[1]
J. Hertz, A. Krogh and R. G. Palmer, Introduction to the Theory of Neural
Computation, Addison-Wesley, Redwood City (1991).
[2]
J. Alspector, R. Goodman and T. X. Brown, eds., Applications of Neural Networks to
Telecommunications, Lawrence Erlbaum, Hillsdale (1993).
[3]
J. Alspector, R. Goodman and T. X. Brown, eds., Applications of Neural Networks to Telecommunications
2, Lawrence Erlbaum, Hillsdale (1995).
[4]
J. Alspector, R. Goodman and T. X. Brown, eds., Applications of Neural Networks to
Telecommunications 3, Lawrence Erlbaum, Hillsdale (1997).
[5]
IEEE
Journal on Selected Areas in Communications vol. 15, no. 2 (1997).
[6]
IEEE
Journal on Selected Areas in Communications vol. 18, no. 2 (2000).
[7]
S. H. Bang, B. J. Sheu and J. Choi,
“Programmable VLSI neural network processors for equalization of digital
communication channels”, 1-12 in [2] (1993).
[8]
J. Cid-Suerio and A. R. Figueiras-Vidal,
“Improving conventional equalizers with neural networks”, 20-26 in [2] (1993).
[9]
T. X. Brown, “Neural netwroks for adaptive
equalization”, 27-33 in [2] (1993).
[10] M.
Meyer and G. Pfeiffer, “Multilayer perception based equalizers applied to nonlinear
channels”, 188-195 in [2] (1993).
[11] M.
K. Sönmez and T. Adali, “Channel equalization by distribution learning: the
least relative entropy algorithm”, 218-224 in [2] (1993).
[12] M.
J. Bradley and P. Mars, “Analysis of recurrent networks as digital communication
channel equalizer”, 1-8 in [3] (1995).
[13] D.
S. Reay, “Nonlinear channel equalization using associative memory neural
networks”, 17-24 in [3] (1995).
[14] A.
Jayakumar and J. Alspector, “Experimental analog neural network based decision
feedback equalizer for digital mobile radio”, 33-40 in [3] (1995).
[15]
Q. Gan, N. Sundararajan, P. Saratchandran and R.
Subramanian, “Equalisation of rapidly time-varying channels using an efficient
RBF neural network”, 224-231 in [4] (1997).
[16] E.
Dubossarsky, T. R. Osborn and S. Reisenfeld, “Equalization and the impulsive
MOOSE: Fast adaptive signal recovery in very heavy tailed noise”, 232-240 in
[4] (1997).
[17] K.
Raivio, J. Henrikson and O. Simula, “Neural receiver structures based on
self-organizing maps in nonlinear multipath channels”, 241-247 in [4] (1997).
[18] T.
X. Brown, “Neural Networks for Switching”, IEEE
Communications Magazine 27,
72-80 (1989).
[19] S.
Amin and M. Gell, “Constrained optimization for switching using neural
networks”, 106-111 in [2] (1993).
[20] Y.
K. Park, V. Cherkassky and G. Lee, “ATM cell scheduling for broadband switching
systems by neural network”, 112-118 in [2] (1993).
[21] Y.
K. Park and G. Lee, “NN based ATM cell scheduling with queue length-based
priority scheme”, 261-269 in [5] (1997).
[22] A.
Varma and R. Antonucci, “A neural-network controller for scheduling packet
transmissions in a crossbar switch”, 121-128 in [3] (1995).
[23]
A. Murgu, “Adaptive flow control in multistage
communications networks based on a sliding window learning algorithm”, 112-120
in [3] (1995).
[24]
W. K. F. Lor and K. Y. M. Wong, “Decentralized
neural dynamic routing in circuit-switched networks”, 137-144 in [3] (1995).
[25]
P. Campbell, A. Christiansen, M. Dale, H. L.
Ferrá, A. Kowalczyk and J. Szymanski, “Experiments with simple neural networks
for real-time control”, 165-178 in [5] (1997).
[26]
A. Christiansen, A. Herschtal, M. Herzberg, A.
Kowalczyk and J. Szymanski, “Neural networks for resource allocation in
telecommunication networks “, 265-273 in [4] (1997).
[27]
S. Wu and K. Y. M. Wong, “Overload control for
distributed call processors using neural networks”, 149-156 in [4] (1997).
[28]
S. Wu and K. Y. M. Wong, “Dynamic overload
control for distributed call processors using the neural network method”, IEEE Trans. of Neural Networks 9, 1377-1387 (1998).
[29] B.
de Vries, C. W. Che, R. Crane, J. Flanagan, Q. Lin and J. Pearson, “Neural
network speech enhancement for noise robust speech recognition “, 9-16 in [3]
(1995).
