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)
Circular Extreme Learning Machine (C-ELM) is an extension of Extreme Learning Machine. Its power is mapping both linear and circular separation boundaries. However, C-ELM uses the random determination of the input weights and hidden biases, which may lead to local optimal. This paper proposes a hybrid learning algorithms based on the C-ELM and the(More)
In this paper, we present an improvement of the modified cuckoo search (MCS) method. We focus on a new nest generation from the top nest group. This group of nests assists a better local search. We use Tent map chaotic sequences to replace the constant parameter, inverse golden ratio of MCS. This process aims to find a better solution in case of multi-modal(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)