A new training strategy for neural network using shuffled frog-leaping algorithm and application to channel equalization |
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Authors: | Sunita Panda Archana Sarangi Siba Prasada Panigrahi |
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Affiliation: | 1. Kalam Institute of Technology, Berhampur, Odisha, India;2. ITER, SOA University, Bhubaneswar, Odisha, India;3. CV Raman College of Engineering, Bhubaneswar, Odisha, India |
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Abstract: | This paper makes use of shuffled frog-leaping algorithm (SFLA) as a training algorithm to train multi-layer artificial neural network (ANN). Next, The SFLA ANNs are used for channel equalization. We, in this paper, also introduce SFLA for channel equalization that is formulated as an optimization problem. In short, this paper introduces a novel strategy for training of ANN and also proposes two novel approaches for channel equalization problem using shuffled frog-leaping algorithm (SFLA). The proposed strategies are tested both in time-invariant and time varying channels and interestingly yield better performance than contemporary approaches as evidenced by simulation results. |
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Keywords: | Neural network Shuffled frog-leaping algorithm Channel equalization |
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