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Published in:
2010 | OriginalPaper | Chapter
Adaptive Nonlinear Signal Approximation Using Bacterial Foraging Strategy
Authors : Naik Manoj Kumar, Panda Rutuparna
Published in: Swarm, Evolutionary, and Memetic Computing
Publisher: Springer Berlin Heidelberg
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Uniform approximation of signals has been an area of interest for researchers working in different disciplines of science and engineering. This paper presents an adaptive algorithm based on E. coli bacteria foraging strategy (EBFS) for uniform approximation of signals by linear combinations of shifted nonlinear basis functions. New class of nonlinear basis functions has been derived from a sigmoid function. The weight factor of the newly proposed nonlinear basis functions has been optimized by using the EBFS to minimize the mean square error. Different test signals are considered for validation of the present technique. Results are also compared with Genetic algorithm approach. The proposed technique could also be useful in fractional signal processing applications.