膜计算（脉冲神经P系统）、神经网络（脉冲神经网络、储层计算）、形态计算、计算机视觉与图像处理、机器学习（深度学习）等。

硕士研究招生方向:

(1) 深度学习（机器学习）与计算机视觉、图像处理（含医学图像处理、遥感图像出等）；

(2) 深度学习（机器学习）与自然语言处理；

(3) 膜计算（脉冲神经P系统）模型、算法与应用;

(4) 脉冲神经网络、储层计算、神经形态计算与应用；

(5) 语言大模型、多模态大模型与应用。

对以上方向感兴趣同学，欢迎加入研究团队。

（团队成员：彭宏、罗晓晖、李兵、刘志才、郭承刚、杨妮晶、邓萍、何冠霖、夏梅宸）

(团队名额较多，欢迎联系我及团队其他老师)

1. 学术论文

    以第1作者（含通讯作者）或合作者身份发表有关膜计算、图像处理等的期刊或会议论文140余篇，其中SCI/EI收录80余篇。

    学术论文成果网页:

(1) Google学术：https://scholar.google.com/citations?user=uBD6HDgAAAAJ&hl=zh-CN

(2) Researchgate：https://www.researchgate.net/profile/Hong_Peng4

    近年部分学术论文：

[1] Q. Liu, H. Peng, L. Long, J. Wang, Q. Yang, M.J. Pérez-Jiménez, D. Orellana-Martín. Nonlinear spiking neural systems with autapses for predicting chaotic time series. IEEE Transactions on Cybernetics, 2023.. (SCI一区、IF=19.118、通讯作者) 

(DOI: 10.1109/TCYB.2023.3270873) 

[2] Q. Liu, L. Long, H. Peng, J. Wang, Q. Yang, X. Song, A. Riscos-Núñez, M.J. Pérez-Jiménez. Gated spiking neural P systems for time series forecasting. IEEE Transactions on Neural Networks and Learning Systems, 2022. (SCI一区、IF=14.255、通讯作者) 

(DOI: 10.1109/TNNLS.2021.3134792) 

[3] Q. Liu, Y. Huang, Q. Yang, H. Peng, J. Wang. An attention-aware long short-term memory-like spiking neural model for sentiment analysis. International Journal of Neural Systems, 2023. (SCI二区、IF=6.325、通讯作者) 

[4] Y. Huang, H. Peng, Q. Liu, Q. Yang, J. Wang, D. Orellana-Martín, M.J. Pérez-Jiménez. Attention-enabled gated spiking neural P model for aspect-level sentiment classification. Neural Networks, 157, 2023, 437-443. (SCI一区、IF=9.657、通讯作者)

[5] Y. Huang, Q. Liu, H. Peng, J. Wang, Q. Yang, D. Orellana-Martín. Sentiment classification using bidirectional LSTM-SNP model and attention mechanism. Expert Systems with Applications, 221, 2023, 119730. (SCI一区、IF=8.665、通讯作者)

[6] R. Xian, R. Lugu, H. Peng, Q. Yang, X. Luo, J. Wang. Edge detection method based on nonlinear spiking neural systems. International Journal of Neural Systems, 33(1), 2250060, 2022.  (SCI二区、IF=6.325、通讯作者)

[7] P. Deng, T. Li, D. Wang, H. Wang, H. Peng, S.-J. Horng. Multi-view clustering guided by unconstrained non-negative matrix factorization. Knowledge-Based Systems,

266, 2023, 110425.  (SCI一区、IF=8.139)

[8] B. Yang, L. Qin, H. Peng, C. Guo, X. Luo, J. Wang. SDDC-Net: A U-shaped deep spiking neural P convolutional network for retinal vessel segmentation. Digital Signal Processing, 136, 2023, 104002. (SCI三区、IF=2.920)

[9] Y. Zhang, Q. Yang, Z. Liu, H. Peng, J. Wang. A prediction model based on gated nonlinear spiking neural system. International Journal of Neural Systems, 33(6), 2023, 2350029. (SCI二区、IF=6.325、通讯作者)

[10] Q. Liu, L. Long, Q. Yang, H. Peng, J. Wang, X. Luo. LSTM-SNP: A long short-term memory model inspired from spiking neural P systems. Knowledge-Based Systems, 235, 2022, 107656. (SCI一区、IF=8.038、通讯作者)

[11] Y. Cai, S. Mi, J. Yan, H. Peng, X. Luo, Q. Yang, J. Wang. An unsupervised segmentation method based on dynamic threshold neural P systems for color images. Information Sciences, 587, 2022, 473-484. (SCI一区、IF=6.795、通讯作者)

[12] L. Long, Q. Liu, H. Peng, Q. Yang, X. Luo, J. Wang, X. Song. A time series forecasting approach based on nonlinear spiking neural systems. International Journal of Neural Systems, 32(8), 2250020, 2022. (SCI二区、IF=6.325、通讯作者)

[13] X. Chen, H. Peng, J. Wang, F. Hao. Supervisory control of discrete event systems under asynchronous spiking neuron P systems, Information Sciences, 597, 253-273, 2022. (SCI一区、IF=8.233、通讯作者)

