Wang, J., Xu, C., Yang, X., Zurada, J.M., Novel Pruning Algorithm for Smoothing Feedforward Neural Networks based on Group Lasso Method, IEEE Transactions on Neural Networks and Learning Systems, vol. 29, No. 5, pp. 2012-2024, May 2018, doi.org/10.1109/TNNLS.2017.2748585
Vu, V-P, Wang, W.J., Zurada, J.M., Chen, H.C., and Chiu, C.H., Unknown Input Method Based Observer Synthesis for a Discrete Time Uncertain T-S Fuzzy System, IEEE Transactions on Fuzzy Systems, vol. 26, No. 2, April 2018, pp. 761-770, DOI:10.1109/TFUZZ.2017.2724507, pre-print.
Ayinde, B.O., and Zurada,J.M., Nonredundant Sparse Feature Extraction using Autoencoders with Receptive Fields Clustering, Neural Networks, vol. 93, pp. 99-109, September 2017, doi.org/10.1016/j.neunet.2017.04.012, pre-print.
Mirinejad, H., Gaweda, A.E., Brier, M.E., Zurada, J.M., Inanc, T., Individualized Drug Dosing Using RBF-Galerkin Method: Case of Anemia Management in Chronic Kidney Disease, Computer Methods and Programs in Biomedicine, vol. 148, September 2017, pp.45-53, doi.org/10.1016/j.cmpb.2017.06.008, pre-print.
Hou, J-Q., Ong, Y-S., Feng, L., Zurada, J.M., An Evolutionary Transfer Reinforcement Learning Framework for Multi-Agent System, IEEE Trans. on Evolutionary Computation, vol. 21, No. 4, August 2017, pp.601-615. DOI: 10.1109/TEVC.2017.2664665, pre-print.
Ayinde, B. O., & Zurada, J. M., Discovery Through Constraints: Imposing Constraints on Autoencoders for Data Representation and Dictionary Learning, IEEE Systems, Man, and Cybernetics Magazine, 3(3), 13-24, July 2017. DOI: 10.1109/MSMC.2017.2701578, pre-print.
Vu, V-P, Wang, W.J., Chen, H.C., and Zurada, J.M., Unknown Input Method Based Observer Synthesis for a Polynomial T-S Fuzzy Model System with Uncertainties, IEEE Transactions on Fuzzy Systems, July 2017, (in press), DOI: 10.1109/TFUZZ.2017.2688388, pre-print.
Li, F., Zurada, J.M., Wu, W., Input Layer Regularization of Multilayer Feedforward Neural Networks, IEEE Access, May 30, 2017, DOI: 10.1109/ACCESS.2017.2713389
Wang, J., Cai, Q., Chang, Q., Zurada, J.M., Convergence analyses on sparse feedforward neural networks via group lasso regularization, Information Sciences, vol. 381, March 2017, pp. 250-269, DOI: 10.1016/j.ins.2016.11.020, pre-print.
Ayinde, B.O., and Zurada, J.M., Deep Learning of Constrained Autoencoders for Enhanced Understanding of Data, IEEE Trans. on Neural Networks and Learning Systems, 2017, DOI: 10.1109/TNNLS.2017.2747861, pre-print.
Wang, J., Ye, Z., Gao, W., Zurada, J.M., Boundedness and convergence analysis of weight elimination for cyclic training of neural networks , Neural Networks, vol. 82, October 2016, pp. 49-61, DOI: dx.doi.org/10.1016/j.neunet.2016.06.005
Fan, Q., Wu, W., Zurada, J.M., Convergence of batch gradient learning with smoothing regularization and adaptive momentum for neural networks, SpringerPlus, vol. 5, no.1, 2016, p. 295-311, DOI: 10.1186/s40064-016-1931-0
Alansary, A., Ismail, M., Soliman, A., Khalifa, F., Nitzken, M., Elnakib A., Mostapha, M., Austin Black, A., Stinebruner, K., Casanova, M.F., Zurada, J.M., El-Baz, A., Infant Brain Extraction in T1-weighted MR Images using BET and Refinement using LCDG and MGRF Models, IEEE Journal on Biomedical and Health Informatics, vol. 20, no. 3, pp. 925-935, May 2016, DOI: 10.1109/JBHI.2015.2415477
Hosseini-Asl, E., Zurada, J.M., Gimel farb, G., El-Baz, A., 3D Lung Segmentation by Incremental Constrained Nonnegative Matrix Factorization, IEEE Transactions on Biomedical Engineering, vol.63, no.5, pp.952-963, May 2016, DOI: 10.1109/TBME.2015.2482387, pre-print.
