Contributed articleA combined neural network approach for texture classification
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A global-best harmony search based gradient descent learning FLANN (GbHS-GDL-FLANN) for data classification
2016, Egyptian Informatics JournalCitation Excerpt :The remaining part of this paper is organized as follows: Preliminaries in Section 4, proposed method in Section 5, experimental setup in Section 6, simulation results and performance comparisons in Section 7, proof of statistical significance in Section 8, conclusion in Section 9 and references. The Functional Link Artificial Neural Network (FLANN) [240] is a class of Higher Order Neural Networks that make use of higher combination of its inputs [241,242] and has been successfully used in many applications such as pattern recognition [243,244], classification [245–247], channel equalization [248], system identification [249–253] and prediction [254]. Even if it has a single-layer network, still it is capable to handle nonlinear separable classification task as compared to MLP.
A self adaptive harmony search based functional link higher order ANN for non-linear data classification
2016, NeurocomputingCitation Excerpt :Notwithstanding the fact that it has a single-layer network, it is capable to handle non-linear separable classification task as compared to MLP. Functional link artificial neural network (FLANN) [38] utilizes the higher combination of its inputs [39,40] and has been successfully applied in a wide spectrum of applications such as pattern recognition [41,42], classification [43–45], channel equalization [46] , system identification [47–52], etc. Almost all the higher order ANNs (HONNs) including functional link higher order ANN (FLANN) are sensitive to random initialization of weight and rely on the learning algorithm adopted.
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2000, Computers and GeosciencesAutomatic estimation of crowd density using texture
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