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In this paper, we have presented an efficient single layer ANN structure called functional link ANN (FLANN) for the prediction of short time load demand. In contrast to a feed forward ANN structure, i.e. a multilayer perceptron (MLP), the FLANN is basically a single layer structure in which non-linearity is introduced by enhancing the input pattern with nonlinear function expansion. In FLANN structure...
This paper presents adaptive channel equalization for six standard Stanford University Interim (SUI) channels using Least Mean Square Algorithm (LMS) and Multilayer Perceptron Algorithm (MLP) models. The performance analysis of the adaptive equalizers was done based on the Bit Error Rate (BER). The performance of LMS algorithm is found decent whenever there is no nonlinearity in system, whereas in...
This paper investigates Neural Networks (NNs) based adaptive channel equalization for standard Stanford University Interim (SUI) channels. The NN models like Multilayer Perceptron Algorithm (MLP) and Recurrent Neural Network (RNN) are used to design adaptive equalizers. The Back Propagation (BP) and Real Time recurrent Learning (RTRL) are used for training MLP and RNN models respectively. As NNs are...
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