Kuo-Hsiang Cheng

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—A fuzzy–neural sliding-mode (FNSM) control system is developed to control power electronic converters. The FNSM control system comprises a neural controller and a compensation controller. In the neural controller, an asymmetric fuzzy neural network is utilized to mimic an ideal controller. The compensation controller is designed to compensate for the(More)
— As digital images are often in compressed forms, image retrieval involves full decoding of images prior to feature extraction. The decoding process can be computation-expensive so feature extraction in compressed domain is desired. In this work, wavelet-based features are extracted as unified features for retrieval of JPEG and JPEG2000 images. A fast(More)
A hybrid learning neuro-fuzzy system with asymmetric fuzzy sets (HLNFS-A) is proposed in this paper. The learning methods of random optimization (RO) and least square estimation (LSE) are used in hybrid way to train the system parameters of HLNFS-A to achieve stable and fast convergence. In the HLNFS-A, the premise and the consequent parameters are updated(More)
A soft computing filtering approach is proposed for adaptive noise cancellation. The goal of noise cancellation is to extract the desired signal from its noise-corrupted version, using the proposed neuro-fuzzy system (NFS) as an adaptive filter. Traditional linear filtering may not be good enough to handle with the noise complexity. In the study, the NFS(More)
A simple, highly efficient intra prediction algorithm to reduce the computational complexity of H.264/AVC High Profile is proposed. The algorithm combines two methods. The first method is a quant-based block-size selection decision that is based on the sum of the quantization AC coefficients among intra 8 × 8 mode predictions, combined with an error(More)