Proceedings of the International Workshop on Applications of Neural Networks to Telecommunications 2
Edited by Joshua Alspector, Rodney Goodman, Timothy X. Brown
Psychology Press – 1995 – 384 pages
Psychology Press – 1995 – 384 pages
This second International Workshop on Applications of Neural Networks to Telecommunications (IWANNT), like the first, was motivated by needs in the rapidly changing telecommunications industry. The last workshop featured an electronic version of the proceedings which was accessible to interested people via the Internet. Since then, the enormous growth of the Internet and the availability of visual network browsers have made remote information access seem commonplace. Further evidence of the revolution in the industry is clear as it changes from a structure where telecommunications networks, equipment, and services are controlled by large vertically integrated companies to many specialized horizontally structured firms. Traditional monopoly telecommunications is evolving into a diverse and dynamic information infrastructure. There will be firms specializing as local access network providers, while others will provide long distance networks, and still others will provide network equipment and terminal or user equipment. Added to the above mix, which was typically the province of a large vertically integrated and regulated monopoly telephone company, will be companies not traditionally thought of as telecommunications providers -- entertainment companies, news sources, and other information sources. There will also be firms specializing in software to control the networks and enable information service applications such as movies-on-demand. Perhaps most significant, the convergence of computers and communications has blurred these industries till they are nearly indistinct.
This workshop featured 50 papers that represent neural network approaches to a variety of needs. There is the need to adapt to changing channel conditions (adaptive non-linear equalization) and the growing importance of wireless access. The convergence of digital services in asynchronous transfer mode (ATM) packet networks requires new methods of access control and neural networks promise a unique approach. There are also papers on applications in switching and routing. There will be a new class of applications in information filtering, data compression, and speech and pattern recognition and understanding which will make use of neural network technology. The distributed nature of networks will stimulate research in network management and decision tools, fault prediction and error recovery, and new methods of managing large software systems. These proceedings show how neural network technology contributes a new tool to tackling the challenging problems of the information age.
Contents: M.J. Bradley, P. Mars, Analysis of Recurrent Neural Networks as Digital Communication Channel Equalizer. B. de Vries, C.W. Che, R. Crane, J. Flanagan, Q. Lin, J. Pearson, Neural Network Speech Enhancement for Noise Robust Speech Recognition. D.S. Reay, Non-Linear Channel Equalisation Using Associative Memory Neural Networks. A. Artés-Rodríguez, F. González-Serrano, A. Figueiras-Vidal, L. Weruaga-Prieto, Compensation of Bandpass Nonlinearities by Look-Up-Tables and CMAC. A. Jayakumar, J. Alspector, Experimental Analog Neural Network Based Decision Feedback Equalizer for Digital Mobile Radio. M. Junius, O. Kennemann, Intelligent Techniques for the GSM Handover Process. F. Comellas, J. Ozón, Graph Coloring Algorithms for Assignment Problems in Radio Networks. M. Berger, Fast Channel Assignment in Cellular Radio Systems. M.B. Zaremba, K-Q. Liao, G. Chan, M. Gaudreau, Link Bandwidth Allocation in Multiservice Networks Using Neural Technology. A.P. Engelbrecht, I. Cloete, Dimensioning of Telephone Networks Using a Neural Network as Traffic Distribution Approximator. T-D. Chiueh, L-K. Bu, Theory and Implementation of an Analog Network That Solves the Shortest Path Problem. E. Nordström, J. Carlström, A Reinforcement Learning Scheme for Adaptive Link Allocation in ATM Networks. A. Garcia-Lopera, A. Ariza Quintana, F. Sandoval Hernandez, Modular Neural Control of Buffered Banyan Networks. A.D. Estrella, E. Casilari, A. Jurado, F. Sandoval, ATM Traffic Neural Control: Multiservice Call Admission and Policing Function. A. Murgu, Adaptive Flow Control in Multistage Communications Networks Based on a Sliding Window Learning Algorithm. A. Varma, R. Antonucci, A Neural-Network Controller for Scheduling Packet Transmissions in a Crossbar Switch. T.X. Brown, A Technique for Mapping Optimization Solutions into Hardware. W.K.F. Lor, K.Y.M. Wong, Decentralized Neural Dynamic Routing in Circuit-Switched Networks. M. Dixon, M. Bellgard, G.R. Cole, A Neural Network Algorithm to Solve the Routing Problem in Communication Networks. T.X. Brown, Classifying Loss Rates with Small Samples. N. Karunanithi, J. Alspector, A Feature-Based Neural Network Movie Selection Approach. S. Fredrickson, L. Tarassenko, Text-Independent Speaker Recognition Using Radial Basis Functions. N. Kasabov, Hybrid Environments for Building Comprehensive AI and the Task of Speech Recognition. E. Barnard, R. Cole, M. Fanty, P. Vermeulen, Real-World Speech Recognition with Neural Networks. A.K. Chhabra, V. Misra, Experiments with Statistical Connectionist Methods and Hidden Markov Models for Recognition of Text in Telephone Company Drawings. R.A. Bustos, T.D. Gedeon, Learning Synonyms and Related Concepts in Document Collections. R. Battiti, A. Sartori, G. Tecchiolli, P. Tonella, A. Zorat, Neural Compression: An Integrated Application to EEG Signals. F. Mekuria, T. Fjällbrant, Neural Networks for Efficient Adaptive Vector Quantization of Signals. S. Field, N. Davey, R. Frank, A Complexity Analysis of Telecommunications Software Using Neural Nets. P. Barson, N. Davey, S. Field, R. Frank, D.S.W. Tansley, Dynamic Competitive Learning Applied to the Clone Detection Problem. E. Bayro-Corrochano, E. Espinoza-Soliz, Neural Network Based Approach for External Telephone Network Management. C.S. Hood, C. Ji, An Intelligent Monitoring Hierarchy for Network Management. R.M. Goodman, B.E. Ambrose, Learning Telephone Network Trunk Reservation Congestion Control Using Neural Networks. A. Faragó, M. Boda, H. Brandt, T. Henk, T. Trón, J. Bíró, Virtual Lookahead - a New Approach to Train Neural Nets for Solving On-Line Decision Problems. P. Campbell, H. Ferrá, A. Kowalczyk, C. Leckie, P. Sember, Neural Networks in Real Time Decision Making. A.F. Nejad, T.D. Gedeon, Analyser Neural Networks: An Empirical Study in Revealing Regularities of Complex Systems. P. Chardaire, A. Kapsalis, J.W. Mann, V.J. Rayward-Smith, G.D. Smith, Applications of Genetic Algorithms in Telecommunications. O. Gällmo, L. Asplund, Reinforcement Learning by Construction of Hypothetical Targets. S. Bengio, F. Fessant, D. Collobert, A Connectionist System for Medium-Term Horizon Time Series Prediction. K. Kohara, Selective Presentation Learning for Forecasting by Neural Networks. C. Cortes, L.D. Jackel, W-P. Chiang, Predicting Failures of Telecommunication Paths: Limits on Learning Machine Accuracy Imposed by Data Quality. M. Collobert, D. Collobert, A Neural System to Detect Faulty Components on Complex Boards in Digital Switches. H.C. Lau, K.Y. Szeto, K.Y.M. Wong, D.Y. Yeung, A Hybrid Expert System for Error Message Classification. A. Holst, A. Lansner, A Higher Order Bayesian Neural Network for Classification and Diagnosis. T. Sone, A Strong Combination of Neural Networks and Deep Reasoning in Fault Diagnosis. J. Connor, L. Brothers, J. Alspector, Neural Network Detection of Fraudulent Calling Card Patterns.