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This paper presents a new adaptive fuzzy wavelet network controller (A-FWNC) for control of nonlinear affine systems, inspired by the theory of multiresolution analysis (MRA) of wavelet transforms and fuzzy concepts. The proposed adaptive gain controller, which results from the direct adaptive approach, has the ability to tune the adaptation parameter in(More)
This paper applies the Artificial Intelligence technique called Genetic Algorithm (GA) to perform analog integrated circuits design, synthesis and optimization in order to reduce the development time and increase precision of this kind of circuits. In this work an accurate method to determine the device sizes in an analog integrated circuit on the basis of(More)
This paper introduces a new approach for the segmentation of skin lesions in dermoscopic images based on wavelet network (WN). The WN presented here is a member of fixed-grid WNs that is formed with no need of training. In this WN, after formation of wavelet lattice, determining shift and scale parameters of wavelets with two screening stage and selecting(More)
Image classification is an issue that utilizes image processing, pattern recognition and classification methods. Automatic medical image classification is a progressive area in image classification, and it is expected to be more developed in the future. Because of this fact, automatic diagnosis can assist pathologists by providing second opinions and(More)
This paper presents a new two-stage approach to impulse noise removal for medical images based on wavelet network (WN). The first step is noise detection, in which the so-called gray-level difference and average background difference are considered as the inputs of a WN. Wavelet Network is used as a preprocessing for the second stage. The second step is(More)
This paper presents a computer-aided design (CAD) tool for automated sizing and optimization of analog integrated circuits (ICs). This tool uses artificial neural networks (ANNs) in order to deduce the device sizes that optimize the performance objectives while satisfying the constraint specifications. Neural networks can learn and generalize from data(More)