[30] S.
Frederickson and L. Tarassenko, “Text-independent speakers recognition using
radial basis functions”, 170-177 in [3] (1995).
[31] N.
Kasabov, “Hybrid environments for building comprehensive AI and the task of
speech recognition” 178-185 in [3] (1995).
[32] E.
Barnard, R. Cole, M. Fanty and P. Vermeulen, “Real-world speech recognition
with neural networks” 186-193 in [3] (1995).
[33] M.
C. Yuang, P. L. Tien and S. T. Liang, “Intelligent video smoother for
multimedia communications” 136-146 in [5] (1997).
[34] R.
A. Bustos and T. D. Gedeon, “Learning synonyms and related concepts in document
collections” 202-209 in [3] (1995).
[35] T.
D. Gedeon, B. J. Briedis, R. A. Bustos, G. Greenleaf and A. Mowbray, “Query
word-concept clusters in a legal document collection” 189-197 in [4] (1997).
[36] H.
Liu and D. Y. Y. Yun, “Self-organizing finite state vector quantization for
image coding” 176-182 in [2] (1993).
[37] T.
D. Chieuh, T. T. Tang and L. G. Chen, “Vector quantization using
tree-structured self-organizing feature maps” 259-265 in [2] (1993).
[38] F.
Mekuria and T. Fjällbrant, “Neural networks for efficient adaptive vector
quantization of signals” 218-225 in [3] (1995).
[39] S.
Carter, R. J. Frank and D. S. W. Tansley, “Clone detection in
telecommunications software systems: a neural net approach” 273-280 in [2]
(1993).
[40] P.
Barson, N. Davey, S. Field, R. Frank and D. S. W. Tansley, “Dynamic competitive
learning applied to the clone detection problem” 234-241 in [3] (1995).
[41] J.
T. Connor, “Predition of access line growth” 232-238 in [2] (1993).
[42] C.
Giraud-Carrier and M. Ward, “Learning customer profiles to generate cash over
the Internet” 165-170 in [4] (1997).
[43] M.
C. Mozer, R. Wolniewicz, D. B. Grimes, E. Johnson and H. Kaushansky, “Churn
reduction in the wireless industry”, Advances
in Neural Information Processing Systems 12, S. A. Solla, T. K. Leen, K.-R. Müller, eds., 935-941, MIT
Press, Cambridge (2000).
[44] A.
P. Engelbrecht and I. Cloete, “Dimensioning of telephone networks using a
neural network as traffic distribution approximator”, 72-79 in [3] (1995).
[45] C.
X. Zhang, “Optimal traffic routing using self-organization principle “, 225-231
in [2] (1993).
[46] L.
Lewis, U. Datta and S. Sycamore, “Intelligent capacity evaluation/planning with
neural network clustering algorithms”, 131-139 in [4] (1997).
[47] D.
B. Hoang, “Neural networks for network topological design”, 140-148 in [4]
(1997).
[48] G.
Wang and N. Ansari, “Optimal broadcast scheduling in packet radio networks
using mean field annealing”, 250-260 in [5] (1997).
[49] A.
Jagota, “Scheduling problems in radio networks using Hopfield networks”, 67-76
in [2] (1993).
[50] F.
Comellas and J. Ozón, “Graph coloring algorithms for assignment problems in
radio networks”, 49-56 in [3] (1995).
[51] M.
O. Berger, “Fast channel assignment in cellular radio systems”, 57-63 in [3]
(1995).
[52] M.
W. Dixon, M. I. Bellgard and G. R. Cole, “A neural network algorithm to solve
the routing problem in communication networks”, 145-152 in [3] (1995).
[53] R.
M. Goodman and B. E. Ambrose, “Learning telephone network trunk reservation
congestion control using neural networks”, 258-264 in [3] (1995).
[54] H.
I. Fahmy, G. Develekos and C. Douligeris, “Application of neural networks and
machine learning in network design”, 226-237 in [5] (1975).
[55] C.
S. Hood and C. Ji, “An intelligent monitoring hierarchy for network
management”, 250-257 in [3] (1995).
[56] C.
Cortes, L. D. Jackel and W. P. Chiang, “Predicting failures of telecommunication
paths: limits on learning machine accuracy imposed by data quality”, 324- 333
in [3] (1995).
[57] L.
Lewis and S. Sycamore, “Learning index rules and adaptation functions for a
communications network fault resolution system”, 281-287 in [2] (1993).
[58] M.
Collobert and D. Collobert, “A neural system to detect faulty components on
complex boards in digital switches”, 334-338 in [3] (1995).
