Savo G. Glisic

Artificial Intelligence and Quantum Computing for Advanced Wireless Networks


Скачать книгу

Spaces and Fixed Point Theory. New York: Wiley.

      63 63 Powell, M.J.D. (1964). An efficient method for finding the minimum of a function of several variables without calculating derivatives. Comput. J. 7: 155–162.

      64 64 Frasconi, P., Gori, M., and Sperduti, A. (1998). A general framework for adaptive processing of data structures. IEEE Trans. Neural Netw. 9 (5): 768–786.

      65 65 L. Almeida, “A learning rule for asynchronous perceptrons with feedback in a combinatorial environment,” in Proc. IEEE Int. Conf. Neural Netw., M. Caudill and C. Butler, Eds., San Diego, 1987, vol. 2, pp. 609–618.

      66 66 Pineda, F. (1987). Generalization of back‐propagation to recurrent neural networks. Phys. Rev. Lett. 59: 2229–2232.

      67 67 Graham, A. (1982). Kronecker Products and Matrix Calculus: With Applications. New York: Wiley.

      68 68 R. Singh, A. Chakraborty and B. S. Manoj, Graph Fourier Transform based on Directed Laplacian, https://arxiv.org/pdf/1601.03204.pdf

      Конец ознакомительного фрагмента.

      Текст предоставлен ООО «ЛитРес».

      Прочитайте эту книгу целиком, купив полную легальную версию на ЛитРес.

      Безопасно оплатить книгу можно банковской картой Visa, MasterCard, Maestro, со счета мобильного телефона, с платежного терминала, в салоне МТС или Связной, через PayPal, WebMoney, Яндекс.Деньги, QIWI Кошелек, бонусными картами или другим удобным Вам способом.

