[Alimi, A.M., Aouiti, C., Chérif, F., Dridi, F. and M’hamdi, M.S. (2018). Dynamics and oscillations of generalized high-order Hopfield neural networks with mixed delays, Neurocomputing321: 274–295.10.1016/j.neucom.2018.01.061]Search in Google Scholar
[Amster, P. (2013). Topological Methods in the Study of Boundary Valued Problems, Springer, New York, NY.10.1007/978-1-4614-8893-4]Search in Google Scholar
[Aouiti, C. (2018). Oscillation of impulsive neutral delay generalized high-order Hopfield neural networks, Neural Computing and Applications29(9): 477–495.10.1007/s00521-016-2558-3]Search in Google Scholar
[Aouiti, C., Coirault, P., Miaadi, F. and Moulay, E. (2017). Finite time boundedness of neutral high-order Hopfield neural networks with time delay in the leakage term and mixed time delays, Neurocomputing260: 378–392.10.1016/j.neucom.2017.04.048]Search in Google Scholar
[Bayro-Corrochano, E. and Scheuermann, G. (2010). Geometric Algebra Computing, in Engineering and Computer Science, Springer, London.10.1007/978-1-84996-108-0]Search in Google Scholar
[Brackx, F., Delanghe, R. and Sommen, F. (1982). Clifford Analysis, Pitman Books Limited, London.]Search in Google Scholar
[Buchholz, S. (2005). A theory of Neural Computation with Clifford Algebras, PhD thesis, University of Kiel, Kiel.]Search in Google Scholar
[Buchholz, S., Tachibana, K. and Hitzer, E.M. (2007). Optimal learning rates for Clifford neurons, in J.M. de Sá et al. (Eds), Artificial Neural Networks, Springer, Berlin/Heidelberg, pp. 864–873.10.1007/978-3-540-74690-4_88]Search in Google Scholar
[Buchholz, S. and Sommer, G. (2008). On Clifford neurons and Clifford multilayer perceptrons, Neural Networks21(7): 925–935.10.1016/j.neunet.2008.03.00418514482]Search in Google Scholar
[Corrochano, E.B., Buchholz, S. and Sommer, G. (1996). Selforganizing Clifford neural network, IEEE International Conference on Neural Networks, Washington, DC, USA, Vol. 1, pp. 120–125.]Search in Google Scholar
[Şaylı, M. and Yılmaz, E. (2017). Anti-periodic solutions for state-dependent impulsive recurrent neural networks with time-varying and continuously distributed delays, Annals of Operations Research258(1): 159–185.10.1007/s10479-016-2192-6]Search in Google Scholar
[Dorst, L., Fontijne, D. and Mann, S. (2007). Geometric Algebra for Computer Science: An Object-oriented Approach to Geometry, Morgan Kaufmann, Burlington, VA, pp. 609–612.]Search in Google Scholar
[He, Y., Guoping, L. and David, R. (2007). New delay-dependent stability criteria for neural networks with time-varying delay, IEEE Transactions on Neural Networks18(1): 310–314.10.1109/TNN.2006.88837317278483]Search in Google Scholar
[Hitzer, E., Nitta, T. and Kuroe, Y. (2013). Applications of Clifford’s geometric algebra, Advances in Applied Clifford Algebras23(2): 377–404.10.1007/s00006-013-0378-4]Search in Google Scholar
[Hu, J. and Wang, J. (2012). Global stability of complex-valued recurrent neural networks with time-delays, IEEE Transactions on Neural Networks and Learning Systems23(6): 853–865.10.1109/TNNLS.2012.219502824806758]Search in Google Scholar
[Kan, Y., Lu, J., Qiu, J. and Kurths, J. (2019). Exponential synchronization of time-varying delayed complex-valued neural networks under hybrid impulsive controllers, Neural Networks114: 157–163.10.1016/j.neunet.2019.02.00630974391]Search in Google Scholar
[Ke, Y. and Miao, C. (2017). Anti-periodic solutions of inertial neural networks with time delays, Neural Processing Letters45(2): 523–538.10.1007/s11063-016-9540-z]Search in Google Scholar
[Kuroe, Y. (2011). Models of Clifford recurrent neural networks and their dynamics, International Joint Conference on Neural Networks, San Jose, CA, USA, Vol. 3, pp. 1035–1041.]Search in Google Scholar
[Li, Y., Meng, X. and Xiong, L. (2017). Pseudo almost periodic solutions for neutral type high-order Hopfield neural networks with mixed time-varying delays and leakage delays on time scales, International Journal of Machine Learning and Cybernetics8(6): 1915–1927.10.1007/s13042-016-0570-7]Search in Google Scholar
[Li, Y. and Qin, J. (2018). Existence and global exponential stability of periodic solutions for quaternion-valued cellular neural networks with time-varying delays, Neuro-computing292: 91–103.10.1016/j.neucom.2018.02.077]Search in Google Scholar
[Li, Y., Qin, J. and Li, B. (2019a). Anti-periodic solutions for quaternion-valued high-order Hopfield neural networks with time-varying delays, Neural Processing Letters49(3): 1217–1237.10.1007/s11063-018-9867-8]Search in Google Scholar
[Li, Y., Qin, J. and Li, B. (2019b). Existence and global exponential stability of anti-periodic solutions for delayed quaternion-valued cellular neural networks with impulsive effects, Mathematical Methods in the Applied Sciences42(1): 5–23.10.1002/mma.5318]Search in Google Scholar
