Hammerstein-Wiener Model: A New Approach to the Estimation of Formal Neural Information

被引：0
|
作者：
Abbasi-Asl, Reza [1 ]
Khorsandi, Rahman [2 ]
Vosooghi-Vahdat, Bijan [1 ]
机构：
[1] Sharif Univ Technol, Sch Elect Engn, Biomed Signal & Image Proc Lab BiSIPL, POB 11356-9363, Tehran, Iran
[2] Univ Miami, Dept Elect & Comp Engn, Coral Gables, FL 33124 USA
来源：
BASIC AND CLINICAL NEUROSCIENCE | 2012年 / 3卷 / 04期
关键词：
Formal Information Estimation; Neurons; Hammerstein-Wiener Model;
D O I：
暂无
中图分类号：
Q189 [神经科学];
学科分类号：
071006 ;
摘要：
A new approach is introduced to estimate the formal information of neurons. Formal Information, mainly discusses about the aspects of the response that is related to the stimulus. Estimation is based on introducing a mathematical nonlinear model with Hammerstein-Wiener system estimator. This method of system identification consists of three blocks to completely describe the nonlinearity of input and output and linear behaviour of the model. The introduced model is trained by 166 spikes of neurons and other 166 spikes are used to test and validate the model. The simulation results show the R-Value of 92.6 % between estimated and reference information rate. This shows improvement of 1.41 % in comparison with MLP neural network.
引用
收藏
页码：45 / 51
页数：7
相关论文
共 50 条
  • [21] Structured Hammerstein-Wiener Model Learning for Model Predictive Control
    Moriyasu, Ryuta
    Ikeda, Taro
    Kawaguchi, Sho
    Kashima, Kenji
    IEEE CONTROL SYSTEMS LETTERS, 2022, 6 : 397 - 402
  • [22] Hierarchical Least Squares Estimation Algorithm for Hammerstein-Wiener Systems
    Wang, Dong-Qing
    Ding, Feng
    IEEE SIGNAL PROCESSING LETTERS, 2012, 19 (12) : 825 - 828
  • [23] Design of parametric estimation algorithm for Hammerstein-Wiener mathematical models
    Abouda, Saif Eddine
    Elloumi, Mourad
    Koubaa, Yassine
    Chaari, Abdessattar
    2019 19TH INTERNATIONAL CONFERENCE ON SCIENCES AND TECHNIQUES OF AUTOMATIC CONTROL AND COMPUTER ENGINEERING (STA), 2019, : 371 - 375
  • [24] Nonlinear Model Predictive Control Based on Hammerstein-Wiener Model
    Hong, Man
    Cheng, Shao
    2011 INTERNATIONAL CONFERENCE ON COMPUTERS, COMMUNICATIONS, CONTROL AND AUTOMATION (CCCA 2011), VOL II, 2010, : 309 - 312
  • [25] Hammerstein-Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals
    Chihi, Ines
    Sidhom, Lilia
    Kamavuako, Ernest Nlandu
    BIOSENSORS-BASEL, 2022, 12 (02):
  • [26] Modeling and parameter learning method for the Hammerstein-Wiener model with disturbance
    Li, Feng
    Chen, Lianyu
    Wo, Songlin
    Li, Shengquan
    Cao, Qingfeng
    MEASUREMENT & CONTROL, 2020, 53 (5-6): : 971 - 982
  • [27] Wideband Wireless Transmitter Identification Based on Hammerstein-Wiener Model
    Sun, Minhong
    Xu, Tiancheng
    Guo, Hongchen
    Zhong, Hua
    JOURNAL OF APPLIED SCIENCE AND ENGINEERING, 2018, 21 (02): : 261 - 269
  • [28] Model-based predictive control for Hammerstein-Wiener systems
    Bloemen, HHJ
    van den Boom, TJJ
    Verbruggen, HB
    INTERNATIONAL JOURNAL OF CONTROL, 2001, 74 (05) : 482 - 495
  • [29] Disturbance-Encoding-Based Neural Hammerstein-Wiener Model for Industrial Process Predictive Control
    Zhang, Jin
    Tang, Zhaohui
    Xie, Yongfang
    Li, Fanbiao
    Ai, Mingxi
    Zhang, Guoyong
    Gui, Weihua
    IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2022, 52 (01): : 606 - 617
  • [30] Identification of a precision motion stage based on the Hammerstein-Wiener model
    Zhang, Zhu
    Zhang, Delong
    Zheng, Haiyang
    Huang, Tao
    Xie, Yangqiu
    PROCEEDINGS OF THE 38TH CHINESE CONTROL CONFERENCE (CCC), 2019, : 1637 - 1642
12345