Recent Developments on Convergence of Online Gradient Methods for Neural Network Training. - Wei Wu 010; Zhengxue Li; Guorui Feng; Naimin Zhang; Dong Nan; Zhiqiong Shao; Jie Yang 007; Liqing Zhang; Yuesheng Xu (ISNN (1), 2004)

A New Training Algorithm for a Fuzzy Perceptron and Its Convergence. - Jie Yang 007; Wei Wu 010; Zhiqiong Shao (ISNN (1), 2005)

Dispensability of Bias for Three-layer Max-min Fuzzy Neural Networks. - Jie Yang 007; Long Li; Yan Liu 015 (ICNC (1), 2007)

Is bias dispensable for fuzzy neural networks? - Jie Yang 007; Wei Wu 010 (Fuzzy Sets Syst., 2007)

An Attribute Value Reduction Algorithm Based on Set Operations. - Mingchen Yao; Jie Yang 007; Huisheng Zhang; Wei Wu 010 (DBTA, 2009)

An intuitionistic fuzzy associative memory network and its learning rule. - Long Li; Jie Yang 007; Wei Wu 010; Tianshuang Wu (GrC, 2009)

Choice of initial bias in max-min fuzzy neural networks. - Jie Yang 007; Long Li; Yan Liu 015; Jing Wang; Wei Wu 010 (IJCNN, 2010)

A modified gradient-based neuro-fuzzy learning algorithm and its convergence. - Wei Wu 010; Long Li; Jie Yang 007; Yan Liu 015 (Inf. Sci., 2010)

Convergence of Cyclic and Almost-Cyclic Learning With Momentum for Feedforward Neural Networks. - Jian Wang 010; Jie Yang 007; Wei Wu 010 (IEEE Trans. Neural Networks, 2011)

Binary Higher Order Neural Networks for Realizing Boolean Functions. - Chao Zhang 017; Jie Yang 007; Wei Wu 010 (IEEE Trans. Neural Networks, 2011)

A Modified One-Layer Spiking Neural Network Involves Derivative of the State Function at Firing Time. - Wenyu Yang; Jie Yang 007; Wei Wu 010 (ISNN (1), 2012)

A remark on the error-backpropagation learning algorithm for spiking neural networks. - Jie Yang 007; Wenyu Yang; Wei Wu 010 (Appl. Math. Lett., 2012)

Negative effects of sufficiently small initialweights on back-propagation neural networks. - Yan Liu 015; Jie Yang 007; Long Li; Wei Wu 010 (J. Zhejiang Univ. Sci. C, 2012)

A Modified Spiking Neuron that Involves Derivative of the State Function at Firing Time. - Wenyu Yang; Jie Yang 007; Wei Wu 010 (Neural Process. Lett., 2012)

Modified gradient-based learning for local coupled feedforward neural networks with Gaussian basis function. - Yanpeng Qu; Changjing Shang; Jie Yang 007; Wei Wu 010; Qiang Shen 001 (Neural Comput. Appl., 2013)

Double parallel feedforward neural network based on extreme learning machine with L1/2 regularizer. - Atlas Khan; Jie Yang 007; Wei Wu 010 (Neurocomputing, 2014)

An Algorithm for Motif Discovery with Iteration on Lengths of Motifs. - Yetian Fan; Wei Wu 010; Jie Yang 007; Wenyu Yang; Rongrong Liu (IEEE ACM Trans. Comput. Biol. Bioinform., 2015)

Robustness of classification ability of spiking neural networks. - Jie Yang 007; Pingping Zhang; Yan Liu 015 (CoRR, 2018)

The convergence analysis of SpikeProp algorithm with smoothing L1∕2 regularization. - Junhong Zhao; Jacek M. Zurada; Jie Yang 007; Wei Wu 010 (Neural Networks, 2018)

A New Conjugate Gradient Method with Smoothing L1/2 Regularization Based on a Modified Secant Equation for Training Neural Networks. - Wenyu Li; Yan Liu 015; Jie Yang 007; Wei Wu 010 (Neural Process. Lett., 2018)