[Li, Y. and Wang, C. (2013). Existence and global exponential stability of equilibrium for discrete-time fuzzy BAM neural networks with variable delays and impulses, Fuzzy Sets and Systems217: 62–79.10.1016/j.fss.2012.11.009]Search in Google Scholar
[Li, Y., Wang, H. and Meng, X. (2019c). Almost automorphic synchronization of quaternion-valued high-order Hopfield neural networks with time-varying and distributed delays, IMA Journal of Mathematical Control and Information36(3): 983–1013.10.1093/imamci/dny015]Search in Google Scholar
[Li, Y. and Yang, L. (2014). Almost automorphic solution for neutral type high-order Hopfield neural networks with delays in leakage terms on time scales, Applied Mathematics and Computation242: 679–693.10.1016/j.amc.2014.06.052]Search in Google Scholar
[Liu, Y., Xu, P., Lu, J. and Liang, J. (2016). Global stability of Clifford-valued recurrent neural networks with time delays, Nonlinear Dynamics84(2): 767–777.10.1007/s11071-015-2526-y]Search in Google Scholar
[Liu, Y., Zhang, D., Lou, J., Lu, J. and Cao, J. (2018). Stability analysis of quaternion-valued neural networks: Decomposition and direct approaches, IEEE Transactions on Neural Networks and Learning Systems29(9): 4201–4211.10.1109/TNNLS.2017.275569729989971]Search in Google Scholar
[Liu, Y., Zheng, Y., Lu, J., Cao, J. and Rutkowski, L. (2020). Constrained quaternion-variable convex optimization: A quaternion-valued recurrent neural network approach, IEEE Transactions on Neural Networks and Learning Systems31(3): 1022–1035, DOI: 10.1109/TNNLS.2019.2916597.10.1109/TNNLS.2019.291659731247564]Search in Google Scholar
[Lou, X. and Cui, B. (2007). Novel global stability criteria for high-order Hopfield-type neural networks with time-varying delays, Journal of Mathematical Analysis and Applications330(1): 144–158.10.1016/j.jmaa.2006.07.058]Search in Google Scholar
[Ou, C. (2008). Anti-periodic solutions for high-order Hopfield neural networks, Computers & Mathematics with Applications56(7): 1838–1844.10.1016/j.camwa.2008.04.029]Search in Google Scholar
[Pearson, J. and Bisset, D. (1992). Back propagation in a Clifford algebra, in I. Aleksander and J. Taylor (Eds), Artificial Neural Networks, North-Holland, Amsterdam, pp. 413–416.]Search in Google Scholar
[Pearson, J. and Bisset, D. (2007). Neural networks in the Clifford domain, IEEE International Conference on Neural Networks, Orlando, FL, USA, Vol. 3, pp. 1465–1469.]Search in Google Scholar
[Rivera-Rovelo, J. and Bayro-Corrochano, E. (2006). Medical image segmentation using a self-organizing neural network and Clifford geometric algebra, International Joint Conference on Neural Networks, Vancouver, Canada, pp. 3538–3545.]Search in Google Scholar
[Sakthivel, R., Raja, R. and Anthoni, S. (2013). Exponential stability for delayed stochastic bidirectional associative memory neural networks with Markovian jumping and impulses, Journal of Optimization Theory and Applications150(1): 166–187.10.1007/s10957-011-9808-4]Search in Google Scholar
[Selvaraj, P., Sakthivel, R. and Kwon, O. (2018). Finite-time synchronization of stochastic coupled neural networks subject to Markovian switching and input saturation, Neural Networks105: 154–165.10.1016/j.neunet.2018.05.00429886328]Search in Google Scholar
[Shi, P. and Dong, L. (2010). Existence and exponential stability of anti-periodic solutions of Hopfield neural networks with impulses, Applied Mathematics and Computation216(2): 623–630.10.1016/j.amc.2010.01.095]Search in Google Scholar
[Wang, Z., Cao, J., Cai, Z. and Huang, L. (2019). Periodicity and finite-time periodic synchronization of discontinuous complex-valued neural networks, Neural Networks119: 249–260.10.1016/j.neunet.2019.08.02131472291]Search in Google Scholar
[Xiang, H., Yan, K. and Wang, B. (2006). Existence and global exponential stability of periodic solution for delayed high-order Hopfield-type neural networks, Physics Letters A352(4–5): 341–349.10.1016/j.physleta.2005.12.014]Search in Google Scholar
[Xu, B., Liu, X. and Liao, X. (2003). Global asymptotic stability of high-order Hopfield type neural networks with time delays, Computers and Mathematics with Applications45(10–11): 1729–1737.10.1016/S0898-1221(03)00151-2]Search in Google Scholar
[Xu, B., Liu, X. and Liao, X. (2006). Global exponential stability of high order Hopfield type neural networks, Applied Mathematics and Computation174(1): 98–116.10.1016/j.amc.2005.03.020]Search in Google Scholar
[Xu, C. and Li, P. (2017). Pseudo almost periodic solutions for high-order Hopfield neural networks with time-varying leakage delays, Neural Processing Letters46(1): 41–58.10.1007/s11063-016-9573-3]Search in Google Scholar
[Zhao, L., Li, Y. and Li, B. (2018). Weighted pseudo-almost automorphic solutions of high-order Hopfield neural networks with neutral distributed delays, Neural Computing and Applications29(7): 513–527.10.1007/s00521-016-2553-8]Search in Google Scholar