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Using the modern elements in a radio-electronic equipment requires close attention to processes as in branch of design and production of electronic components to use the last development in the field simulation of components and in the field computer-aided design of an equipment on the basis of radio-electronic components for the purpose of support adequacy, accuracy and convergence. Possibilities...
This article contributes a novel modified fractional order LMS (MFOLMS) algorithm with a variable gradient order scale. With the aid of fractional order calculus, a modified fractional order gradient descent (FOGD) method is first proposed. Hence, a MFOLMS algorithm is developed, which significantly improves the estimation accuracy of the existing approaches while slightly sacrifices the convergence...
In speech recognition system, an improved multi-base neural network speech recognition model is proposed to solve the problem of long learning time and slow convergence rate of deep neural network. However, the improved model introduces a large number of parameters in the training process to make the model over-fitted in the test set, resulting in the deterioration of generalization ability and the...
It is well-known that the precision of data, weight vector, and internal representations employed in learning systems directly impacts their energy, throughput, and latency. The precision requirements for the training algorithm are also important for systems that learn on-the-fly. In this paper, we present analytical lower bounds on the precision requirements for the commonly employed stochastic gradient...
FCM is sensitive to initialization and tends to result in local minimum in iterations. This paper studies the crossover and mutation probability of genetic algorithm and presents a new crossover and mutation probability. The proposed clustering scheme based on genetic algorithm and fuzzy c-means takes full advantage of the global optimization of genetic algorithm and the local search ability of FCM...
In this paper, a novel stochastic approximation technique is presented as a low-complexity alternative to conventional least squares-based digital predistortion model extraction solutions. The proposed technique is based on the simultaneous perturbation stochastic approximation (SPSA) algorithm. It avoids the hardware-intensive matrix operations associated with least squares by using an iterative...
With reducing feature sizes, there is a growing need for soft errors to be handled at the software level. This paper focuses on iterative scientific applications, particularly, solvers of PDEs. After empirically studying the impact of bit flips on convergence and correctness of these applications as well as analyzing the underlying numerical algorithm, we propose the following method for improving...
We introduce a new numerical method for the computation of the inverse nonlinear Fourier transform and compare its computational complexity and accuracy to those of other methods available in the literature. For a given accuracy, the proposed method requires the lowest number of operations.
The purpose of this article is an analysis of periodic disturbance compensator parameters, which ensure a stability of compensation process. The stability of periodic disturbance compensation is given by dynamic feature of control system and spectral features of periodic disturbance. As a result of analysis is to set limits of stability fields at specific conditions of control system dynamic function...
The problem of electromagnetic (EM) wave scattering on the small particle is reduced to solving the Fredholm integral equation of the second kind. Integral representation of solution to the scattering problem implies in determination of some unknown function which contains in integrand of this equation. The respective linear algebraic system (LAS) for the components of this unknown vector function...
The recent renewed interest in diffraction gratings as mirrors for vertical-cavity surface-emitting lasers (VCSELs) stimulated the development of fast and accurate full-wave simulators. In fact, their modeling requires large sets of scattering analyses, which can be effectively carried out by means of mode-matching techniques. In this framework, a crucial point is the numerical synthesis of the grating...
Network intrusion detection is the process of identifying malicious behaviors that target a network and its resources. Current systems implementing intrusion detection processes observe traffic at several data collecting points in the network but analysis is often centralized or partly centralized. These systems are not scalable and suffer from the single point of failure, i.e. attackers only need...
Enterprise in financial trouble is a comprehensive event and the enterprise financial situation can be reflected through the liquidity ratio, earnings per share and net assets per share and cash content per share. Artificial neural network method is used to establish the financial early warning model to find the potential financial crisis at an early age. The experiment results show that BP neural...
A constrained locally corrected Nyström (CLCN) method has recently been developed. The CLCN method enables the imposition of normal continuity on underlying vector quantities across mesh element boundaries. This is accomplished by deriving appropriate transformation vectors through simple algebraic analyses of local, homogeneous constraint conditions. Compared to the LCN method, the CLCN method improves...
The performance of a tracker can be measured by two often conflicting criteria - robustness and accuracy. Recently researchers have focused on improving robustness, using adaptive appearance models. However updating the appearance model can cause drift and lower the accuracy of motion (state) estimation. These trackers generally compute 2 degree of freedom(DOF) image translation of the object, and...
This paper shows an implementation of the Ψ and UML (Updated Maximum Likelihood) methods to incorporate unforced choice paradigms (nAUC) and simulation results for repeatability, efficiency and accuracy. Parametric methods like Ψ and UML promise higher accuracy and efficiency compared to classic and non-parametric methods and support fixed sets of stimuli. Unforced choice paradigms have shown similar...
A new method for orbit prediction, which is as accurate as numerical methods and as fast as analytical methods, in terms of computational time, is needed. Kolmogorov-Arnold-Moser (KAM) torus orbit prediction method is a modern orbit determination that can meet the aforementioned needs. This paper presents a differential correction technique to create parameters needed by the new theory and an approximate...
Applications that deal with time-series data often require evaluating complex statistics for which each time series is essentially one data point. When only a few time series are available, bootstrap methods are used to generate additional samples that can be used to evaluate empirically the statistic of interest. In this work a novel bootstrap method is proposed, which is shown to have some asymptotic...
Subspace segmentation is one of the hottest issues in computer vision and machine learning fields. Generally, data (e.g. face images) are lying in a union of multiple linear subspaces, therefore, it is the key to find a block diagonal affinity matrix, which would result in segmenting data into different clusters correctly. Recently, graph construction based segmentation methods attract lots of attention...
In nature, staying together is often of great selective advantage for social animals. Social animals frequently make consensus decisions, not least about group movements, in order to maintain group cohesion. Inspired by this social behavior, this paper proposes a new Particle Swarm Optimizer Based on Group Decision-Making (PSOGDM). Unlike the existing variants of PSO, historical information, such...
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