907\u2013940"},{"issue":"2","key":"3_CR28","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1109\/TNN.2003.809401","volume":"14","author":"GB Huang","year":"2003","unstructured":"G.B. Huang, Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Trans. Neural Netw. 14(2), 274\u2013281 (2003)","journal-title":"IEEE Trans. Neural Netw."},{"key":"3_CR29","unstructured":"K. Jinchuan, L. Xinzhe, Empirical analysis of optimal hidden neurons in neural network modeling for stock prediction, in Proceedings-2008 Pacific-Asia Workshop on Computational Intelligence and Industrial Application, PACIIA 2008, vol. 2 (2008), pp. 828\u2013832"},{"issue":"5","key":"3_CR30","doi-asserted-by":"publisher","first-page":"836","DOI":"10.1109\/TNN.2007.912306","volume":"19","author":"S Trenn","year":"2008","unstructured":"S. Trenn, Multilayer perceptrons: approximation order and necessary number of hidden units. IEEE Trans. Neural Netw. 19(5), 836\u2013844 (2008)","journal-title":"IEEE Trans. Neural Netw."},{"key":"3_CR31","unstructured":"X. Glorot, Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, vol. 9 (2010), pp. 249\u2013256"},{"key":"3_CR32","unstructured":"V. Nair, G.E. Hinton, Rectified linear units improve restricted boltzmann machines, in Proceedings of the 27th International Conference on International Conference on Machine Learning (2010)"},{"key":"3_CR33","unstructured":"B. Xu, N. Wang, T. Chen, M. Li, Empirical evaluation of rectified activations in convolutional network, in ICML Deep Learning Workshop (Lille, France, 2015)"},{"key":"3_CR34","unstructured":"D.A. Clevert, T. Unterthiner, S. Hochreiter, Fast and accurate deep network learning by Exponential Linear Units (ELUs), in International Conference on Learning Representations (2015), pp. 1\u201314"},{"key":"3_CR35","first-page":"536","volume":"25","author":"A Cauchy","year":"1847","unstructured":"A. Cauchy, M\u00e9thode g\u00e9n\u00e9rale pour la r\u00e9solution des systemes d\u2019\u00e9quations simultan\u00e9es. Comp. Rend. Sci. Paris 25, 536\u2013538 (1847)","journal-title":"Comp. Rend. Sci. Paris"},{"issue":"3","key":"3_CR36","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1214\/aoms\/1177729586","volume":"22","author":"H Robbins","year":"1951","unstructured":"H. Robbins, S. Monro, A stochastic approximation method the annals of mathematical statistics. An. Math. Stat. 22(3), 400\u2013407 (1951)","journal-title":"An. Math. Stat."},{"issue":"3","key":"3_CR37","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1214\/aoms\/1177729392","volume":"23","author":"J Kiefer","year":"1952","unstructured":"J. Kiefer, J. Wolfowitz, Stochastic estimation of the maximum of a regression function. Ann. Math. Stat. 23(3), 462\u2013466 (1952)","journal-title":"Ann. Math. Stat."},{"key":"3_CR38","unstructured":"N.S. Keskar, D. Mudigere, J. Nocedal, M. Smelyanskiy, P.T.P. Tang, On large-batch training for deep learning: generalization gap and sharp minima, in 5th International Conference on Learning Representations, ICLR 2017\u2014Conference Track Proceedings (2017)"},{"key":"3_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/0041-5553(64)90137-5","volume":"4","author":"B Polyak","year":"1964","unstructured":"B. Polyak, Some methods of speeding up the convergence of iteration methods. USSR Comput. Math. Math. Phys. 4, 1\u201317 (1964)","journal-title":"USSR Comput. Math. Math. Phys."},{"key":"3_CR40","first-page":"543","volume":"269","author":"Y Nesterov","year":"1983","unstructured":"Y. Nesterov, A method for solving the convex programming problem with convergence rate O(1\/k\u02c62). Dokl. Akad. Nauk SSSR 269, 543\u2013547 (1983)","journal-title":"Dokl. Akad. Nauk SSSR"},{"key":"3_CR41","first-page":"2121","volume":"12","author":"J Duchi","year":"2011","unstructured":"J. Duchi, E. Hazan, Y. Singer, Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121\u20132159 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"3_CR42","unstructured":"D.P. Kingma, J.B, Adam: a method for stochastic optimization. CoRR, abs\/1412.6980 (2014)"},{"key":"3_CR43","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."}],"container-title":["SpringerBriefs in Applied Sciences and Technology","Using Artificial Neural Networks for Analog Integrated Circuit Design Automation"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-35743-6_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,8]],"date-time":"2022-10-08T04:38:31Z","timestamp":1665203911000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-35743-6_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,12]]},"ISBN":["9783030357429","9783030357436"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-35743-6_3","relation":{},"ISSN":["2191-530X","2191-5318"],"issn-type":[{"value":"2191-530X","type":"print"},{"value":"2191-5318","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,12]]},"assertion":[{"value":"12 December 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}