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.学术专著：

(1) Hong Peng, Jun Wang. Advanced Spiking Neural P Systems: Models and Applications. Springer, Singapore, August 2024.

2. 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180余篇，其中SCI/EI收录130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

代表性的学术论文：

(1) 脉冲神经P系统模型

[1] 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.

[2] H. Peng, J. Wang, M.J. Pérez-Jiménez, A. Riscos-Núñez. Dynamic threshold neural P systems, Knowledge-Based Systems 163, 2019, 875–884.

[3] H. Peng, J. Wang. Coupled neural P systems. IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(6), 1672-1682.

[4] H. Peng, Z. Lv, B. Li, X. Luo, J. Wang, X. Song, T. Wang,M.J. Pérez-Jiménez, A. Riscos-Núñez. Nonlinear Spiking Neural P Systems, International Journal of Neural Systems, 30(10), 2020, 2050008.

[5] 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.

[6] 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.

[7] 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.

[8] X. Song, L. Valencia-Cabrera, H. Peng, J. Wang.Spiking neural P systems with autapses, Information Sciences, 570, 383-402.

[9] Z. Lv, T. Bao, N. Zhou, H. Peng, X. Huang, A. Riscos-Nunez, M.J. Perez-Jimenez. Spiking neural P systems with extended channel rules, International Journal of Neural Systems, 31(1), 2050049, 2021.

(2) 模糊脉冲神经P系统模型

[1] 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, 2013: 106-116.

[2] J. Wang, P. Shi, H. Peng, M.J. Pérez-Jiménez, T. Wang. Weighted Fuzzy Spiking Neural P Systems. IEEE Transactions on Fuzzy Systems, vol. 21, no. 2, 2013: 209-220.

[3] M. Tu, J. Wang, H. Peng, P. Shi. Application of adaptive fuzzy spiking neural P systems in fault diagnosis model of power systems. Chinese Journal of Electronics, 23(1), 2014, 87-92.

[4] J. Wang, H. Peng, M. Tu, M.J. Pérez-Jiménez, P. Shi. A fault diagnosis method of power systems based on an improved adaptive fuzzy spiking neural P systems and PSO algorithms. Chinese Journal of Electronics, 25(CJE-2), 2016: 320-327.

[5] 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.

[6] H. Peng, J. Wang, J. Ming, P. Shi, M.J. Pérez-Jiménez, W. Yu, C. Tao. Fault diagnosis of power systems using intuitionistic fuzzy spiking neural P systems. IEEE Transaction on Smart Grid, 9(5), 2018, 4777-4784.

[7] 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, 2019, 102–109.

[8] T. Wang, X. Wei, J. Wang, T. Huang, H. Peng, X. Song, L. Valencia Cabrera, M.J. Pérez-Jiménez, A weighted corrective fuzzy reasoning spiking neural P system for fault diagnosis in power systems with variable topologies, Engineering Applications of Artificial Intelligence 92, 2020, 103680.

(3) 膜聚类模型

[1] H. Peng, J. Wang, M.J. Pérez-Jiménez, A. Riscos-Núñez, An unsupervised learning algorithm for membrane computing, Information Sciences, 304, 2015, 80-91.

[2] 彭宏,蒋洋,王军, M.J. Pérez-Jiménez, 一种带混合进化机制的膜聚类算法.软件学报, 26(5) ,2015, 1001−1012.

[3] H. Peng, J. Wang, P. Shi, A. Riscos-Núñez, M.J. Pérez-Jiménez, An automatic clustering algorithm inspired by membrane computing, Pattern Recognition Letters, 68 (2015) 34–40.

[4] H. Peng, J. Wang, P. Shi, M.J. Pérez-Jiménez, A. Riscos-Núñez, An extended membrane system with active membrane to solve automatic fuzzy clustering problems, International Journal of Neural Systems, 26(3), 2016, 1650004.

[5] 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, 2017, 74-82.

(4) 脉冲神经P系统的深度学习模型

[1] S. Zhao, L. Zhang, H. Peng, Z. Liu, J. Wang. ConvSNP: a deep learning model embedded with SNP-like neurons. Journal of Membrane Computing, 4, 87–95, 2022.

[2] 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.

[3] 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, 34(9), 2023, 6227-6236.

[4] 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, 54(3), 2024, 1841-1853.

[5] 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.

(5) 时间序列分析

[1] 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.

[2] 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.

[3] 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, 34(9), 2023, 6227-6236.

[4] 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, 54(3), 2024, 1841-1853.