[59] R.
Goodman and B. Ambrose, “Applications of learning techniques to network
management”, 34-44 in [2] (1993).
[60] A.
Chattell and J. B. Brook, “A neural network pre-processor for a fault diagnosis
expert system”, 297-305 in [2] (1993).
[61] A.
Holst and A. Lansner, “Diagnosis of technical equipment using a Bayesian neural
network”, 147-153 in [2] (1993).
[62] A.
Holst and A. Lansner, “A higher order Bayesian neural network for
classification and diagnosis”, 347-354 in [3] (1995).
[63] H.
C. Lau, K. Y. Szeto, K. Y. M. Wong and D. Y. Yeung, “A hybrid expert system for
error message classification”, 339-346 in [3] (1995).
[64] T.
Sone, “Using distributed neural networks to identify faults in switching
systems”, 288-296 in [2] (1993).
[65] T.
Sone, “A strong combination of neural networks and deep reasoning in fault
diagnosis”, 355-362 in [3] (1995).
[66] P.
Leray, P. Gallinari and E. Didelet, “Local diagnosis for real-time network
traffic management”, 124-130 in [4] (1997).
[67] H.
Wietgrefe, K. D. Tuchs, K. Jobmann, G. Carls, P. Fröhlich, W. Nejdl and S.
Steinfeld, “Using neural networks for alarm correlation in cellular phone
networks”, 248-255 in [4] (1997).
[68] B.
P. Yuhas, “Toll-fraud detection”, 239-244 in [2] (1993).
[69] J.
T. Connor, L. B. Brothers and J. Alspector, “Neural network detection of
fraudulent calling card patterns”, 363-370 in [3] (1995).
[70] S.
D. H. Field and P. W. Hobson, “Techniques for telecommunications fraud management”,
107-115 in [4] (1997).
[71]
M. Junius and O. Kennemann, “Intelligent
techniques for the GSM handover process”, 41-48 in [3] (1995).
[72]
J. Biesterfeld, E. Ennigrou and K. Jobmann,
“Neural networks for location prediction in mobile networks”, 207-214 in [4]
(1997).
[73]
K. Smith and M. Palaniswami, “Static and dynamic
channel assignment using neural networks, 238-249 in [5] (1997).
[74]
S. Singh and D. Bertsekas, “Reinforcement
learning for dynamic channel allocation in cellular telephone systems”, Advances in Neural Information Processing
Systems 9, M. C. Mozer, M. I.
Jordan, T. Petsche, eds., 974-980, MIT Press, Cambridge (1997).
[75]
E. J. Wilmes and K. T. Erickson, “Reinforcement
learning and supervised learning control of dynamic channel allocation for
mobile radio systems”, 215-223 in [4] (1997).
[76]
A. Hiramatsu, “ATM communications network
control by neural networks”, IEEE Trans.
on Neural Networks, 1, 122-140
(1990).
[77]
A. Hiramatsu, “Integration of ATM call admission
control and link capacity control by distributed neural networks”, IEEE J. Selected Areas in Commun. 9, 1131-1138 (1991).
[78]
S. A. Youssef, I. W. Habib and T. N. Sadaawi, “A
neurocomputing controller for bandwidth allocation in ATM Networks”, 191-199 in
[5] (1997).
[79]
T. X. Brown, “Adaptive access control applied to
Ethernet data”, Advances in Neural
Information Processing Systems 9,
M. C. Mozer, M. I. Jordan, T. Petsche, eds., 932-938, MIT Press, Cambridge
(1997).
[80] C.
K. Tham and W. S. Soh, “ATM connection admission control using modular neural
networks”, 71-78 in [4] (1997).
[81] M.
B. Zaremba, K. Q. Liao, G. Chan and M. Gaudreau, “Link bandwidth allocation in
multiservice networks using neural technology”, 64-71 in [3] (1995).
[82] E.
Nordström and J. Carlström, “A reinforcement learning scheme for adaptive link
allocation in ATM networks”, 88-95 in [3] (1995).
[83] O.
Gällmo and L. Asplund, “Reinforcement learning by construction of hypothetical
targets”, 300-307 in [3] (1995).
[84] P.
Marbach, O. Mihatsch and J. N. Tsitiklis, “Call admission control and routing
in integrated services networks using neuro-dynamic programming”, 197-208 in
[6] (2000).
[85] H.
Tong and T. X. Brown, “Adaptive call admission control under quality of service
constraints: a reinforcement learning solution”, 209-221 in [6] (2000).
[86] J.
Carlström and E. Nordström, “Control of self-similar ATM call traffic by
reinforcement learning”, 54-62 in [4] (1997).
