Sirapat Chiewchanwattana

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In this paper, a novel meta-heuristic technique an improved Grey Wolf Optimizer (IGWO) which is an improved version of Grey Wolf Optimizer (GWO) is proposed. The performance is evaluated by adopting the IGWO to training q-Gaussian Radial Basis Functional-link nets (qRBFLNs) neural networks. The function approximation problems in regression areas and the(More)
In this paper, radial basis functional-link nets (RBFLNs) based on a q-Gaussian function is proposed. In order to enhance the generalization performance of a modified radial basis function neural network and enhance the performance of the new network, the evolutionary algorithm named real-coded chemical reaction optimization (RCCRO), is presented for(More)
Multilevel thresholding is the most important method for image processing. Conventional multilevel thresholding methods have proven to be efficient in bi-level thresholding; however, when extended to multilevel thresholding, they prove to be computationally more costly, as they comprehensively search the optimal thresholds for the objective function. This(More)
In this paper, an improved version of the moth-flame optimization (MFO) algorithm for image segmentation is proposed to effectively enhance the optimal multilevel thresholding of satellite images. Multilevel thresholding is one of the most widely used methods for image segmentation, as it has efficient processing ability and easy implementation. However, as(More)
The most difficult problem with the extreme learning machine is the selection of the hidden nodes size. The proper number of hidden nodes is predefined through a trial and error approach. The convex incremental extreme learning machine (CI-ELM) has been proposed to tackle this problem. CI-ELM is an incremental constructive neural network with universal(More)