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In this paper a PID Fuzzy-Neural controller (FNC) is designed as an Active Queue Management (AQM) in internet routers to improve the performance of Fuzzy Proportional Integral (FPI) controller for congestion avoidance in computer networks. A combination of fuzzy logic and neural network can generate a fuzzy neural controller which in association with a neural network emulator can improve the output response of the controlled system. This combination uses the neural network training ability to adjust the membership functions of a PID like fuzzy neural controller. The goal of the controller is to force the controlled system to follow a reference model with required transient specifications of minimum overshoot, minimum rise time and minimum steady state error. The fuzzy membership functions were tuned using the propagated error between the plant outputs and the desired ones. To propagate the error from the plant outputs to the controller, a neural network is used as a channel to the error. This neural network uses the back propagation algorithm as a learning technique. Firstly the parameters of PID of Fuzzy-Neural controller are selected by trial and error method, but to get the best controller parameters the Particle Swarm Optimization (PSO) is used as an optimization method for tuning the PID parameters. From the obtained results, it is noted that the PID Fuzzy-Neural controller provides good tracking performance under different circumstances for congestion avoidance in computer networks

The speed of learning in neural network environment is considered as the most effective parameter spatially in large data sets. This paper tries to minimize the time required for the neural network to fully understand and learn about the data by standardize input data. The paper showed that the Z-Score standardization of input data significantly decreased the number of epoochs required for the network to learn. This paper also proved that the binary dataset is a serious limitation for the convergence of neural network, so the standardization is a must in such case where the 0’s inputs simply neglect the connections in the neural network. The data set used in this paper are features extracted from gel electrophoresis images and that open the door for using artificial intelligence in such areas.

h S ab r y Co mp u ter E n g . D ep ar t m en t, Al -Na hr ai n U ni v er s it y, I r aq Abstract --As an effective mechanism acting on the intermediate nodes to support end-to-end congestion control, Active Queue Management (AQM) takes a trade-off between link utilization and delay experienced by data packets. In this paper a linear quadratic optimal controller was designed based on linear control theory for TCP/AQM router. The design specifications, depends on choosing weighting matrices Q and R. One must carry out a trial-and-error process to choose the weighting matrices that can satisfy the design specifications. To overcome this difficulty we employ the Genetic Algorithm (GA) to find the proper weighting matrices. This idea gives a new alternative procedure in time varying feedback control to improve the stability performance. The controller simulation results show the efficiency of the proposed controller.

This work decodes two-class motor imagery (MI) based on four main processing steps: (i) Raw electroencephalographic (EEG) signal is decomposed to single trials and spatial filters are estimated for each trial by common spatial filtering (CSP) method; (ii) features are extracted by taking the log transformation (normal distribution) of the spatially filtered EEG signal; (iii) optimal channel selection algorithm is proposed to reduce the number of EEG channels, such approach is regarded as key technological advantage in the implementation of brain–computer interface (BCI) to reduce the system processing time; (iv) finally, support vector machine (SVM) is employed to discriminate two classes of left and right hand MI. Two variations of SVM were proposed: polynomial function kernel and radial-based function RBF kernel. The results revealed that CSP succeeded in removing the strong correlation bound between the EEG samples by maximizing the variance of class 2 samples while minimizing the variance of class 1 samples. The channel selection algorithm achieved its goal to reduce the data dimension by selecting two channels out of three having the lowest variance entropies of 0.239 and 0.261 for channel 1 and channel 2, respectively. The features vector was divided into 80% train and 20% test with five-fold cross validation. The classification performance of SVM-polynomial kernel was 87.86% while it is 95.72% for SVM-RBF kernel as average accuracy of five-folds for both. Thus SVM-RBF is superior to SVM-Poly in the proposed framework.