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AFAR:adaptive fuzzy ant-based routing for communication networks

Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2008年第12期9卷 1666-1675页

作者： Seyed Javad mIRABEDINI m.ham.ad teshnehlab m.H.SHENASA Ali mOVAGHAR Amir masoud RAHmANI Engineering Department Science and Research BranchIslamic Azad UniversityTehranIran Control Department K.N.Toosi Technology UniversityTehranIran Computer Department Sharif UniversitTehranIran

We propose a novel approach called adaptive fuzzy ant-based routing (AFAR), where a group of intelligent agents (or ants) builds paths between a pair of nodes, exploring the network concurrently and exchanging obtained information to up-date the routing tables. Routing decisions can be made by the fuzzy logic technique based on local information about the current network state and the knowledge constructed by a previous set of behaviors of other agents. The fuzzy logic technique allows multiple constraints such as path delay and path utilization to be considered in a simple and intuitive way. Simulation tests show that AFAR outperforms OSPF, AntNet and ASR, three of the currently most important state-of-the-art algorithms, in terms of end-to-end delay, packet delivery, and packet drop ratio. AFAR is a promising alternative for routing of data in next generation networks.

关键词： adaptive fuzzy routing algorithm Swarm intelligence Routing table Communication network Packet delay,Throughput

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Artificial Neural Networks Application to Predict Wheat Yield Using Climatic Data

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Journal of Agricultural Science and Technology(B) 2011年第1期1卷 76-88页

作者： B. Safa A. Khalili m. teshnehlab A. Liaghat Department of Agronomy and Horticulture University of Nebraska-Lineoln 202 Keim Hall Lincoln NE 68583-0915 USA Department of Irrigation and Reclamation Engineering College of Agriculture University of Tehran Karaj lran Department of Electrical Engineering K.N. Toosi Technology University Tehran lran

The goal of this study was to apply artificial neural networks to predict rain-fed wheat yield using meteorological data a few days to few months before harvesting. The climatic observation data used; were mean of daily minimum and maximum temperature, extreme of daily minimum and maximum temperature, sum of daily rainfall, number of rainy days, sum of daily sun hours, mean of daily wind speed, extreme of daily wind speed, mean of daily relative humidity, and sum of daily water requirements that were collected during 1990-1999 in Sararood Station for wheat phenological stages consisting; sowing, germination, emergence, 3rd leaves, tillering, stem formation, heading, flowering, milk maturity, wax maturity, full maturity, separately for each growing season. Then, they arranged in a matrix whose rows form each of the statistical years and the columns are meteorological factors at each phenological stage. Finally, the obtained model had the following capabilities： Prediction of wheat yield with maximum errors of 45-60 kg/ha at least two months before full maturity stage, determination of the sensitivity of each phenological stage with respect to meteorological factors, and determination of the priority order and importance of each meteorological factor effective in plant growth and crop yield.

关键词： Artificial neural network wheat yield climatic data phenological stage crop model.

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