[14] J. Yan, L. Zhang, H. Peng, J. Wang. A novel edge detection method based on dynamic threshold neural P systems with orientation. Digital Signal Processing, 127, 103526, 2022.  (SCI三区、IF=2.920)

[15] L. Long, Q. Liu, H. Peng, J. Wang, Q. Yang. Multivariate time series forecasting method based on nonlinear spiking neural P systems and non-subsampled shearlet transform. Neural Networks 152 (2022) 300-310. (SCI一区、IF=9.657、通讯作者)

[16] L. Long, R. Lugu, X. Xiong, Q. Liu, H., Peng, J. Wang, D. Orellana-Martín, M.J. Pérez-Jiménez. Echo spiking neural P system. Knowledge-Based Systems, 253, 2022, 109568.

(SCI一区、IF=8.038、通讯作者)

[17] B. Li, H. Peng, J. Wang. A novel fusion method based on dynamic threshold neural P systems and nonsubsampled contourlet transform for multi-modality medical images. Signal Processing, 178, 2021, 107793: 1-13. (ESI、SCI二区、IF=4.389、通讯作者)

[18] B. Li, H. Peng, X. Luo, J. Wang, X. Song, M.J. Pérez-Jiménezz and A. Riscos-Núñez. Medical image fusion method based on coupled neural p systems in nonsubsampled shearlet transform domain. International Journal of Neural Systems,, 31(1), 2021, 2050050: 1-17.  (SCI一区、IF=6.4、通讯作者)

[19] Z. Lv, T. Bao, N. Zhou, H. Peng, X. Huang, A. Riscos-Núñez, M.J. Pérez-Jiménez. Spiking neural p systems with extended channel rules. International Journal of Neural Systems,31(1), 2021, 2050049:1-13. (SCI一区、IF=6.4、通讯作者)

[20] X. Song, L. Valencia-Cabrera, H. Peng, J. Wang. Spiking Neural P Systems with Autapses. Information Sciences, 2021, 570, 383-402. (SCI一区、IF=5.91)

[21] X. Song, L. Valencia-Cabrera, H. Peng, J. Wang, M.J. Pérez-Jiménez, Spiking neural P systems with delay on synapses, International Journal of Neural Systems, 31(1), 2021, 2050042: 1-19. (SCI一区、IF=5.604)

[22] H. Peng, T. Bao, X. Luo, J. Wang, X. Song, A. Riscos-Núñez, M.J. Pérez-Jiménez. Dendrite P systems, Neural Networks, 127, 2020, 110-120. (SCI二区、IF=5.785、第一作者)

[23] B. Li, H. Peng, J. Wang, X. Huang. Multi-focus image fusion based on dynamic threshold neural P systems and surfacelet transform. Knowledge-Based Systems, 196, 2020, 105794: 1-12. (SCI一区、IF=5.101、通讯作者)

[24] J. Yang, H. Peng, X. Luo, J. Wang, Stochastic numerical p systems with application in data clustering problems, IEEE Access, 8(1), 31507-31518, 2020. (SCI二区、IF=4.098、通讯作者)

[25] H. Peng, Z. Lv, B. Li, X. Luo, J. Wang, X. Song, T. Wang, M.Pérez-Jiménez, A.Riscos-Núñez.Nonlinear Spiking Neural P Systems, International Journal of Neural Systems, 30(10), 2020, 2050008: 1-17. (SCI一区、IF=6.4、第一作者)

[26] H. Peng, B. Li, J. Wang, X. Song, T. Wang, L. Valencia-Cabrera, I. Pérez-Hurtado, A. Riscos-Núñez, M.J. Pérez-Jiménez. Spiking neural P systems with inhibitory rules. Knowledge-Based Systems, 188, 2020, 105064: 1-10. (SCI一区、IF=5.101、第一作者)

[27] X. Song, J. Wang, H. Peng, G. Ning, Z. Sun, T. Wang, F. Yang. Small universal asynchronous spiking neural P systems with multiple channels. Neurocomputing, 378, 2020, 1-8. (SCI二区、IF=4.072)

[28] H. Peng, J. Wang. Coupled neural P systems. IEEE Transactions on Neural Networks and Learning Systems, 30(6), 1672-1682, 2019. (SCI一区、IF=7.982、第一作者)

[29] H. Peng, J. Wang, M.J. Pérez-Jiménez, A. Riscos-Núñez. Dynamic threshold neural P systems. Knowledge-Based Systems, 163, 2019, 875–884. (SCI二区、IF=4.396、第一作者)

[30] J. Wang, H. Peng, W. Yu, J. Ming, M.J. Pérez-Jiménez, C. Tao, X. Huang. Interval-valued fuzzy spiking neural P systems for fault diagnosis of power transmission networks. Engineering Applications of Artificial Intelligence, 82, 102–109, 2019. (SCI二区、IF=3.526、通讯作者)