Hosseini-Asl, E., Zurada, J.M., Nasraoui, O., Deep Learning of Part-based Representation of Data Using Sparse Autoencoders with Nonnegativity Constraints, IEEE Transactions on Neural Networks and Learning Systems, vol.27, no.12, pp.2486-98, 2016, DOI: 10.1109/TNNLS.2015.2479223 pre-print.
Wang, J., Yang, G.L, Liu, S., Zurada, J.M, Convergence Analysis of Multilayer Feedforward Networks Trained with Penalty Terms, Journal of Applied Computer Science Methods, Vol. 7, No. 2, 2015, pp 89-103, DOI: 10.1515/jacsm-2015-0011 pre-print.
Teng, T. H., Tan, A. H., Zurada, J. M., Self-Organizing Neural Networks Integrating Domain Knowledge and Reinforcement Learning, IEEE Transactions on Neural Networks and Learning Systems, vol.26, no.5, May 2015, pp.889-902, pre-print.
Zhou W., Zurada J.M., New Stability Condition for Discrete-Time Fully Coupled Neural Networks with Multivalued Neurons, Neurocomputing, vol. 166, October 20, 2015, pp.38-43, DOI:10.1016/j.neucom.2015.04.036, pre-print.
Alansary, A., Ismail, M., Soliman, A., Khalifa, F., Nitzken, M., Elnakib A., Mostapha, M., Austin Black, A., Stinebruner, K., Casanova, M.F., Zurada, J.M., El-Baz, A., Infant Brain Extraction in T1-weighted MR Images using BET and Refinement using LCDG and MGRF Models, IEEE Journal on Biomedical and Health Informatics, vol. PP, No.99, 2015, preprint at DOI: 10.1109/JBHI.2015.2415477, pre-print.
Kang, M-J., Boo, C-J., Kim, H-C., Zurada, J.M., A Nonlinear Regression based Approach for Multilayer Soil Parameter Estimation, International Journal of Control and Automation, Vol.7, No.2 (2014), pp.65-74, pre-print.
Teng, T. H., Tan, A. H., Zurada, J.M., Self-Organizing Neural Networks Integrating Domain Knowledge and Reinforcement Learning, IEEE Transactions on Neural Networks and Learning Systems, vol. PP, no. 99, pp. 1-1, 2014, pre-print.
Torabi, A.J., Er, M.J., Li, X., Lim, B.S., Zhai, L., Oentaryo, R.J., Pen, G.O., Zurada, J.M., A Survey on Artificial Intelligence-Based Modeling Techniques for High Speed Milling Processes, IEEE Systems Journal, vol. PP, no. 99, pp. 1-12, 2014, pre-print.
Akabua, E., Inanc, T., Brier, M.E., Gaweda, A., Zurada, J.M., Individualized Model Discovery: The Case of Anemia Patients, Computer Methods and Programs in Biomedicine, vol.118, No.1, January 2015, pp. 23-33, pre-print.
Chorowski, J., Zurada, J.M., Learning understandable neural networks with non-negative weight constraints, IEEE Transactions on Neural Networks and Learning Systems, vol. 26, No.1, January 2015, pp. 62-69, pre-print.
Li, B., Li, H., Zurada, J.M., Cross-layer Design of Joint Beamforming and Random Network Coding in Wireless Multicast Networks, IEEE Communications Letters, vol. 18, No. 12, 2014, pp. 2173-2176, pre-print.
Fan, Q., Zurada, J.M., Wu, W., Convergence of online gradient method for feedforward neural networks with Smoothing L1/2 regularization penalty, Neurocomputing, 2014, vol. 131, pp. 208-216, pre-print.
Nitzken, M., Casanova, M., Gimel’farb, G., Inanc, T., Zurada, J.M., El-Baz, A.S., Shape Analysis of Human Brain: A Brief SurveyIEEE Transactions on Information Technology in Biomedicine, vol. 18, No.4, July 2014, pp.1337-54, pre-print.
Wu, W., Fan Q., Wang, J., Yang, D., Liu, Y., Zurada, J.M., Batch gradient method with smoothing L � regularization for training of feedforward neural networksNeural Networks, vol. 50, February 2014, pp. 72-78, pre-print.
Chorowski, J., Wang, J., Zurada, J.M., Review and performance comparison of SVM- and ELM-based classifiersNeurocomputing, 2014, vol. 128, pp. 507-516, pre-print.
Hosseini-Asl, E., Zurada, J.M., Multiplicative Algorithm for Correntropy-Based Nonnegative Matrix FactorizationJournal of Applied Computer Science Methods, vol. 5, no. 2, 2013, pp. 89-104, pre-print.
