In existing fuzzy logic controllers (FLCs), input variables are mostly the error and the change-of-error regardless of complexity of controlled plants. Either control input u or the change of control input Deltau is commonly used as its output variable. A rule table is then constructed on a two-dimensional (2-D) space. This scheme naturally inherits from conventional proportional-derivative (PD) or proportional-integral (PI) controller. Observing that 1) rule tables of most FLCs have skew-symmetric property and 2) the absolute magnitude of the control input |u| or |Deltau| is proportional to the distance from its main diagonal line in the normalized input space, we derive a new variable called the signed distance, which is used as a sole fuzzy input variable in our simple FLC called single-input FLC (SFLC). The SFLC has many advantages: The total number of rules is greatly reduced compared to existing FLCs, and hence, generation and tuning of control rules are much easier. The proposed SFLC is proven to be absolutely stable using Popov criterion. Furthermore, the control performance is nearly the same as that of existing FLCs, which is revealed via computer simulations using two nonlinear plants.
In biomedical images, one of the serious issues is noise which affects their coherent nature. To analyze the results for the detection and treatment of disease, it is essential to remove the noise. The advancement in brain imaging technologies requires reasonable techniques for pre-processing steps like denoising, deblurring, contrast enhancement, etc. Magnetic resonance imaging (MRI) images of the human brain are often corrupted with noises due to the application of various image acquisition techniques, operator performance, and types of equipment. In this paper, we evaluate several fuzzy logic based denoising filters. A combined approach of fuzzy logic and a convolutional autoencoder has been also used on a brain image dataset for the performance evaluation. The experimental results show that the combined approach performs better than other methods.
Feature detection is very important to image processing area. In this paper we compare and analyze some characteristics of image processing algorithms for corner and blob feature detection. We also analyze the simulation results through image matching process. We show that how these algorithms work and how fast they execute. The simulation results are shown for helping us to select an algorithm or several algorithms extracting corner and blob feature.
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