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An improved design of RBF neural network control algorithm based on spintronic memristor crossbar array

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Abstract

Spintronic memristors have received significant attention as a potential building block for control systems, and particularly, it can be laid out in a high-density grid known as a crossbar array. Meanwhile, radial basis function (RBF) neural network control algorithm can effectively improve the control performance against large uncertain systems. But choosing reasonable parameters for RBF neural network to save convergence time and reduce the amplitude of the initial step is very important and difficult. Therefore, based on the study of characteristics of RBF neural network and spintronic memristors, this paper proposes a RBF neural network control algorithm based on spintronic memristor crossbar array; that is to say, the RBF neural network is divided into two parts of hardware and software. Hardware completes the updated parameters’ storage and extraction by spintronic memristor crossbar array, and software accomplishes the parameters updating process from the traditional RBF neural network. With a multiple-input multiple-output data sample training as the research object, simulation results show that the typical RBF neural network control algorithm needs 24 steps to fulfill the convergence requirements and the amplitude of the initial step is close to 2e−4. However, the proposed algorithm only needs 18 steps and the amplitude of the initial step is just about 8.6e−9. The comparison of these results indicates evidently the effectiveness of proposed algorithm.

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References

  1. Chua L (1971) Memristor—the missing circuit element. IEEE Trans Circuit Theory 18(5):507–519

    Article  Google Scholar 

  2. Chen Y, Wang X (2009) Compact modeling and corner analysis of spintronic memristor, 2013 IEEE/ACM international symposium on nanoscale architectures (NANOARCH), pp 7–12

  3. Chen Y, Li H, Sun Z (2014) Spintronic memristor as interface between DNA and solid state devices, memristors and memristive systems. Springer, New York, pp 281–298

    Google Scholar 

  4. Wang X, Chen Y, Xi H, Li H, Dimitrov D (2009) Spintronic memristor through spin-torque-induced magnetization motion. IEEE Electron Device Lett 30(3):294–297

    Article  Google Scholar 

  5. Li S, Zheng J (2010) Adaptive controller design for a class of nonlinear time-delay systems based on RBF neural networks. J Syst Sci Math Sci 5(2):577–592

    MathSciNet  MATH  Google Scholar 

  6. Wang Z, Zhang Y, Fang H (2008) Neural adaptive control for a class of nonlinear systems with unknown deadzone. Neural Comput Appl 17(4):339–345

    Article  Google Scholar 

  7. Zhao J, Wei H, Zhang C, Li W, Guo W, Zhang K (2015) Natural gradient learning algorithms for RBF networks. Neural Comput 27(2):481–505

    Article  Google Scholar 

  8. Fasshauer GE, Zhang JG (2007) On choosing “optimal” shape parameters for RBF approximation. Numer Algorithms 45(1–4):345–368

    Article  MathSciNet  MATH  Google Scholar 

  9. Chen W, Jiao LC, Wu J (2012) Globally stable adaptive robust tracking control using RBF neural networks as feedforward compensators. Neural Comput Appl 21(2):351–363

    Article  Google Scholar 

  10. Gao S, Dong H, Ning B, Chen Y, Sun X (2015) Adaptive fault-tolerant automatic train operation using RBF neural networks. Neural Comput Appl 26(1):141–149

    Article  Google Scholar 

  11. Golbabai A, Mohebianfar E, Rabiei H (2015) On the new variable shape parameter strategies for radial basis functions. Comput Appl Math 34(2):691–704

    Article  MathSciNet  MATH  Google Scholar 

  12. Liu J (2013) Radial basis function (RBF) neural network control for mechanical systems. Springer, Berlin, pp 133–191

    Book  Google Scholar 

  13. Prado RNA, Oliveira JAN, Melo JD, Neto AD (2014) Takagi Hayashi fuzzy neural system in hardware: an application for embedding in industrial sensors, making them intelligent. J Control Autom Electr Syst 25(5):585–596

    Article  Google Scholar 

  14. Li MM, Verma B, Fan X, Tickle K (2008) RBF neural networks for solving the inverse problem of backscattering spectra. Neural Comput Appl 17(4):391–397

    Article  Google Scholar 

  15. Zhang S, Qiu X, Liu C (2014) Neural adaptive compensation control for a class of MIMO uncertain nonlinear systems with actuator failures. Circuits Syst Signal Process 33(6):1971–1984

    Article  MathSciNet  Google Scholar 

  16. Wang M, Liu X, Shi P (2011) Adaptive neural control of pure-feedback nonlinear time-delay systems via dynamic surface technique, systems. IEEE Trans Syst Man Cybern B Cybern 41(6):1681–1692

    Article  Google Scholar 

  17. Wang H, Liu X, Liu K, Chen B, Lin C (2014) Adaptive neural control for a general class of pure-feedback stochastic nonlinear systems. Neurocomputing 135(8):348–356

    Google Scholar 

  18. Huang J, Li C, He X (2013) Stabilization of a memristor-based chaotic system by intermittent control and fuzzy processing. Int J Control Autom Syst 11(3):643–647

    Article  Google Scholar 

  19. Kim H, Sah MP, Yang C, Roska T, Chua LO (2012) Neural synaptic weighting with a pulse-based memristor circuit. IEEE Trans Circuits Syst I Regul Pap 59(1):148–158

