| Levenberg–marquardt training H Yu, BM Wilamowski Intelligent systems, 12-1-12-16, 2018 | 879 | 2018 |
| Improved computation for Levenberg–Marquardt training BM Wilamowski, H Yu IEEE transactions on neural networks 21 (6), 930-937, 2010 | 714 | 2010 |
| Advantages of radial basis function networks for dynamic system design H Yu, T Xie, S Paszczynski, BM Wilamowski IEEE Transactions on Industrial Electronics 58 (12), 5438-5450, 2011 | 513 | 2011 |
| Selection of proper neural network sizes and architectures—A comparative study D Hunter, H Yu, MS Pukish III, J Kolbusz, BM Wilamowski IEEE Transactions on Industrial Informatics 8 (2), 228-240, 2012 | 506 | 2012 |
| Power electronics and motor drives BM Wilamowski, JD Irwin CRC press, 2018 | 453* | 2018 |
| Neural network architectures and learning algorithms BM Wilamowski IEEE Industrial Electronics Magazine 3 (4), 56-63, 2009 | 438 | 2009 |
| The electronics handbook JC Whitaker, MH Kryder, JF Shackelford, VK Tripathi, G DeSantis, ... Crc Press, 2018 | 379 | 2018 |
| An algorithm for fast convergence in training neural networks BM Wilamowski, S Iplikci, O Kaynak, MO Efe IJCNN'01. International Joint Conference on Neural Networks. Proceedings …, 2001 | 277 | 2001 |
| A novel RBF training algorithm for short-term electric load forecasting and comparative studies C Cecati, J Kolbusz, P Różycki, P Siano, BM Wilamowski IEEE Transactions on industrial Electronics 62 (10), 6519-6529, 2015 | 270 | 2015 |
| Neural network learning without backpropagation BM Wilamowski, H Yu IEEE Transactions on Neural Networks 21 (11), 1793-1803, 2010 | 238 | 2010 |
| Comparison between traditional neural networks and radial basis function networks T Xie, H Yu, B Wilamowski 2011 IEEE international symposium on industrial electronics, 1194-1199, 2011 | 198 | 2011 |
| Computing gradient vector and Jacobian matrix in arbitrarily connected neural networks BM Wilamowski, NJ Cotton, O Kaynak, GÜ Dundar IEEE Transactions on Industrial Electronics 55 (10), 3784-3790, 2008 | 182 | 2008 |
| Fully connected cascade artificial neural network architecture for attention deficit hyperactivity disorder classification from functional magnetic resonance imaging data G Deshpande, P Wang, D Rangaprakash, B Wilamowski IEEE transactions on cybernetics 45 (12), 2668-2679, 2015 | 181 | 2015 |
| Efficient algorithm for training neural networks with one hidden layer BM Wilamowski, Y Chen, A Malinowski IJCNN'99. International Joint Conference on Neural Networks. Proceedings …, 1999 | 170 | 1999 |
| An incremental design of radial basis function networks H Yu, PD Reiner, T Xie, T Bartczak, BM Wilamowski IEEE transactions on neural networks and learning systems 25 (10), 1793-1803, 2014 | 149 | 2014 |
| Fast and efficient second-order method for training radial basis function networks T Xie, H Yu, J Hewlett, P Rózycki, B Wilamowski IEEE transactions on neural networks and learning systems 23 (4), 609-619, 2012 | 146 | 2012 |
| Fuzzy system based maximum power point tracking for PV system BM Wilamowski, X Li IEEE 2002 28th Annual Conference of the Industrial Electronics Society …, 2002 | 132 | 2002 |
| Implementing a fuzzy system on a field programmable gate array M McKenna, BM Wilamowski IJCNN'01. International Joint Conference on Neural Networks. Proceedings …, 2001 | 105 | 2001 |
| A fast density and grid based clustering method for data with arbitrary shapes and noise B Wu, BM Wilamowski IEEE Transactions on Industrial Informatics 13 (4), 1620-1628, 2016 | 103 | 2016 |
| Nonlinear system modeling using RBF networks for industrial application X Meng, P Rozycki, JF Qiao, BM Wilamowski IEEE transactions on industrial informatics 14 (3), 931-940, 2017 | 100 | 2017 |
Hao YuAuburn UniversityEmail verificata su tigermail.auburn.edu