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In various studies, it has been demonstrated that combining the decisions of multiple classifiers can lead to better recognition results. Plurality voting is one of the most widely used combination strategies. In this paper, we both theoretically and experimentally analyze the performance of a plurality voting-based ensemble classifier. Theoretical expressions for system performance are derived as...
In this paper, we study impulsive fuzzy BAM neural networks. Criteria are obtained for exponential stability of globally exponential stability of periodic solution of time-varying delayed fuzzy neural networks with impulses.The criteria obtained in this paper is easily verifiable. It is believed that it is useful in design neural networks in practices.
We present a pattern recognition methodology based on stochastic logic. The technique implements a parallel comparison of input data from a set of sensors to various pre-stored categories. Smart pulse-based stochastic-logic blocks are constructed to provide an efficient architecture that is able to implement Bayesian techniques, thus providing a low-cost solution in terms of gate count and power dissipation...
This paper describes a new approach for response integration in ensemble neural networks using interval type-2 fuzzy logic. When using ensemble neural networks it is important to choose a good method of response integration to obtain a better identification in pattern recognition. In this paper a comparative analysis between interval type-2 fuzzy logic, type-1 fuzzy logic and the Sugeno integral,...
This paper will present the achievements of its authors related to the development of new cognitive information systems classes used in the tasks of automatic understanding of image data semantics. Such systems are a practical implementation of the paradigm for machine semantics understanding of selected image data types, with special regards to various classes of medical images. The development of...
In this paper, we address both recognition of true object classes and rejection of false (non-object) classes as occurs in many realistic pattern recognition problems. We modified our hierarchical binary-decision classifier to produce analog outputs at each node, with values proportional to the class conditional probabilities at that node. This yields a new soft-decision hierarchical system. The hierarchical...
During CAD development and any kind of design optimisation over years a huge amount of geometries accumulate in a design department. To organize and structure these designs with respect to reusability, a hierarchical set of components on different scalings is extracted by the designers. This hierarchy allows to compose designs from several parts and to adapt the composition to the current task. Nevertheless,...
Feature extraction is a key element of pattern recognition for myoelectric control. In this paper, recurrence plots and recurrence quantification analysis (RQA) are used as the feature extractor for surface EMG signals. For eight different hand motions, two-channel EMG signals are recorded. Ten individual RQA parameters are calculated for each channel of EMG signals. With different combinations of...
This paper introduces a neurochaotic information processor based upon perturbed Duffing equation. The proposed chaotic neural network has parameters to tune by which decision is made to behave either chaotically or periodically. The neurochaotic nonlinear network adopts the chaotic dynamics of so-called Duffing oscillator for the chaotic movement in the search space. It then uses the benefits of fast...
Acoustic Emission (AE) can be used to discriminate the different types of damage occurring in composite materials, because any AE signal contains useful information about the damage mechanisms. A major issue in the use of the AE technique is how to discriminate the AE signatures which are due to the different damage mechanisms. Conventional studies have focused on the analysis of different parameters...
Self organizing map (SOM) is a kind of artificial neural network with a competitive and unsupervised learning. This technique is commonly used to dataset clustering tasks and can be useful in patterns recognition problems. This paper presents an artificial neural network application to signals language recognition problem, where the image representation is given by bit signatures. The recognition...
This paper presents a novel segmentation algorithm for offline cursive handwriting recognition. An over-segmentation algorithm is introduced to dissect the words from handwritten text based on the pixel density between upper and lower baselines. Each segment from the over-segmentation is passed to a multiple expert-based validation process. First expert compares the total foreground pixel of the segmentation...
Though the olfactory model entitled KIII has been widely used to pattern recognition, it only can give bare prediction. Combining EM model with the transductive confidence machine, a novel method to recognize hypoxia electroencephalogram (EEG) with a preset confidence level is proposed in this paper. This method can make prediction with confidence measure rather than bare prediction. The experimental...
In this paper , we propose a new statistical learning algorithm. This study quantitatively verifies the effectiveness of its feature extraction performance for face information processing. Simple-FLDA is an algorithm based on a geometrical analysis of the Fisher linear discriminant analysis. As a high-speed feature extraction method, the present algorithm in this paper is the improved version of Simple-FLDA...
In this paper, we propose a new computer aided diagnosis method of pulmonary nodules in X-ray CT images to reduce false positive (FP) rate under high true positive (TP) rate conditions. An essential core of the method is to extract and combine two novel and effective features from the raw CT images: One is orientation features of nodules in a region of interest (ROI) extracted by a Gabor filter, while...
Model field theory (MFT) is a powerful tool of pattern recognition, which has been used successfully for various tasks involving noisy data and high level of clutter. Detection of spatio-temporal activity patterns in EEG experiments is a very challenging task and it is well-suited for MFT implementation. Previous work on applying MFT for EEG analysis used Gaussian assumption on the mixture components...
Sensing data fusion has various types of real world applications in fields of weather forecasting, environmental surveillance, medical diagnosis, information assurance, space exploration and national security. Image fusion acts as a primary approach of data fusion. For similar images, some unique patterns occur within each individual one. There are some typical image fusion techniques, either area...
Recurrent multilayer network structures and Hebbian learning are two of the research results on the brain that are widely accepted by neuroscientists. The former led to multilayer perceptrons (MLPs) and recurrent MLPs, and the latter to associative memories. This paper presents recurrent and/or multilayer networks of novel associative memories, each being a new functional model of the neuron with...
The growing hierarchal self-organizing map (GHSOM) is the most efficient model among the variants of SOM. It is used successfully in document clustering and in various pattern recognition applications effectively. The main constraint that limits the implementation of this model and all the other variants of SOM models is that they work only with vector space model (VSM). In this paper, we extend the...
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