[5] L. Long, C. Guo, X. Xiong, H. Peng, J. Wang. A reservoir computing model based on nonlinear spiking neural P systems for time series forecasting. Applied Soft Computing, 159, 2024, 111644.

[6] H. Peng, X. Xiong, M. Wu, J. Wang, Q. Yang, D. Orellana-Martín, M.J. Pérez-Jiménez, Reservoir computing models based on spiking neural P systems for time series classification. Neural Networks, 169, 2024, 274-281.

[7] X. Xiong, M. Wu, J. He, H. Peng, J. Wang, X. Long, Q. Yang. Time series classification models based on nonlinear spiking neural P systems. Engineering Applications of Artificial Intelligence, 129, 2024, 107603.

[8] 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.

[9] Y. Gao, J. Wang, L. Guo, H. Peng. Short-term photovoltaic power prediction using Nonlinearspiking neural P systems. Sustainability, 16, 2024, 1709.

[10] L. Li, L. Guo, J. Wang, H. Peng. Short-term load forecasting based on spiking neural P systems. Applied Sciences, 13(2), 2023, 792.

(6) 自然语言处理(NLP)

[1] 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.

[2] 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.

[3] Y. Huang, X. Bai, Q. Liu, H. Peng, Q. Yang, J. Wang. Sentence-level sentiment classification based on multi-attention bidirectional gated spiking neural P systems. Applied Soft Computing, 152, 2024, 111231.

[4] 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, 33(8), 2023, 2350037.

[5] X. Bai, Y. Huang, H. Peng, J. Wang, Q. Yang, D. Orellana-Martín, Antonio Ramírez-de-Arellano, Mario J. Pérez-Jiménez, Sequence recommendation using multi-level self-attention network with gated spiking neural P systems, Information Sciences, 656，2024, 119916.

[6] X. Bai, L. Zhang, M. Jiang, H. Peng, J. Wang, Q. Yang, A. Ramírez-de-Arellano. Gated graph spiking neural P network for session-based recommendation. Knownledge-Based Systems, 300, 2024, 112162.

(7)图像与计算机视觉（含医学图像处理）

[1] R. Xian, X. Xiong, H. Peng, J. Wang, A. Ramírez de Arellano Marrero, Q. Yang, Feature fusion method based on spiking neural convolutional network for edge detection, Pattern Recognition, 147, 2024, 110112.

[2] H. Zheng, M. Xia, H. Peng, Z. Liu, J. Guo. NSNP-DFER: a nonlinear spiking neural P network for dynamic facial expression recognition. Computers and Electrical Engineering, 115, 2024, 109125.

[3] C. Zhou, L. Ye, H. Peng, Z. Liu, J. Wang, A. Ramírez-De-Arellano. A parallel convolutional network based on spiking neural systems. International Journal of Neural Systems, 34(5), 2024, 2450022.

[4] Z. Jiang, S. Sun, H. Peng, Z. Liu, J. Wang. Multiple-in-single-out object detector leveraging spiking neural membrane systems and multiple Transformers. International Journal of Neural Systems, 34(7), 2024, 2450035.

[5] J. Fu, H. Peng, B. Li, Z. Liu, R. Lugu, J. Wang, A. Ramirez-de-Arellano. Multitask adversarial networks based on extensive nonlinear spiking neuron models. International Journal of Neural Systems, 34(6), 2024, 24500321.

[6] L. Ye, C. Zhou, H. Peng, J. Wang, Z. Liu, Q. Yang. Multi-level feature interaction image super-resolution network based on convolutional nonlinear spiking neural model. Neural Networks, 2024, 106366.

[7] 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, 2023.

[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.

[9] 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.

[10] 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.

[11] 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, 107793: 1-13, 2021.

[12] B. Li, H. Peng, X. Luo, J. Wang, X. Song, M.J. Perez-Jimenez, A. Riscos-Nunez. Medical image fusion method based on coupled neural P systems in nonsubsampled shearlet transform domain, International Journal of Neural Systems, 31(1), 2050050: 1-17, 2021.

[13] H. Peng, B. Li, Q. Yang, J. Wang, Multi-focus image fusion approach based on CNP systems in NSCT domain, Computer Vision and Image Understanding, 210, 103228, 2021.

[14] 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.

[15] L. Ye, C. Zhou, H. Peng, J. Wang, Z. Liu, A. Ramirez-de-Arellano. Multi-directional feature fusion super-resolution network based on nonlinear spiking neural P systems. Signal Processing, 2024, 109595.

3. 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。