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In this paper a novel Fuzzy-based proportional integral derivative (PID) controller, which acts as an active queue manager (AQM) for Internet routers, is proposed. These controllers are used to reduce packet loss and improve network utilization in TePIIP networks. The TePIIP network is modeled as a time delayed system with a saturated input. This is because of limitations of packet dropping probability and effects of propagation delays in TePIIP networks. An improved Fuzzy algorithm is employed to derive optimal or near optimal PID control gains such that the error between the router queue length and the desired queue length is minimized. Basically, in this new methodology, the classical PID and fuzzy controller have been combined by a blending mechanism which depends on a certain function of actuating error. Simulation results show that for the same link utilization, the new hybrid fuzzy PID controller provides better performance than random early detection (RED) and PID controllers.

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This paper discusses the application of a multi-layer perceptron network to estimate direction of arrival (DOA) using ant colony optimization (ACO) for training. ACO simulates the foraging behavior of ant colonies which manage to find the shortest path from nest to feeding source. This technique was originally developed for discrete optimization problems, but recent research efforts has led to some algorithm modifications to make it applicable to continuous optimization problems. In this work we utilize continuous ACO to train a neural network for direction of arrival estimation which encounters an interpolation of a complex nonlinear function. The performance of proposed hybrid approach is compared to radial basis function network that is a well known solution to DOA problem and some improvements in approximation are discussed.Index Terms-Direction of arrival, ant colony optimization, radial basis function neural network