PUBLICATIONS

 

Selected publications

  • J. Richardson, J. Korniak, P. Reiner, B. M. Wilamowski, "Nearest Neighbor Spline Approximation (NNSA) Improvement to TSK Fuzzy Systems", IEEE Trans. on Industrial Informatics 2016, Volume: 12, Issue: 1, 169 - 178
  • Zhan Su, Janusz Kolbusz, B. M. Wilamowski, "Linearization of Bipolar Amplifiers Based on Neural Network Training Algorithm", IEEE Trans. on Industrial Electronics (in early access pp. 1-8)
  • X. Wu, P. Rozycki, B.M. Wilamowski, "Single Layer Feedforward Networks Construction Based on Orthogonal Least Square and Particle Swarm Optimization", Artificial Intelligence and Soft Computing. ICAISC 2016. Lecture Notes in Computer Science, vol. 9692, 2016, pp.158-169
  • P. Rozycki, J. Kolbusz, R. Korostenskyi, B. M. Wilamowski, "Estimation of Deep Neural Networks Capabilities Using Polynomial Approach", Artificial Intelligence and Soft Computing. ICAISC 2016. Lecture Notes in Computer Science, vol. 9692, 2016, pp.136-147
  • M. Pukish, P. Rozycki, B. Wilamowski, "PolyNet - A Polynomial-Based Learning Machine for Universal Approximation", IEEE Transactions on Industrial Informatics, 2015, Volume: 11, Issue: 3, pp. 708 - 716 (IF=8.785)
  • C. Cecati, J. Kolbusz, P. Siano, P. Rozycki, B. Wilamowski, "A novel RBF Training Algorithm for Short-term Electric Load Forecasting: Comparative Studies", IEEE Transactions on Industrial Electronics, 2015, Volume: 62, Issue: 10, pp. 6519 - 6529 (IF=6.498)
  • X. Wu, P. Rozycki, B. Wilamowski, "Hybride Constructive Algorithm for Single – Layer Feeforward Network Learning", IEEE Transactions on Neural Networks and Learning Systems, 2015, Volume:26, Issue: 8, pp. 1659-1668 (IF=4.291)
  • G. Deshpande, P. Wang, D. Rangaprakash, B. M. Wilamowski, "Fully Connected Cascade Artificial Neural Network Architecture for Attention Deficit Hyperactivity Disorder Classification from Functional Magnetic Resonance Imaging Data", IEEE Trans. on Man System and Cybernetics, vol 45, No 12, December 2015, pp. 2668-2679
  • P. Rozycki, J. Kolbusz and B.M. Wilamowski, "Dedicated Deep Neural Network Architectures and Methods for Their Training", INES 2015, IEEE 19th International Conference on Intelligent Engineering Systems , September 3–5, 2015, Bratislava, Slovakia, pp. 73-78.
  • B. M. Wilamowski, J. Korniak, "Learning architectures with enhanced capabilities and easier training", INES 2015, IEEE 19th International Conference on Intelligent Engineering Systems , September 3–5, 2015, Bratislava, Slovakia, pp. 21-29.
  • J. Kolbusz, P. Rozycki, T. Bartczak, Bogdan M. Wilamowski, "Using Parity-N Problems as a Way to Compare Abilities of Shallow, Very Shallow and Very Deep Architectures", ICAISC’15 14-th Int. Conference "Artificial Intelligence and Soft Computing" , Zakopane, Poland, June 14-18, 2015, pp. 112-122
  • H. Yu, P. D. Reiner, T. Xie, T. Bartczak, B. M. Wilamowski, "An Incremental Design of Radial Basis Function Networks", IEEE Trans. Neural Netw. Learning Syst. 25(10): 1793-1803 (2014)
  • T. Xie, H. Yu, J. Hewlett, P. Rozycki, B. Wilamowski, "Fast and Efficient Second-Order Method for Training Radial Basis Function Networks", IEEE Trans. on Neural Networks and Learning Systems, vol. 23, no. 4, pp. 609 - 619 , Apr 2012.
  • P. Siano, C. Cecati, H. Yu, J. Kolbusz, "Real Time Operation of Smart Grids via FCN Networks and Optimal Power Flow", IEEE Transaction on Industrial Informatics, vol. 8, no. 4, 2012, pp. 944 – 952
  • D. Hunter, Hao Yu, M. S. Pukish, J. Kolbusz, B.M. Wilamowski, “Selection of Proper Neural Network Sizes and Architectures—A Comparative Study", IEEE Trans. on Industrial Informatics, vol. 8, May 2012, pp. 228-240.
  • B. M. Wilamowski, Hao Yu, and Kun Tao Chung "Parity-N Problems as a Vehicle to Compare Efficiency of Neural Network Architectures", Industrial Electronics Handbook, vol. 5 – Intelligent Systems, 2nd Edition, chapter 10, pp. 10-1 to 10-8, CRC Press 2011.
  • B. M. Wilamowski, H. Yu, "Improved Computation for Levenberg Marquardt Training", IEEE Trans. on Neural Networks, vol. 21, no. 6, pp. 930-937, June 2010.
  • B. M. Wilamowski and H. Yu, "Neural Network Learning Without Backpropagation", IEEE Trans. on Neural Networks, vol. 21, no.11, pp1793-1803, Nov. 2010.
  • B. M. Wilamowski, "Neural Network Architectures and Learning algorithms- How Not to Be Frustrated with Neural Networks" IEEE Industrial Electronics Magazine, vol 3, no 4, pp.56-63, (2009) (best paper award).
  • B. M. Wilamowski, N. J. Cotton, O. Kaynak, and G. Dundar, "Computing Gradient Vector and Jacobian Matrix in Arbitrarily Connected Neural Networks", IEEE Trans. on Industrial Electronics, vol. 55, no. 10, pp. 3784-3790, Oct 2008.
  • B. M. Wilamowski, D. Hunter, and A. Malinowski, "Solving parity-N problems with feedforward neural networks", Proc. 2003 IEEE IJCNN, 2546-2551, IEEE Press, 2003.
  • B. M. Wilamowski and R. C. Jaeger, " Implementation of RBF Networks by Feedforward Sigmoidal Neural Networks," Intelligent Engineering Systems Through Artificial Neural Networks vol. 7, ed. C. H. Dagli and others, New York 1997, pp. 183-188
  • B. M. Wilamowski and L. Torvik, " Modification of Gradient Computation in the Back-Propagation Algorithm", presented at ANNIE'93 - Artificial Neural Networks in Engineering, St. Louis, Missouri, Nov.14-17, 1993, pp. 175-180,

 

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