W. Zhou, J.M. Zurada, A Competitive Layer Model for Cellular Neural Networks, Neural Networks, vol. 33 (2012), pp.216-227
J. Wang, W. Wu, J.M. Zurada, Computational Properties and Convergence Analysis of BPN for Cyclic and Almost Cyclic Learning with Penalty, Neural Networks, vol. 33, (2012), pp.127-135
E. Akabua, T. Inanc, A. Gaweda, M.R. Brier, K. Seongho, J.M. Zurada, Robust Identification Approach to Individualized Anemia Modeling, Journal of Applied Computer Science Methods, vol. 3, No.2, 2011, pp.65-75
J. Chorowski, J.M. Zurada, Extracting Rules from Neural Networks as Decision Diagrams, Neural Networks, IEEE Transactions on, vol.22, no.12, pp.2435-2446, Dec. 2011 DOI 10.1109/TNN.2011.2106163
Wang J., Wu W., J.M. Zurada, Deterministic convergence of conjugate gradient method for feedforward neural networks, Neurocomputing, vol. 74, 2011, pp. 2368-2376
M.N. Nguyen, J.M. Zurada, J.C. Rajapakse, Toward Better Understanding of Protein Secondary Structure: Extracting Prediction Rules, IEEE Trans. on Computational Biology and Bioinformatics, March 2010, posted on IEEE Xplore, DOI 10.1109/TCBB.2010.16
Z. Zhu, Y.S. Ong, J.M. Zurada,Identification of Full and Partial Class Relevant Genes, IEEE Trans. on Computational Biology and Bioinformatics, 2010, pp.263-277, DOI 10.1109/TCBB.2008.105
W. Zhou, J.M. Zurada, Competitive Layer Model of Discrete-Time Recurrent Neural Networks with LT Neurons, Neural Computation, August 2010, Vol. 22, No. 8, pp. 2137-2160. Download Matlab code for the article.
J. Wojtusiak, J. Chorowski, J. Pietrzykowski, J.M. Zurada, Searching and Reasoning with Distributed Resources in Computational Intelligence, Journ. of Applied Computer Science Methods, 2009, vol.1, No.2, pp.5-19
Zhou, W., Zurada, J.M., A Class of Discrete-Time Recurrent Neural Networks with Multivalued Neurons, Neurocomputing, (72) October 2009, pp.2782-88
Zhou, W., Zurada, J.M., Discrete-Time Recurrent Neural Networks With Complex-Valued Linear Threshold Neurons, IEEE Transactions on Circuits and Systems-II Express Briefs, vol.56, No.8, August 2009, pp.669-73
Mazurowski, M.A., Zurada, J.M., Tourassi, G.D., An adaptive incremental approach to constructing ensemble classifiers: application in an information-theoretic computer-aided decision system for detection of masses in mammograms, Medical Physics, vol.36, issue 7, July 2009, pp.2976-84
J. M. Zurada, P.A. Habas and M.K.Muezzinoglu, Collaborative Web-based Workspaces for an Academic Department, Journal of Applied Computer Science Methods, vol. 1, No.1, 2009, pp.25-36
P. A. Est�vez, M. Tesmer, C. A. Perez, J. M. Zurada, Adaptive Mutual Information Feature Selection, IEEE Transactions on Neural Networks, vol.20, No.2, 2009, pp.189-201
J. M. Zurada, M. A. Mazurowski, J. Wojtusiak, R. Ragade, A. Abdullin, and J. Gentle, Building virtual community in computational intelligence and machine learning, IEEE Computational Intelligence Magazine, Vo. 4, No.1, 2009, pp.43-46, 54
M. Majewski, J. M. Zurada, “Sentence recognition using artificial neural networks“, Knowledge-Based Systems 21, 2008, pp.629-635
M. A. Mazurowski, J. M. Zurada, G. D. Tourassi, Selection of examples in case-based computer-aided decision systems , Phys. Med. Biol., vol. 53, pp. 6079-6096, November 2008.
I. Aizenberg, D. Paliy, J. M. Zurada, J. Astola, Blur identification by multilayer neural network based on multi-valued neurons, IEEE Tran. Neural Networks, vol. 19, pp. 883-898, May 2008.
M. A. Mazurowski, P. A. Habas, J. M. Zurada, J.Y. Lo, J.A. Baker, G. D. Tourassi, Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance, Neural Netw., vol. 21, pp. 427-436, March-April 2008.