/9j/4AAQSkZJRgABAQEBLAEsAAD/7SMkUGhvdG9zaG9wIDMuMAA4QklNBAQAAAAAAAccAgAAAgAA ADhCSU0EJQAAAAAAEOjxXPMvwRihontnrcVk1bo4QklNBDoAAAAAAOUAAAAQAAAAAQAAAAAAC3By aW50T3V0cHV0AAAABQAAAABQc3RTYm9vbAEAAAAASW50ZWVudW0AAAAASW50ZQAAAABDbHJtAAAA D3ByaW50U2l4dGVlbkJpdGJvb2wAAAAAC3ByaW50ZXJOYW1lVEVYVAAAAAEAAAAAAA9wcmludFBy b29mU2V0dXBPYmpjAAAADABQAHIAbwBvAGYAIABTAGUAdAB1AHAAAAAAAApwcm9vZlNldHVwAAAA AQAAAABCbHRuZW51bQAAAAxidWlsdGluUHJvb2YAAAAJcHJvb2ZDTVlLADhCSU0EOwAAAAACLQAA ABAAAAABAAAAAAAScHJpbnRPdXRwdXRPcHRpb25zAAAAFwAAAABDcHRuYm9vbAAAAAAAQ2xicmJv b2wAAAAAAFJnc01ib29sAAAAAABDcm5DYm9vbAAAAAAAQ250Q2Jvb2wAAAAAAExibHNib29sAAAA AABOZ3R2Ym9vbAAAAAAARW1sRGJvb2wAAAAAAEludHJib29sAAAAAABCY2tnT2JqYwAAAAEAAAAA AABSR0JDAAAAAwAAAABSZCAgZG91YkBv4AAAAAAAAAAAAEdybiBkb3ViQG/gAAAAAAAAAAAAQmwg IGRvdWJAb+AAAAAAAAAAAABCcmRUVW50RiNSbHQAAAAAAAAAAAAAAABCbGQgVW50RiNSbHQAAAAA AAAAAAAAAABSc2x0VW50RiNQeGxAcsAAAAAAAAAAAAp2ZWN0b3JEYXRhYm9vbAEAAAAAUGdQc2Vu dW0AAAAAUGdQcwAAAABQZ1BDAAAAAExlZnRVbnRGI1JsdAAAAAAAAAAAAAAAAFRvcCBVbnRGI1Js dAAAAAAAAAAAAAAAAFNjbCBVbnRGI1ByY0BZAAAAAAAAAAAAEGNyb3BXaGVuUHJpbnRpbmdib29s AAAAAA5jcm9wUmVjdEJvdHRvbWxvbmcAAAAAAAAADGNyb3BSZWN0TGVmdGxvbmcAAAAAAAAADWNy b3BSZWN0UmlnaHRsb25nAAAAAAAAAAtjcm9wUmVjdFRvcGxvbmcAAAAAADhCSU0D7QAAAAAAEAEs AAAAAQACASwAAAABAAI4QklNBCYAAAAAAA4AAAAAAAAAAAAAP4AAADhCSU0EDQAAAAAABAAAAFo4 QklNBBkAAAAAAAQAAAAeOEJJTQPzAAAAAAAJAAAAAAAAAAABADhCSU0nEAAAAAAACgABAAAAAAAA AAI4QklNA/UAAAAAAEgAL2ZmAAEAbGZmAAYAAAAAAAEAL2ZmAAEAoZmaAAYAAAAAAAEAMgAAAAEA WgAAAAYAAAAAAAEANQAAAAEALQAAAAYAAAAAAAE4QklNA/gAAAAAAHAAAP////////////////// //////////8D6AAAAAD/////////////////////////////A+gAAAAA//////////////////// /////////wPoAAAAAP////////////////////////////8D6AAAOEJJTQQAAAAAAAACAAE4QklN BAIAAAAAAAQAAAAAOEJJTQQwAAAAAAACAQE4QklNBC0AAAAAAAYAAQAAAAM4QklNBAgAAAAAABAA AAABAAACQAAAAkAAAAAAOEJJTQQeAAAAAAAEAAAAADhCSU0EGgAAAAADYwAAAAYAAAAAAAAAAAAA DAMAAAh/AAAAFwA5ADcAOAAxADEAMQA5ADcAOQAwADIAOQA3AF8AYgBhAGMAawBjAG8AdgBlAHIA AAABAAAAAAAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAACH8AAAwDAAAAAAAAAAAAAAAAAAAAAAEA AAAAAAAAAAAAAAAAAAAAAAAAEAAAAAEAAAAAAABudWxsAAAAAgAAAAZib3VuZHNPYmpjAAAAAQAA AAAAAFJjdDEAAAAEAAAAAFRvcCBsb25nAAAAAAAAAABMZWZ0bG9uZwAAAAAAAAAAQnRvbWxvbmcA AAwDAAAAAFJnaHRsb25nAAAIfwAAAAZzbGljZXNWbExzAAAAAU9iamMAAAABAAAAAAAFc2xpY2UA AAASAAAAB3NsaWNlSURsb25nAAAAAAAAAAdncm91cElEbG9uZwAAAAAAAAAGb3JpZ2luZW51bQAA AAxFU2xpY2VPcmlnaW4AAAANYXV0b0dlbmVyYXRlZAAAAABUeXBlZW51bQAAAApFU2xpY2VUeXBl