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One of the main obstacles to reliable communications is the inter symbol interference. An adaptive equalizer is required at the receiver to mitigate the effects of non-ideal channel characteristics and to obtain reliable data transmission. The conventional way to combat with ISI is to include an equalizer in the receiver. This paper presents a new approach to equalization of communication channels...
This paper presents a new approach to equalization of communication channels using Functional Link Artificial Neural Networks (FLANNs). A novel method of training the FLANNs using PSO Algorithm is described. The performance of the proposed network has been compared with the conventional LMS based channel equalizer and FLANN trained with BP algorithm based equalizer. From the results it can be noted...
In this paper, the theories of genetic algorithm (GA) and back propagation (BP) algorithm are introduced. For the purpose of overcoming the disadvantages of standard BP algorithm, such as local optimum and low convergence speed, the paper adopts genetic algorithm optimizing BP neural network for training. By analyzing computer stimulation results and comparing with traditional blind equalization algorithm,...
In this paper, the principle of neural network blind equalization algorithm was depicted, the fault of traditional BP (back propagation) algorithm, which is slow convergence rate and easy to fall into a local minimum, was analyzed, the influence of momentum factor to equalization performance was researched, the convergence track of traditional BP algorithm and momentum factor BP algorithm was compared...
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