M. A. Mazurowski, P. A. Habas, J. M. Zurada, G. D. Tourassi, Decision optimization of case-based computer aided decision systems using genetic algorithms with application to mammography , Phys. Med. Biol., vol. 53, pp. 895-908, January 2008.
A. L. P. Tay, J. M. Zurada, L. P. Wing, J. Xu, The hierarchical fast learning artificial neural network (HieFLANN) – an autonomous platform for hierarchical neural network construction, IEEE Trans. on Neural Networks, vol.18, no.6, pp.1645-1657, November 2007.
Y. Hou, J. M. Zurada, W. Karwowski, W. S. Marras, K. Davis, “Identification of Key Variables Using Fuzzy Average With Fuzzy Cluster Distribution,” IEEE Trans. Fuzzy Systems, vol. 15, no. 4, pp. 673-685, August 2007.
J. T. Kwok, I. W.-H. Tsang, and J. M. Zurada, “A class of single-class minimax probability machines for novelty detection,” IEEE Trans. Neural Networks, vol. 18, no. 3, pp. 778-785, May 2007.
L. Wang, W. Liu, H. Shi, and J. M. Zurada, “Cellular neural networks with transient chaos,” IEEE Trans. Circuits Syst. II – Express Briefs, vol. 54, no. 5, pp. 440-444, May 2007.
A. E. Gaweda, M. K. Muezzinoglu, G. R. Aronoff, A. A. Jacobs, J. M. Zurada, and M. E. Brier, “Using clinical information in goal-oriented learning,” IEEE Eng. Med. Biol. Mag., vol. 26, no. 2, pp. 27-36, March-April 2007.
P. A. Habas, J. M. Zurada, A. S. Elmaghraby, and G. D. Tourassi, “Reliability analysis framework for computer-assisted medical decision systems,” Med. Phys., vol. 34, no. 2, pp. 763-772, February 2007.
Y. Hou, J. M. Zurada, W. Karwowski, W. S. Marras, and K. Davis, “Estimation of the dynamic spinal forces using a recurrent fuzzy neural network,” IEEE Trans. Syst. Man Cybern. Part B-Cybern., vol. 37, no. 1, pp. 100-109, February 2007.
J. Zhou, M. J. Er, and J. M. Zurada, “Adaptive neural network control of uncertain nonlinear systems with nonsmooth actuator nonlinearities, Neurocomputing, vol. 70, pp. 1062-1070, January 2007.
W. Karwowski, A. E. Gaweda, W. S. Marras, K. Davis, J. M. Zurada, and D. Rodrick, “A fuzzy relational rule network modeling of electromyographical activity of trunk muscles in manual lifting based on trunk angels, moments, pelvic tilt and rotation angles,” Int. J. Ind. Ergon., vol. 36, no. 10, pp. 847-859, October 2006.
I. W.-H. Tsang, J. T.-Y. Kwok, and J. M. Zurada, “Generalized core vector machines,” IEEE Trans. Neural Networks, vol. 17, no. 5, pp. 1126-1140, September 2006.
M. K. Muezzinoglu and J. M. Zurada, “RBF-based neurodynamic nearest neighbor classification in real pattern space,” Pattern Recognition, vol. 39, pp. 747-760, May 2006.
A. E. Gaweda, M. K. Muezzinoglu, G. R. Aronoff, A. A. Jacobs, J. M. Zurada, and M. E. Brier, “Individualization of pharmacological anemia management using reinforcement learning,” Neural Networks, vol. 18, no. 5-6, pp. 826-834, July-August 2005.
M. K. Muezzinoglu, C. Guzelis, and J. M. Zurada, “An energy function-based design method for discrete Hopfield associative memory with attractive fixed points,” IEEE Trans. Neural Networks, vol. 16, no. 2, pp. 370-378, March 2005.
A. Lozowski, M. Lysetskiy, and , “Signal processing with temporal sequences in olfactory systems,” IEEE Trans. Neural Networks, vol. 15, no. 5, pp. 1268-1275, September 2004.
M. P. Wachowiak, R. Smolikova, Y. Zheng, J. M. Zurada, and A. S. Elmaghraby, “An approach to multimodal biomedical image registration utilizing particle swarm optimization,” IEEE Trans. Evol. Comput., vol. 8, no. 3, pp. 289-301, June 2004.
R. Smolikova, M. P. Wachowiak, and J. M. Zurada, “An information-theoretic approach to estimating ultrasound backscatter characteristics,” Comput. Biol. Med., vol. 34, no. 4, pp. 355-370, June 2004.