AAAAAEltZyAAAAAGYm91bmRzT2JqYwAAAAEAAAAAAABSY3QxAAAABAAAAABUb3AgbG9uZwAAAAAA AAAATGVmdGxvbmcAAAAAAAAAAEJ0b21sb25nAAAMAwAAAABSZ2h0bG9uZwAACH8AAAADdXJsVEVY VAAAAAEAAAAAAABudWxsVEVYVAAAAAEAAAAAAABNc2dlVEVYVAAAAAEAAAAAAAZhbHRUYWdURVhU AAAAAQAAAAAADmNlbGxUZXh0SXNIVE1MYm9vbAEAAAAIY2VsbFRleHRURVhUAAAAAQAAAAAACWhv cnpBbGlnbmVudW0AAAAPRVNsaWNlSG9yekFsaWduAAAAB2RlZmF1bHQAAAAJdmVydEFsaWduZW51 bQAAAA9FU2xpY2VWZXJ0QWxpZ24AAAAHZGVmYXVsdAAAAAtiZ0NvbG9yVHlwZWVudW0AAAARRVNs aWNlQkdDb2xvclR5cGUAAAAATm9uZQAAAAl0b3BPdXRzZXRsb25nAAAAAAAAAApsZWZ0T3V0c2V0 bG9uZwAAAAAAAAAMYm90dG9tT3V0c2V0bG9uZwAAAAAAAAALcmlnaHRPdXRzZXRsb25nAAAAAAA4 QklNBCgAAAAAAAwAAAACP/AAAAAAAAA4QklNBBQAAAAAAAQAAAADOEJJTQQMAAAAABnfAAAAAQAA AHEAAACgAAABVAAA1IAAABnDABgAAf/Y/+0ADEFkb2JlX0NNAAH/7gAOQWRvYmUAZIAAAAAB/9sA hAAMCAgICQgMCQkMEQsKCxEVDwwMDxUYExMVExMYEQwMDAwMDBEMDAwMDAwMDAwMDAwMDAwMDAwM DAwMDAwMDAwMAQ0LCw0ODRAODhAUDg4OFBQODg4OFBEMDAwMDBERDAwMDAwMEQwMDAwMDAwMDAwM DAwMDAwMDAwMDAwMDAwMDAz/wAARCACgAHEDASIAAhEBAxEB/90ABAAI/8QBPwAAAQUBAQEBAQEA AAAAAAAAAwABAgQFBgcICQoLAQABBQEBAQEBAQAAAAAAAAABAAIDBAUGBwgJCgsQAAEEAQMCBAIF BwYIBQMMMwEAAhEDBCESMQVBUWETInGBMgYUkaGxQiMkFVLBYjM0coLRQwclklPw4fFjczUWorKD JkSTVGRFwqN0NhfSVeJl8rOEw9N14/NGJ5SkhbSVxNTk9KW1xdXl9VZmdoaWprbG1ub2N0dXZ3eH l6e3x9fn9xEAAgIBAgQEAwQFBgcHBgU1AQACEQMhMRIEQVFhcSITBTKBkRShsUIjwVLR8DMkYuFy gpJDUxVjczTxJQYWorKDByY1wtJEk1SjF2RFVTZ0ZeLys4TD03Xj80aUpIW0lcTU5PSltcXV5fVW ZnaGlqa2xtbm9ic3R1dnd4eXp7fH/9oADAMBAAIRAxEAPwDzgkydTylJ8SkeT8UlYWKk+JSk+JSS SUqT4lKT4lJJJSpPiUpPiUkklKk+JSk+JSSSUqT4lKT4lJJJSpPiUpPiUkxSUmSSSUK9/9Dzc8n4 pJHk/EplOsXSTJ4KSl9O51Wpg/Vzq2ZgjqmPSy7EbYWEF8Oc5j6a/R9OHO/TPyK6mLKM89jwpNuu YIZY9g10a5zR7o36NP5+33JFT3d3TQXWOr+reI2yq57LA25jnse621paMf7J6duxuLk14lLKb6v5 m70cmr+cq4vSzlluPT0LHssbe99b2X1+nY05GZjtpD/s7n3UV/Z7v8J/N4mP9n/n/Qs45ttjSCLH tcIhwc4ERxw5SddkuBa62wtMlwL3QZO8u+l7tz/f/XTeEpt63rHQ8vKxXjG6RjdP2Wh1t7LqnATU c1tdzqqGvqb9md7H0WM6f/3I9S6qv0+X6h0/J6blOxMsBtzQ1xDTuEOG4fuoX2i8GW3WAhuzdvdO 0/mfS+h/IUHPe9xc9xe4xLnEk6DaPc7+SiBSrVp5/FMkAXGBqT2CeCTpzMQihZIpf70ySk6SSSiX v//R83PJ+JTJHk/Ep2se9za6xuseQ1jR3c47Wj/OUyxZSL5nzn+P/kl0Wb9VMbCbnPtyLjVhhrmP 21jeza11ns3Oe5z8jdis9H1PQ/R25P6NQyPq3gUtzLfXv9PDtsoh4qYXPrdRXIcdzPd9o9lX84nc EuzGM0DVHfwcF1jntaw8N4UF0GT9Vq6jdTVZkWZFLXvB9Iek87rqsen1QfZfux/0rP8AjvS/o36Q HXugN6VudXZbdWLxUHuYACw1VXNtlhc3c6222n/raRhIC62UMsCQAdS44Hc8Jbux+j2Hgums+qOL Vk1VvyrX125DcOGMYLGWPdlbTYx5+h6WLVa3b9P1Lv8ARKhmdDx6sF2Rj2XPsbi1ZmxwYfbc+mtt