Interpretability for Neural Networks from the Perspective of Probability Density. - Lu Lu; Tingting Pan; Junhong Zhao; Jie Yang 007 (SSCI, 2019)

Extreme learning machine with local connections. - Feng Li 006; Jie Yang 007; Mingchen Yao; Sibo Yang; Wei Wu 010 (Neurocomputing, 2019)

A Genetic XK-Means Algorithm with Empty Cluster Reassignment. - Chun Hua; Feng Li 006; Chao Zhang 017; Jie Yang 007; Wei Wu 010 (Symmetry, 2019)

Learning imbalanced datasets based on SMOTE and Gaussian distribution. - Tingting Pan; Junhong Zhao; Wei Wu 010; Jie Yang 007 (Inf. Sci., 2020)

Binary Output Layer of Extreme Learning Machine for Solving Multi-class Classification Problems. - Sibo Yang; Chao Zhang 017; Yuan Bao; Jie Yang 007; Wei Wu 010 (Neural Process. Lett., 2020)

A New Oversampling Method Based on the Classification Contribution Degree. - Zhenhao Jiang; Tingting Pan; Chao Zhang 017; Jie Yang 007 (Symmetry, 2021)

A new classifier for imbalanced data with iterative learning process and ensemble operating process. - Tingting Pan; Witold Pedrycz; Jie Yang 007; Wei Wu 010; Yulin Zhang (Knowl. Based Syst., 2022)

A New Fuzzy Spiking Neural Network Based on Neuronal Contribution Degree. - Fang Liu 017; Jie Yang 007; Witold Pedrycz; Wei Wu 010 (IEEE Trans. Fuzzy Syst., 2022)

Spiking Neural Network Regularization With Fixed and Adaptive Drop-Keep Probabilities. - Junhong Zhao; Jie Yang 007; Jun Wang 002; Wei Wu 010 (IEEE Trans. Neural Networks Learn. Syst., 2022)

Oversampling method based on GAN for tabular binary classification problems. - Jie Yang 007; Zhenhao Jiang; Tingting Pan; Yueqi Chen; Witold Pedrycz (Intell. Data Anal., 2023)

A novel parallel merge neural network with streams of spiking neural network and artificial neural network. - Jie Yang 007; Junhong Zhao (Inf. Sci., 2023)

A new boundary-degree-based oversampling method for imbalanced data. - Yueqi Chen; Witold Pedrycz; Jie Yang 007 (Appl. Intell., 2023)

Coding Method Based on Fuzzy C-Means Clustering for Spiking Neural Network With Triangular Spike Response Function. - Fang Liu 017; Witold Pedrycz; Chao Zhang 017; Jie Yang 007; Wei Wu 010 (IEEE Trans. Fuzzy Syst., 2023)

An improved generative adversarial network to oversample imbalanced datasets. - Tingting Pan; Witold Pedrycz; Jie Yang 007; Jian Wang 010 (Eng. Appl. Artif. Intell., 2024)

Generative adversarial networks for multi-fidelity matrix completion with massive missing entries. - Zongqi Liu; Xueguan Song; Jie Yang 007; Chao Zhang 017; Dacheng Tao (Inf. Fusion, 2024)

Zero-order fuzzy neural network with adaptive fuzzy partition and its applications on high-dimensional problems. - Bingjie Zhang 001; Jian Wang 010; Chao Zhang 017; Jie Yang 007; Tufan Kumbasar; Wei Wu 010 (Neurocomputing, 2024)

A New Oversampling Method Based on Triangulation of Sample Space. - Yueqi Chen; Witold Pedrycz; Jian Wang 010; Chao Zhang 017; Jie Yang 007 (IEEE Trans. Syst. Man Cybern. Syst., 2024)

Generative adversarial physics-informed neural networks for solving forward and inverse problem with small labeled samples. - Wensheng Li; Chuncheng Wang; Hanting Guan; Jian Wang 010; Jie Yang 007; Chao Zhang 017; Dacheng Tao (Comput. Math. Appl., 2025)