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In this paper we develop an improved minimization criterion for normalized least mean squares (NLMS) algorithm using past weight vectors and adaptive learning rate. The proposed criterion minimizes the summation of each squared Euclidean norm of difference between the currently updated weight vector and past weight vector. The result of the modified NLMS algorithm has lower misalignment than the conventional...
In this paper, the neural network based approach to find the approximate beam width of 15 element dynamically phased array in a particular scan angle is presented. The dynamically phased array smart antenna is a group of antennas in which the relative phases of the respective signals feeding the antennas are varied in such a way that the effective radiation pattern of the array is reinforced in a...
In mobile radio environment, the co-channel interference (CCI) resulting from frequency reuse is the main cause of call drops, unnecessary handoffs, compromising voice quality and channel capacity reduction. CCI is one of the most significant factors limiting the capacity and scalability of wireless networks. Various techniques such as power control, antenna down tilting, cell sectoring and multiple...
The Artificial Neural Networks (ANN) has been applied to channel equalization with quite promising results. Although an ANN takes time during it’s training, it generates instant results during its implementation phase. ANN are capable of performing complex non-linear mapping between their input and output space. In this paper we propose a new complex neural equalizer based on a simple model of polynomial...
The noise cancellation has become the essential requirement in the field of signal processing. This paper presents the noise-cancellation technique based on higher-order neural network (HONN). These networks consists of an aggregation function, generalized neural network (GNN) which is based on the generalized-mean of all the inputs applied to it. The results of GNN is also compared with the existing...
The noise cancellation has become the essential requirement in the field of signal processing. This paper presents the noise-cancellation technique based on higher-order neural network (HONN). These network consists of an aggregation function(Generalized-mean Neuron) which is based on the generalized mean of all the inputs applied to it. The simulation results show these network can be suitably applied...
In this paper, a learning algorithm for a single integrate-and-fire neuron (IFN) is proposed and tested for various applications in which a multilayer perceptron based neural network is conventionally used. It is found that a single IFN is sufficient for the applications that require a number of neurons in different hidden layers of a conventional neural network. Several benchmark and real-life problems...
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