bm1/pGemy/d6n+ESMZC9NlDLAkAHf9rkER/Apl0VX1XpN+VXbdd6eNmWYgaxjQ94bZjY1Fw9Vza2 e/N9S3/B+mz/AIX1FKn6qY92QzFGRY281UWOkMhzrnVtdRW36VVvpG51Hqu/T2VsS4Jdle9Du86x 5YQ4c9kt53Fx1JO6fNbv/N3DbXa+y7IIxqG2WODGsqe54xXsbi5D9zXbWZv8279L/N2f9qPYLD+r TsjpuJnWXGkZTb3OLmxWzY212GXv+l6eRbi3Ntf/AINiXCeyvdhV3p/KTjEzHkICYrc6x9XK+nYl lrbbXW0Pa2xtjGhh325GJsZYw/z9bsPe9n57P+LWEeECCDRXRkJCx5NhJJJRMr//0vNjyfiU7XOa 4OaS1zSC1wMEEahzXBRP0j8Sr2F0Tq2fTXfh4xtquyBh1v3MaDe5hvFP6R7P8C31HP8A5piktbST p/TM/Pxy6rJFbWl9VNL32fpHBjsnJrqawPrZto3WWers9bf6aLb0TqopyLHZDbK6QbbBvsO8/aLc J5a1w99m/Dty/wDwvX/pUfE6b9bsJtmFjUOYzOAEbqiHbrKMFluNf6n6P1bM/FrbdTZ+mx8iu79J i/pVZNP1+ssDBXYDebRsY2hjHGu53Tsv1GM2U/os3Ks+0ep9B932/wD7sJwlCtbYzHLenDV9ULvq /wBZZknEu6i2q6pxfW0vyCD6t4wftFT2M2frOQ6qzf8An12eoh4P1e61l41NeLkt+zZNjKTSbLNj QXZH6S+sD0/RZbiWP/r20or+nfXKWepFb3tc2tz78Wsmqp56u62lzrWfqlVv659tZ+r7Nn6b0VKp n16L8h9FdjTjOtZkMrZQwMOMaLshnosaxnsffj3fo2bL/V9T9J+mR4oeK3gy1oY/saFHTs2+qq85 7an3B17a7LLfUFVBubbmbmtc1/2f08r9FW/7Vs9Syur9IrtfROvNobZVnTjMpvdW9ltzWCljKr9r WQ30/ttV1XoVf9bu/mlOjo/15oazFpxzDbdjKpxnPJe6tz2e5z7X4HqZ1P2hv/J7H5X6x/OIbWfX Gxxorsa6u6p+RubbiDHNNTrMa237S1/2Ouih9tmP/O7K/oVfQYgJQ62kwyXpw14/+iqzOiddxMhr 7eoQ9gsDLfVyA5rKqr7btHtbexjqcO1jPb+n/Ren+hS/5v8AWnRTRni9mLHptbZeNpb9lsmiuxrf 5pmbRftr/wBDZ6f6WtPR0r65WNt9Cnc3qZdZc5z8bV9wZW6X2P8A1HIy6+pVV00/oLsqjLr9H1N6 bDp+uuQKL8RlhGQTk0WEUsBOPbj4j7N1uz0/s+RjYddrLP8AjH/obXo8cNd0e3l01j9iNnSOs1X4 ePV1Dbbc4YjAyy8Cn1Gtym1b2t2OqsqfXc+rF9TZ/hWb0q/q51N8VMzGguB30kZAeKWG+pj343pe o5nq0X49eHs9f9J/M7LkZmH9cbMfHvo9O9lNhdjuotw7XOfVsxXP249j7cr7My6mlr7PVZTj21f4 L0lFnSvrrjYhxa8eyumj1Kw1vo+oYfk419VT5+05DKLfttr66nWfZP8AlD9FW+q9Lih4p4MveNuR 1AZtGTbg5d7rjjWOBHqOezefp2M9T6Ln/n+z1f8ASKqVtZX1Z+tN19uRfhusstsmy5jqSx9j311f o7KLPQsfZk5FdP6H/tT61X87Rf6eZnYOX0+/7PlsDLdjLBtc2xrmWNFtVtV1DrKba7GO+nVYmWL0 ZBEgC/rS6SZJMXv/0/NTyfiVudI+t2X0rCpwacXHsoptOQd4dvfa59dnquta7fXspo+zenV+j9P3 2fpdiwnfSPxKSep6MfXjqDGCqrFxq6q7WWY7Ie8VirGZ07Ho/Tvt9Wmr0MbL/S/z2Xj/AKVFb/jB 6r+kYcXG9G6XW0j1Nu91l2Rm2M/Sfovt7cm2jI2f4P0fR/SUrl0gdde+iCno2fXLZj1YbOmUMw6D c2vGbdkCv0siuzFvrsb6x9S70r7P1v8AnlHF+vPWcfLyMvbS9+VlV5dlcOawekHV/Zq2td/MPrdU z9/9BWufjzH3pS0efx4+5KlPS43186tj0Y9TaaSzGNTgSXh1j6PsWx9lgd6nu/ZrfWqb+gt9b9L/ ADWP6T0fXm6i1trMCv1WV3UC45GT63pZFjsu1pyPV9T1/tFjn/av55cwSSZJSSU9JjfXbLxmYoZh 0ubh1GoA23yW/oQyp7vW3fYv1Wv1un/0K/1cn9FX6v6KPSfrx1TpeJThV00XY9Eem1+9u1wuuy32 1ei9npPt+0+i/b/g6Mb/AES50GDI5TyDzofHslQU7ON9aszEzGZWPUwGu7MyAyx9lm451dWPc265 7/Wu9P7Oy5lj3ep6v84rLfrv1De227HpvvqaBi2vNg