Khamron Sunat

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The output weights computing of extreme learning machine (ELM) encounters two problems, the computational and outlier robustness problems. The computational problem occurs when the hidden layer output matrix is a not full column rank matrix or an ill-conditioned matrix because of randomly generated input weights and biases. An existing solution to this(More)
With a high computational complexity of Eigenvector/Eigenvalue calculation, especially with a large database, of a traditional face recognition system, PCA, this paper proposes an alternative approach to utilize a fixed point algorithm for EVD stage optimization. We also proposed the optimization to reduce the complexity during the high computation stage,(More)
This paper proposes an unsupervised discrimination analysis for feature selection based on a property of the Fourier transform of the probability density distribution. Each feature is evaluated on the basis of a simple observation motivated by the concept of optical diffraction, which is invariant under feature scaling. The time complexity is(More)
In this paper, we present a new approach for hand-written character and digit recognitions based on shape descriptor and the Hausdorff Context. We start at finding the corresponding points between two shapes by using a modified shape context. We then use these correspondences as key geometric points for shape alignment with the Thin Plate Spline (TPS)(More)
Most feed-forward artificial neural network training algorithms for classification problems are based on an iterative steepest descent technique. Their well-known drawback is slow convergence. A fast solution is an Extreme Learning Machine (ELM) computing the Moore-Penrose inverse using SVD. However, the most significant training time is pseudo-inverse(More)
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)
This paper presents a low cost reduced instruction set computer (RISC) implementation of an intelligent ultra fast charger for a nickel–cadmium (Ni–Cd) battery. The charger employs a genetic algorithm (GA) trained generalized regression neural network (GRNN) as a key to ultra fast charging while avoiding battery damage. The tradeoff between mean square(More)