W. Duch, R. Setiono, and J. M. Zurada, “Computational intelligence methods for rule-based data understanding,” Proc. IEEE, vol. 92, no. 5, pp. 771-805, May 2004. [Front cover of the Issue] [Prolog by J. Esch]
M. Lysetskiy and J. M. Zurada, “Bifurcating neuron: computation and learning,” Neural Networks, vol. 17, no. 2, pp. 225-232, March 2004.
M. K. Muezzinoglu, C. Guzelis, and J. M. Zurada, “A new design method for complex-valued multistate Hopfield associative memory,” IEEE Trans. Neural Networks, vol. 14, no. 4, pp. 891-899, July 2003.
A. E. Gaweda, A. A. Jacobs, M. E. Brier, and J. M. Zurada, “Pharmacodynamic population analysis in chronic renal failure using artificial neural networks – a comparative study,” Neural Networks, vol. 16, no. 5-6, pp. 841-845, June-July 2003.
A. E. Gaweda and J. M. Zurada, “Data-driven linguistic modeling using relational fuzzy rules,” IEEE Trans. on Fuzzy Systems, vol. 11, no. 1, pp. 121-134, February 2003.
M. Lysetskiy, A. Lozowski, and J. M. Zurada, “Temporal-to-spatial dynamic mapping, flexible recognition, and temporal correlations in an olfactory cortex model,” Biol. Cybern., vol. 87, no. 1, pp. 58-67, July 2002.
M. Lysetskiy, A. Lozowski, and J. M. Zurada, “Invariant recognition of spatio-temporal patterns in the olfactory system model,” Neural Process. Lett., vol. 15, no. 3, pp. 225-234, July 2002.
M. P. Wachowiak, R. Smolikova, J. M. Zurada, and A. S. Elmaghraby, “Estimation of K distribution parameters using neural networks,” IEEE Trans. Biomed. Eng., vol. 49, no. 6, pp. 617-623, June 2002.
R. Setiono, L. W. Kheng, and J. M. Zurada, “Extraction of rules from artificial neural networks for nonlinear regression,” IEEE Trans. Neural Networks, vol. 13, no. 3, pp. 564-577, May 2002.
H. Wakuya and J. M. Zurada, “Bi-directional computing architecture for time series prediction,” Neural Networks, vol. 14, no. 9, pp. 1307-1321, November 2001.
Y. Tan, J. Wang, and J. M. Zurada, “Nonlinear blind source separation using a radial basis function network,” IEEE Trans. Neural Networks, vol. 11, no. 1, pp. 124-134, January 2001.
Y. Li, J. Wang, and J. M. Zurada, “Blind extraction of singularly mixed source signals,” IEEE Trans. Neural Networks, vol. 11, no. 6, pp. 1413-1422, November 2000.
D. A. Miller and J. M. Zurada, “A dynamical system perspective of structural learning with forgetting,” IEEE Trans. Neural Networks, vol. 9, no. 3, pp. 508-515, May 1998.
K. Graviss and J. M. Zurada, “A neural network controller for optimal temperature control of household refrigerators,” Intell. Autom. Soft Comput., vol. 4, no. 3, pp. 357-372, 1998.
J. M. Zurada, A. Malinowski, and S. Usui, “Perturbation method for deleting redundant inputs of perceptron networks,” Neurocomputing, vol. 14, no. 2, pp. 177-193, February 1997.
R. Kozma, A. Malinowski, J. M. Zurada, and M. Kitamura, “Evaluating the performance of artificial neural networks trained by structural learning,” Australian Journal of Intelligent Information Processing Systems, vol. 3, no. 2, pp. 10-15, Winter 1996.
S. Jankowski, A. Lozowski, and J. M. Zurada, “Complex-valued multistate neural associative memory,” IEEE Trans. Neural Networks, vol. 7, no. 6, pp. 1491-1496, November 1996.
Y. M. Kadah, A. A. Farag, J. M. Zurada, A. Youssef, and A. Badawi, “Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images,” IEEE Trans. Med. Imag., vol. 15, no. 4, pp. 466-478, August 1996.
J. M. Zurada, I. Cloete, and E. van der Poel, “Generalized Hopfield networks for associative memories with multi-valued stable states,” Neurocomputing, vol. 13, no. 2-4, pp. 135-149, October 1996.
M. E. Brier, J. M. Zurada, and G. R. Aronoff, “Neural network – predicted peak and trough gentamycin concentration,” Pharm. Res., vol. 12, no. 3, pp. 406-412, March 1995.
J. M. Zurada and A. Malinowski, “Multilayer perceptron networks: selected aspects of training optimization,” Applied Mathematics and Computer Science, vol. 4, no. 3, pp. 281-307, 1994.