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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Optimizing Functional Link Neural Network Learning Using Modified Bee Colony on Multi-class Classifications
verfasst von : Yana Mazwin Mohmad Hassim, Rozaida Ghazali
Erschienen in: Advances in Computer Science and its Applications
Verlag: Springer Berlin Heidelberg
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Functional Link Neural Network (FLNN) has emerged as an important tool for solving classification problems and widely applied in many engineering and scientific problems. FLNN is known to be conveniently used as compared to ordinary feed forward network like the Multilayer Perceptron (MLP) due to its flat network architecture which employs less tuneable weights. The standard method for tuning the weight in FLNN is using a Backpropagation (BP) learning algorithm. However, BP-learning algorithm has difficulties such as trapping in local minima and slow convergence especially for solving non-linearly separable classification problems. In this work, a modified Artificial Bee Colony (mABC) is used to recover the BP drawbacks. With modifications on the employed bee’s exploitation phase, the implementation of the mABC as a learning scheme for FLNN is expected to give a better accuracy result for the classification tasks.