A multi-agent system “test bed” for evaluating autonomous agents
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Drilling performance prediction using general regression neural networks
IEA/AIE '00 Proceedings of the 13th international conference on Industrial and engineering applications of artificial intelligence and expert systems: Intelligent problem solving: methodologies and approaches
Identifying significant parameters for Hall-Heroult process using general regression neural networks
IEA/AIE '00 Proceedings of the 13th international conference on Industrial and engineering applications of artificial intelligence and expert systems: Intelligent problem solving: methodologies and approaches
Computers and Operations Research - Special issue: Emerging economics
Preliminary quantity estimate of highway bridges using neural networks
ICAAICSE '01 Proceedings of the sixth international conference on Application of artificial intelligence to civil & structural engineering
Comparing the Performance of MLP and RBF Neural Networks Employed by Negotiating Intelligent Agents
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Remote Sensing Imagery for Soil Characterization: a Wavelet Neural Data Fusion Approach
Proceedings of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008
Neural Networks
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Modeling of solar energy for Malaysia using artificial neural networks
ICOSSSE'11 Proceedings of the 10th WSEAS international conference on System science and simulation in engineering
Emissions predictive modelling by investigating various neural network models
Expert Systems with Applications: An International Journal
Road link traffic speed pattern mining in probe vehicle data via soft computing techniques
Applied Soft Computing
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From the Publisher:Understanding Neural Networks is a textbook and workbook that provides a unique interactive learning environment. With or without the aid of a classroom instructor, it allows students and other users to learn about neural networks while gaining practical, hands-on experience with all of the leading network models. Each model is presented as realistically as possible. Also included are chapter exercises and questions, many with illustrations. The key feature of this workbook is the software. Available for PC-ATs and compatibles and the Macintosh, these disks contain a collection of full-featured commercial-quality simulators for the most important network paradigms. The user interface is graphic and easy to use, and the simulators are consistent across all networks. The simulators can also build and train significantly large networks, allowing users to construct networks on their own with data relevant to their problems. Volume 1 covers learning, attractor networks, and hierarchical networks (including back-propagation networks). Volume 2 takes up temporal networks (including recurrent networks), self-organizing networks, higher-order networks, and such new directions in neural networks as fuzzy networks and evolutionary networks. Both volumes contain instructions on how to use the workbook, an introduction, appendixes, a table of random numbers, a glossary, a bibliography, and index. Maureen Caudill is a consultant on neural networks in San Diego. Charles Butler is Senior Principal Scientist at Physical Sciences in Alexandria, Virginia. He is a specialist in the development of neural-network applications. They are the authors of Naturally IntelligentSystems, a comprehensive nonmathematical introduction to neural networks.