[87] A.
D. Estrella, E. Casilari, A. Jurado and F. Sandoval, “ATM traffic neural
control: Multiservice call admission and policing function”, 104-111 in [3]
(1995).
[88] A.
Faragó, M. Boda, H. Brandt, T. Henk, T. Trón and J. Bíró, “Virtual lookahead –
a new approach to train neural nets for solving on-line decision problems”,
265-272 in [3] (1995).
[89] I.
Mahadevan and C. S. Raghavendra, “Admission control in ATM networks using fuzzy-ARTMAP”,
79-87 in [4] (1997).
[90] Y.
Liu and C. Douligeris, “Rate regulation with feedback controller in ATM
networks – a neural network approach”,
200-208 in [5] (1997).
[91] A.
Pitsillides, Y. A. Şekercioğlu and G. Ramamurphy, “Effective control of traffic
flow in ATM networks using fuzzy explicit rate marking (FERM), 209-225 in [5]
(1997).
[92] A.
Murgu, “Fuzzy mean flow estimation with neural networks for multistage ATM
systems”, 27-35 in [4] (1997).
[93] Z.
Fan and P. Mars, “Dynamic routing in ATM networks with effective bandwidth
estimation by neural networks”, 45-53 in [4] (1997).
[94] A.
Faragó, J. Bíró, T. Henk and M. Boda, “Analog neural optimization for ATM
resource management”, 156-164 in [5] (1997).
[95] T.
X. Brown, “Bandwidth dimensioning for data traffic”, 88-96 in [4] (1997).
[96] T.
Edwards, D. S. W. Tansley, R. J. Frank and N. Davey, “Traffic trends analysis
using neural networks”, 157-164 in [4] (1997).
[97] E.
Casilari, A. Reyes, A. D. Estrella and F. Sandoval, “Generation of ATM video
traffic using neural networks”, 19-26 in [4] (1997).
[98] E.
Casilari, A. Jurendo, G. Pansard, A. D. Estrella and F. Sandoval, “Model
generation of aggregate ATM traffic using a neural control with accelerated self-scaling”,
36-44 in [4] (1997).
[99] A.
A. Tarraf, I. W. Habib and T. N. Sadaawi, “Neural networks for ATM multimedia
traffic prediction”, 85-91 in [2] (1993).
[100] J.
E. Neves, L. B. de Almeida and M. J. Leitão, “ATM call control by neural
networks”, 210-217 in [2] (1993).
[101] E.
Gelenbe, A. Ghanwani and V. Srinivasan, “Improved neural heuristics for
multicast routing”, 147-155 in [5] (1997).
[102]
Z. Ali,
A. Gafoor and C. S. G. Lee, “Media synchronization in multimedia web
environment using a neuro-fuzzy framework”, 168-183 in [6] (2000).
[103]
F.
Davoli and P. Maryni, “A two-level stochastic approximation for admission
control and bandwidth allocation”, 222-233 in [6] (2000).
[104] C. J.
Chang, B. W. Chen, T. Y. Liu and F. C. Ren, “Fuzzy/Neural congestion control
for integrated voice and data DS-CDMA/FRMA cellular networks”, 283-293 in [6]
(2000).
[105] G.
Cybenko, “Approximation by superpositions of a sigmoid function”, Mathematics of Control, Signals and Systems
2, 303 (1989).
[106] J.
Boyan and M. L. Littman, “Packet routing in dynamically changing networks: a
reinforcement learning approach”, Advances
in Neural Information Processing Systems 6, J. Cowan, G. Tesauro, J. Alspector, eds., 671-678, Morgan
Kaufmann, San Francisco (1994).
[107] S.
Choi and D. Y. Yeung, “Predictive Q-routing: a memory-based reinforcement
learning approach to adaptive traffic control”, Advances in Neural Information Processing Systems 8, D. Touretzky, M. C. Mozer, M. E.
Hasselmo, eds., 945-951, MIT Press, Cambridge (1996).
[108] L.
Hérault, D. Dérou and M. Gordon, “New Q-routing approaches to adaptive traffic
control”, 274-281 in [4] (1997).
[109] T.
X. Brown, “Low power wireless communication via reinforcement learning”, Advances in Neural Information Processing
Systems 12, S. A. Solla, T. K.
Leen, K.-R. Müller, eds., 893-899, MIT Press, Cambridge (2000).
[110] C.
M. Bishop, Neural Networks for Pattern
Recognition, Clarendon Press, Oxford (1995).
[111] S.
Amari, Differential-Geometrical Methods
in Statistics, Springer-Verlag, New York (1985).
[112] J.
