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This paper proposes a new kernel Kalman filter formulation for system identification and time series estimation in nonlinear time-varying environments. The unknown nonlinear time-varying function is approximated by a finite-order linear model in a reproducing kernel Hilbert space. The model coefficients define the state of the Kalman filter. Simulation results illustrate the improvement in estimation...
The problem of preference functions model development for multiple criteria decision-making is considered based on machine-learning approach. It is assumed that the training sample for a plurality of objects, for which decisions are made, is formed from a set of measured features or the particular criteria and the matrix of pairwise comparisons. The problem of constructing a linear preference function...
As seen in many studies the relationship of object oriented matrices of the software and the calculated maintenance effort metric is very complicated, complex and nonlinear in nature. So with this kind of behavior, we can have got a research area where we can work upon to minimize the maintenance effort which can be used to develop and deploy models and systems for the forecasting of software maintenance...
In this paper we present the comparative study of different bifurcation analysis techniques for nonlinear dynamical systems research. The study is given through a comparison of algorithms for multiparametric diagram plotting. Thomas chaotic attractor is considered as a test dynamical system. The paper reviews statistical histogram algorithm, the kernel density estimation (KDE) algorithm and classical...
This study presents a novel apprenticeship learning method to enable a learner to utilize demonstrations observed in an incompatible feature space. It is assumed that an expert and a learner follow non-identical Markov decision processes (MDPs), and a mapping function is estimated to obtain feature expectation of the demonstrations in an agent space. A conditional density estimation technique is used...
This article proposed a novel method to determine the number of principal components and the optimal values of tuning factors for kernel principal component models. Existing work predominantly relies on ad-hoc rules or cross-validatory approaches to estimate. To guarantee statistical independence, the proposed technique incorporates a two-fold cross-validatory approach by omitting one variable in...
Ocean remote sensing based satellite image is useful for the Earth observation such as altimetry, Significant Wave Height, and wind speed measurement. However, The Global Navigation Satellite System (GNSS) represents the new challenge using special feature of the reflected signal to observe characteristics of the ocean call GNSS - reflectometry. The advantages of this technique are that using the...
Mobile robot olfaction systems combine gas sensors with mobility provided by robots. They relief humans of dull, dirty and dangerous tasks in applications such as search & rescue or environmental monitoring. We address gas source localization and especially the problem of minimizing exploration time of the robot, which is a key issue due to energy constraints. We propose an active search approach...
We develop a linear parameter-varying (LPV) spectral decomposition method, based on least-squares estimation and kernel expansions. Statistical properties of the estimator are analyzed and verified in simulations. The method is linear in the parameters, applicable to both the analysis and modeling problems and is demonstrated on both simulated signals as well as measurements of the torque in an electrical...
We consider two close ways of linearization for sublinear operator that takes compact convex values. The first way consists in a representation of given multioperator by the family of so called basis selectors that are single-valued linear bounded operators. The second way consists in linear extension of given multioperator from its values on some Hamel basis. Every of the ways above leads to its...
In recent years, there has been a tremendous increase in the popularity of event-based social networks which allow social and physical interactions among their members. One major challenge for their members is the difficulty of searching events that meet their preferences from a large number of upcoming events. To tackle this challenge, we propose a personalized event recommendation framework called...
To overcome the sources of dynamic measurement error which are intricate and influence each other so that makes it difficult to model by parameter identification method in coordinate measuring machine's dynamic measurement error model. This article use kernel estimation to analyze the complicated relationship between dynamic measurement error values and three dimensional coordinates and direct computer...
This paper firstly analyzes the shortcoming of a self-organizing incremental neural network (SOINN), then proposes a novel online similarity metric and online adaptive kernel density estimator to handle 2 basic problems of unsupervised learning: clustering and density estimation. Our approach is an extension of the standard Gaussian process, online density estimator and SOINN; not only does it fully...
The paper offers an analysis of the distribution of the values of SAIDI and SAIFI indices in nineteen Polish companies dealing with electricity distribution. The data presented concern the assessment of the reliability of electricity supply to over 10.5 million consumers. The paper analyses the influence of the number of remotely controlled switches installed within a MV network on the values of SAIDI...
This paper introduces a novel framework for the study of adaptive or online estimation problems for a common class of nonlinear systems governed by ordinary differential equations (ODEs) on ℝd. In contrast to most conventional strategies for ODEs, the approach here embeds the estimate of the unknown nonlinear function appearing in the plant in a reproducing kernel Hilbert space (RKHS), H. The nonlinear...
This paper proposes methods to classify the plants using images taken from agricultural lands. Wheat, maize and lentil images are used. Texture features of agricultural land images are obtained using Gray Level Co-occurrence Matrix (GLCM) and Laws' Texture Energy Measures which are two of texture analysis methods. The texture features vectors which are generated with these two methods are classified...
Super-resolution reconstruction algorithms have been extensively studied for the last years. However, despite the progress made in this field, many issues remain to be solved. Some of them are basically omitted and their importance is trivialized. Routinely, for instance, researchers are willing to make the relative motion model simpler than it should be considered. The commonly applied non-rigid...
Creating an efficient localization algorithm for Internet of Things (IoT) and heterogeneous networks has been of the greatest challenges in the recent years for the researchers and industries. There has been significant efforts to increase the estimation accuracy and computational speed of localization algorithms either by improving the already existing ones or proposing new solutions. In this paper...
Performance and power consumption are key features for evaluating any processor design. In this paper, we present close attention to the impact on power and energy consumption of customized Instruction SetArchitecture (ISA) designed by means of High Level Synthesis (HLS) tools. We compare these results against a full ISA soft processor, Microblaze. Our customized ISA processors greatly reduce the...
Multivariate volumetric datasets are often encountered in results generated by scientific simulations. Compared to univariate datasets, analysis and visualization of multivariate datasets are much more challenging due to the complex relationships among the variables. As an effective way to visualize and analyze multivariate datasets, volume rendering has been frequently used, although designing good...
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