[31] T. Wang, X. Wei, T. Huang, J. Wang, H. Peng, M.J. Pérez-Jiménez, L. Valencia Cabrera. Modeling fault propagation paths in power systems: a new framework based on event SNP systems with neurotransmitter concentration. IEEE Access, 7(1), 12798-12808, 2019. (SCI二区、IF=3.557)

[32] H. Peng, J. Wang, J. Ming, P. Shi, M.J. Pérez-Jiménez, et al. Fault diagnosis of power systems using intuitionistic fuzzy spiking neural P systems. IEEE Transactions on Smart Grid, 9(5), 4777-4784, 2018. (SCI一区、IF=7.365、第一作者)

[33] H. Peng, J. Yang, J. Wang, T. Wang, Z. Sun, X. Song, X. Luo, X. Huang. Spiking neural P systems with multiple channels. Neural Networks, 95, 66-71，2017. (SCI一区、IF=7.197、第一作者)

[34] H. Peng, J. Wang, P. Shi, M.J. Pérez-Jiménez, A. Riscos-Núñez. Fault diagnosis of power systems using fuzzy tissue-like P systems. Integrated Computer-Aided Engineering, 24(4), 401-411, 2017. (SCI二区、IF=3.667、第一作者)

[35] H. Peng, P. Shi, J. Wang, A. Riscos-Núñez, M.J. Pérez-Jiménez. Multiobjective fuzzy clustering approach based on tissue-like membrane systems. Knowledge-Based Systems, 125, 74-82, 2017. (SCI二区、IF=4.396、第一作者)

[36] H. Peng, J. Wang. A hybrid approach based on tissue P systems and artificial bee colony for IIR system identification. Neural Computing and Applications, 28(9), 2675-2685, 2017. (SCI二区、IF=4.215、第一作者)

[37] H. Peng, J. Wang, P. Shi, M.J. Pérez-Jiménez, et al. An extended membrane system with active membrane to solve automatic fuzzy clustering problems, International Journal of Neural Systems, 26(3), 1650004, 1-17, 2016. (SCI一区、IF=4.580、第一作者)

[38] J. Wang, P. Shi, H. Peng, Membrane computing model for IIR filter design. Information Sciences, 329, 164–176, 2016. (SCI二区、IF=4.305、通讯作者) 

[39] H. Peng, J. Wang, M.J. Pérez-Jiménez, et al. An unsupervised learning algorithm for membrane computing, Information Sciences, 304, 80-91, 2015. (SCI二区、IF=4.305、第一作者)

[40] H. Peng, J. Wang, M.J. Pérez-Jiménez, A. Riscos-Núñez. The framework of P systems applied to solve optimal watermarking problem. Signal Processing, 101, 256-265, 2014. (SCI二区、IF=3.470、第一作者)

[41] JJ. Wang, P. Shi, H. Peng, M.J. Pérez-Jiménez, T. Wang. Weighted fuzzy spiking neural P systems. IEEE Transactions on Fuzzy Systems, 21(2), 209-220, 2013. (SCI一区、IF=8.415、通讯作者)

[42] H. Peng, J. Wang, M.J. Pérez-Jiménez, H. Wang, J. Shao, T. Wang. Fuzzy reasoning spiking neural P system for fault diagnosis. Information Sciences, 235, 106-116, 2013. (SCI一区、IF=4.305、第一作者)

2. 研究项目

　作为项目负责人或主研承担国家自然基金项目、教育部春晖计划项目、四川省科技支撑计划项目、四川省国际合作项目、产学研项目等40余项。承担或主研的部分项目如下：

(1) 国家自然科学基金：脉冲神经膜系统的深度学习模型构建（2022-2025），负责人； 

(2) 国家自然科学基金：大脑启发的膜计算模型及学习机理构建（2021-2024），主研； 

(3) 国家自然科学基金：模糊与自适应膜计算模型及算法研究（2012-2015），主研；

(4) 国家自然科学基金：膜计算的非监督学习模型与机理研究（2015-2018），主研；
(5) 四川省科技厅支撑计划项目：膜计算框架下的数字图像处理关键技术研究（2013-2014），负责人；
(6) 四川省科技厅项目：膜计算的一个公开问题-监督学习问题的研究（2015-1016），负责人；
(7) 教育部春晖计划：数值型膜系统学习模型研究（2013-1015），负责人；
(8) 四川省留学回国人员科技活动项目：由膜计算所启发的新颖学习模型（2015），负责人。

(1)研究生课程:

   机器学习; 算法设计与分析。

(2)本科课程:

   数据库原理; 数据结构与算法; 软件工程; C语言程序设计; .NET程序设计; SQL Server数据库; Linux操作系统等。

1. 2017年度四川省科技进步奖（自然科学类）二等奖，“膜计算模型与算法”。

2. 四川省第六届高等教育教学成果奖二等奖，“地方院校计算机科学与技术专业应用型人才培养模式构造与实践”。
3. 2012年度ICICIC最佳论文奖。

2013-至今，ACMC(亚洲膜计算会议)程序委员；

2013-至今，ICICIC(创新计算、信息与控制国际会议)程序委员。