    Article  MathSciNet  Google Scholar 

  20. Bi X, Zhang C, Li H, Chen Y, Pino RE (2012) Spintronic memristor based temperature sensor design with CMOS current reference. IEEE design, automation and test in Europe conference and exhibition (DATE), pp 1301–1306

  21. Fang Y, Dumas RK, Nguyen TN, Mohseni SM, Chung S, Miller CW, Åkerman J (2013) A nonvolatile spintronic memory element with a continuum of resistance states. Adv Funct Mater 23(15):1919–1922

    Article  Google Scholar 

  22. Qureshi MS, Pickett M, Miao F, Strachan JP (2011) CMOS interface circuits for reading and writing memristor crossbar array, circuits and systems (ISCAS), IEEE international symposium, pp 2954–2957

  23. Wu A, Zeng Z (2015) Improved conditions for global exponential stability of a general class of memristive neural networks. Commun Nonlinear Sci 20(3):975–985

    Article  MathSciNet  MATH  Google Scholar 

  24. Duan S, Hu W, Li C, Wu S (2012) Exponential stability of discrete-time delayed Hopfield neural networks with stochastic perturbations and impulses. Results Math 62(1-2):73–87

    Article  MathSciNet  MATH  Google Scholar 

  25. Hu X, Duan S, Wang L, Liao X (2012) Memristive crossbar array with applications in image processing. Sci China Inform Sci 55(2):461–472

    Article  Google Scholar 

  26. Hu M, Li HH, Chen Y, Wang X (2011) spintronic memristor: compact model and statistical analysis. J Low Power Electron 7(2):234–244

    Article  Google Scholar 

  27. Chen L, Li C, Huang T, Chen Y, Wang X (2013) Memristor crossbar-based unsupervised image learning. Neural Comput Appl 25(2):393–400

    Article  Google Scholar 

  28. Chen Y, Huang T, Huang Y (2014) Complex dynamics of a delayed discrete neural network of two nonidentical neurons, chaos: an interdisciplinary. J Nonlinear Sci 24(1):013108

    MATH  Google Scholar 

  29. Wen S, Bao G, Zeng Z, Chen Y, Huang T (2013) Global exponential synchronization of memristor-based recurrent neural networks with time-varying delays. Neural Netw 48:195–203

    Article  MATH  Google Scholar 

  30. Mirebrahimi SN, Merrikh-Bayat F (2014) Programmable discrete-time type I and type II FIR filter design on the memristor crossbar structure. Analog Integr Circuits Signal Process 79(3):529–541

    Article  Google Scholar 

  31. Duan S, Hu X, Wang L, Gao S, Li C (2014) Hybrid memristor/RTD structure-based cellular neural networks with applications in image processing. Neural Comput Appl 25(2):291–296

    Article  Google Scholar 

  32. Querlioz D, Bichler O, Dollfus P, Gamrat C (2013) Immunity to device variations in a spiking neural network with memristive nanodevices. IEEE Trans Nanotechnol 12(3):288–295

    Article  Google Scholar 

  33. Duan S, Zhang Y, Hu X, Wang L, Li C (2014) Memristor-based chaotic neural networks for associative memory. Neural Comput Appl 25(6):1437–1445

    Article  Google Scholar 

  34. Huang T, Li C, Duan S, Starzyk JA (2012) Robust exponential stability of uncertain delayed neural networks with stochastic perturbation and impulse effects. IEEE Trans Neural Netw Learn Syst 23(6):866–875

    Article  Google Scholar 

  35. Gao S, Dong H, Ning B, Chen Y, Sun X (2015) Adaptive fault-tolerant automatic train operation using RBF neural networks. Neural Comput Appl 26(1):141–149

    Article  Google Scholar 

Download references

Acknowledgements

The work was supported by Program for New Century Excellent Talents in University (Grant No. [2013]47), National Natural Science Foundation of China (Grant Nos. 61372139, 61101233, 60972155), “Spring Sunshine Plan” Research Project of Ministry of Education of China (Grant No. z2011148), Technology Foundation for Selected Overseas Chinese Scholars, Ministry of Personnel in China (Grant No. 2012-186), University Excellent Talents Supporting Foundations in of Chongqing (Grant No. 2011-65), Research Project of Basic Science and Cutting-edge Technology of Chongqing (Grant No. cstc2016jcyjA0573), Research Project of Basic Science and Cutting-edge Technology of Chongqing (Grant No. cstc2016jcyjA0573), Fundamental Research Funds for the Central Universities (Grant Nos. XDJK2014A009, XDJK2013B011, XDJK2015C009, XDJK2016C027).

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Authors and Affiliations

  1. College of Electronics and Information Engineering, Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University, Chongqing, 400715, China

    Tianshu Li, Shukai Duan & Lidan Wang

  2. College of Engineering and Technology, Southwest University, Chongqing, 400715, China

    Jun Liu

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  1. Tianshu Li
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  2. Shukai Duan
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  3. Jun Liu
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  4. Lidan Wang
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Correspondence to Shukai Duan.

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Li, T., Duan, S., Liu, J. et al. An improved design of RBF neural network control algorithm based on spintronic memristor crossbar array. Neural Comput & Applic 30, 1939–1946 (2018). https://doi.org/10.1007/s00521-016-2715-8

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  • DOI: https://doi.org/10.1007/s00521-016-2715-8

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