J. Hopfield, “Neural networks and physical systems with emergent computational
abilities”, Proc. Natl. Acad. Sci. U.S.A. 79, 2554-2558 (1982).
[113] D.
E. Rumelhart, G. E. Hinton and R. J. Williams, “Learning internal
representations by error propagation”, Parallel
Distributed Processing: Explorations in Microstructure of Cognition 1, 318-362, MIT Press, Cambridge
(1988).
[114] J.
Moody and C. J. Darken, “Fast learning in networks of locally-tuned processing
units”, Neural Computation 1, 281-294 (1989).
[115] J.
Park and I. W. Sandberg, “Universal approximation using radial basis function
networks”, Neural Computation 3, 246-257 (1991).
[116] A.
P. Dempster, N. M. Laird and D. B. Rubin, “Maximum likelihood from incomplete
data via the EM algorithm”, Journal of
the Royal Statistical Society B 39,
1-38 (1977).
[117] B.
E. Boser, I. M. Guyon and V. N. Vapnik, “A training algorithm for optimal
margin classifier”, Proc. 5th
ACM Workshop on Computational Learning Theory, 144-152 (1992).
[118] V.
Vapnik, The Nature Of Statistical
Learning Theory, Springer-Verlag, New York (1995).
[119] F.
Girosi, M. Jones and T. Poggio, “Regularization theory and neural network
architectures”, Neural Computation 10, 1455-1480 (1998).
[120] N.
Christianini and J. Shawe-Taylor, An
Introduction to Support Vector Machines and Other Kernel Based Methods,
Cambridge Univ. Press, Cambridge (2000).
[121] S.
Geman, E. Bienenstock and R. Doursat, “Neural networks and the bias/variance
dilemma”, Neural Computation 4, 1-58 (1992).
[122] M.
G. Bello, “Enhanced training algorithms, and integrated training/architecture
selection for multilayer perceptron networks”, IEEE Trans. Neural Networks 3,
864-875 (1992).
[123] M.
C. Mozer and P. Smolensky, “Skeletonization: a technique for trimming the fat
from a network via relevance assessment”, Advances
in Neural Information Processing Systems 1, 107-115, Morgan Kaufmann, San Mateo (1989).
[124] S.
Amari, “Natural gradient works efficiently in learning”, Neural Computation 10,
252-276 (1998).
[125] W.
H. Press, S. A. Teukolsky, W. T. Vwtterling and B. P. Flannery, Numerical Recipes in C: The Art of Scientific Computing (2nd
ed.), Cambridge University Press, Cambridge (1992).
[126] P.
Hanselka, J. Oehlerich and G. Wegmann, “Adaptation of the overload regulation
method stator to multiprocessor controls and simulation results”, ITC-12, 395-401 (1989).
[127] D.
Manfield, B. Denis, K. Basu and G. Rouleau, “Overload control in a hierarchical
switching system”, ITC-11, 894-900
(1985).
[128] M.
Villen-Altamirano, G. Morales-Andres and L. Bermejo-Saez, “An overload control
strategy for distributed control systems”, ITC-11,
835-841 (1985).
[129] J.
S. Kaufman and A. Kumar, “Traffic overload control in a fully distributed
switching environment”, ITC-12,
386-394 (1989).
[130] M.
J. Best and K. Ritter, Linear
Programming: Active Set Analysis and Computer Programs, Prentice-Hall,
Englewood Cliffs (1985).
[131] K.
Stokbro, D. K. Umberger and J. A. Hertz, “Exploiting neurons with localized
receptive fields to learn chaos”, Complex
Syst. 4, 603-622 (1990).
[132] R. M. Goodman, C. M. Higgins, J. W. Miller
and P. Smyth, “Rule-based neural networks for classification and probability
estimation”, Neural Computation 4, 781-803 (1992).
[133] L. Breiman, J. H. Friedman, R. A. Olshen and
C. J. Stone, Classification and
Regression Trees, Wadsworth, Pacific Grove (1984).
[134] J. Sklansky and G. N. Wassel, Pattern Classifiers and Trainable Machines,
Springer-Verlag, New York (1981).
[135] K.
Y. M. Wong and H. C. Lau, “Neural network classification of non-uniform data”, Progress in Neural Information Processing,
S. Amari, L. Xu, L. W. Chan, I. King, K. S. Leung, eds., 242-246, Springer, Singapore (1996).
[136] H. C. Lau, “Neural network classification
techniques for diagnostic problems”, MPhil Thesis, HKUST (1995).
[137] W. K. Au, “Conventional and
neurocomputational methods in teletraffic routing”, MPhil Thesis, HKUST (1999).
Tidak ada komentar:
Posting Komentar