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In this paper we present and validate a methodology to avoid the training procedure of a classifier based on an Hidden Markov Model (HMM) for a real-time gait recognition of two or four phases, implemented to control pediatric active orthoses of lower limb. The new methodology consists in the identification of a set of standardized parameters, obtained by a data set of angular velocities of healthy...
In this study, the Multifeedback-Layer Neural Network (MFLNN) weights are trained by the Particle Swarm Optimization (PSO). This method (MFLNN-PSO) is applied to two different problems to prove accomplishment of the study. Firstly, a chaotic time series prediction problem is used to test the MFLNN-PSO. Also, the method is used for identification of a non-linear dynamic system. This study shows that...
For classification problems, the generalized eigenvalue proximal support vector machine (GEPSVM) and twin support vector machine (TWSVM) are regarded as milestones in the development of the powerful SVMs, as they use the nonparallel hyperplane classifiers. In this brief, we propose an improved version, named twin bounded support vector machines (TBSVM), based on TWSVM. The significant advantage of...
The model of spike neuron, structure of spike neural networks are considered and their classification was made. The training procedures used to spike neural networks are described.
This paper proposes a new method to detect objects in images. Boundaries contain shape of the objects. To detect objects in cluttered images, we use boundary fragments. Boundary fragments are obtained by our new training procedure. Poisson equation is used to divide edges and extract generic model of the object. Gaussian Mixture Model (GMM) is used to model shape of the object. This creates relation...
The performance of Evolutionary Programming (EP) is affected by many factors (e.g. mutation operators and selection strategies). Although the conventional approach with Gaussian mutation operator may be efficient, the initial scale of the whole population can be very large. This may lead to the conventional EP taking too long to reach convergence. To combat this problem, EP has been modified in various...
In this paper, a novel approach for insulation condition assessment is presented. This method is based on processing the detected partial discharge pulses by using a decision tree algorithm. In training procedure, not only partial discharge parameters are collected for different cavity sizes and depths, but also the influence of aging in cable insulation is considered. Results have shown high reliable...
An adaptive neural network control of a novel type of translational meshing motor with model uncertainties is considered. Owing to its nonlinear characteristic, a model reference control system which consists of two neural networks is used. The torque model is identified based on BP neural network, and then a RBF neural network works as the controller. The model reference control system is trained...
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