A novel bit-quad-based Euler number computing algorithm.
Yao, Bin; He, Lifeng; Kang, Shiying; Chao, Yuyan; Zhao, Xiao
2015-01-01
The Euler number of a binary image is an important topological property in computer vision and pattern recognition. This paper proposes a novel bit-quad-based Euler number computing algorithm. Based on graph theory and analysis on bit-quad patterns, our algorithm only needs to count two bit-quad patterns. Moreover, by use of the information obtained during processing the previous bit-quad, the average number of pixels to be checked for processing a bit-quad is only 1.75. Experimental results demonstrated that our method outperforms significantly conventional Euler number computing algorithms.
A novel bit-quad-based Euler number computing algorithm
Yao, Bin; He, Lifeng; Kang, Shiying; Chao, Yuyan; Xiao ZHAO
2015-01-01
The Euler number of a binary image is an important topological property in computer vision and pattern recognition. This paper proposes a novel bit-quad-based Euler number computing algorithm. Based on graph theory and analysis on bit-quad patterns, our algorithm only needs to count two bit-quad patterns. Moreover, by use of the information obtained during processing the previous bit-quad, the average number of pixels to be checked for processing a bit-quad is only 1.75. Experimental results ...
Control theory based airfoil design using the Euler equations
Jameson, Antony; Reuther, James
1994-01-01
This paper describes the implementation of optimization techniques based on control theory for airfoil design. In our previous work it was shown that control theory could be employed to devise effective optimization procedures for two-dimensional profiles by using the potential flow equation with either a conformal mapping or a general coordinate system. The goal of our present work is to extend the development to treat the Euler equations in two-dimensions by procedures that can readily be generalized to treat complex shapes in three-dimensions. Therefore, we have developed methods which can address airfoil design through either an analytic mapping or an arbitrary grid perturbation method applied to a finite volume discretization of the Euler equations. Here the control law serves to provide computationally inexpensive gradient information to a standard numerical optimization method. Results are presented for both the inverse problem and drag minimization problem.
Optimum Transonic Airfoils Based on the Euler Equations
Iollo, Angelo; Salas, Manuel, D.
1996-01-01
We solve the problem of determining airfoils that approximate, in a least square sense, given surface pressure distributions in transonic flight regimes. The flow is modeled by means of the Euler equations and the solution procedure is an adjoint- based minimization algorithm that makes use of the inverse Theodorsen transform in order to parameterize the airfoil. Fast convergence to the optimal solution is obtained by means of the pseudo-time method. Results are obtained using three different pressure distributions for several free stream conditions. The airfoils obtained have given a trailing edge angle.
A review of propeller modelling techniques based on Euler methods
Zondervan, G.J.D.
1998-01-01
Future generation civil aircraft will be powered by new, highly efficient propeller propulsion systems. New, advanced design tools like Euler methods will be needed in the design process of these aircraft. This report describes the application of Euler methods to the modelling of flowfields generate
Aeroelastic Calculations Based on Three-Dimensional Euler Analysis
Bakhle, Milind A.; Srivastava, Rakesh; Keith, Theo G., Jr.; Stefko, George L.
1998-01-01
This paper presents representative results from an aeroelastic code (TURBO-AE) based on an Euler/Navier-Stokes unsteady aerodynamic code (TURBO). Unsteady pressure, lift, and moment distributions are presented for a helical fan test configuration which is used to verify the code by comparison to two-dimensional linear potential (flat plate) theory. The results are for pitching and plunging motions over a range of phase angles, Good agreement with linear theory is seen for all phase angles except those near acoustic resonances. The agreement is better for pitching motions than for plunging motions. The reason for this difference is not understood at present. Numerical checks have been performed to ensure that solutions are independent of time step, converged to periodicity, and linearly dependent on amplitude of blade motion. The paper concludes with an evaluation of the current state of development of the TURBO-AE code and presents some plans for further development and validation of the TURBO-AE code.
1994-01-01
dquations d’Euler) Edited by * S J.W. SLOOFF National Aerospace Laboratory NLR Anthony Fokkerweg 2 1059 CM Amsterdam Netherlands Dr. W. SCHMIDT Air Vehicle...CHAKRAVARTHY. S. R., RIBA , W.* * T.. BYERLY. J. and DRESSER. H. S.. "Multi-Zone Euler 41. DESLANDES R.M.. "Theoretisebe bestimmung der Marching Technique
2014-06-01
3D Euler Equations of Moist Atmospheric Convection 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER...STABILIZATION OF SPECTRAL ELEMENTS FOR THE 3D EULER EQUATIONS OF MOIST ATMOSPHERIC CONVECTION SIMONE MARRAS, ANDREAS MÜLLER, FRANCIS X. GIRALDO Dept. Appl...spectral elements, we introduce a dissipative scheme based on the solution of the compressible Euler equations that are regularized through the addi
Some generalized Lagrange-based Apostol-Bernoulli, Apostol-Euler and Apostol-Genocchi polynomials
Srivastava, H. M.; Özarslan, M. A.; Kaanoğlu, C.
2013-03-01
In this paper, we introduce a general family of Lagrange-based Apostol-type polynomials thereby unifying the Lagrange-based Apostol-Bernoulli and the Lagrange-based Apostol-Genocchi polynomials. We also define Lagrange-based Apostol-Euler polynomials via the generating function. In terms of these generalizations, we find new and useful relations between the unified family and the Apostol-Euler polynomials. We also derive their explicit representations and list some basic properties of each of them. Further relations between the above-mentioned polynomials, including a family of bilinear and bilateral generating functions, are given. Moreover, a generating relation involving the Stirling numbers of the second kind is derived.
Wang, Xiao-Yen; Chow, Chuen-Yen; Chang, Sin-Chung
1996-01-01
The I-D, quasi I-D and 2-D Euler solvers based on the method of space-time conservation element and solution element are used to simulate various flow phenomena including shock waves, Mach stem, contact surface, expansion waves, and their intersections and reflections. Seven test problems are solved to demonstrate the capability of this method for handling unsteady compressible flows in various configurations. Numerical results so obtained are compared with exact solutions and/or numerical solutions obtained by schemes based on other established computational techniques. Comparisons show that the present Euler solvers can generate highly accurate numerical solutions to complex flow problems in a straightforward manner without using any ad hoc techniques in the scheme.
Adali, Sarp
2009-05-01
Variational principles are derived for multiwalled carbon nanotubes undergoing vibrations. Derivations are based on the continuum modeling with the Euler-Bernoulli beam representing the nanotubes and small scale effects taken into account via the nonlocal elastic theory. Hamilton's principle for multiwalled nanotubes is given and Rayleigh's quotient for the frequencies is derived for nanotubes undergoing free vibrations. Natural and geometric boundary conditions are derived which lead to a set of coupled boundary conditions due to nonlocal effects.
Reddy, T. S. R.; Srivastava, R.; Mehmed, Oral
2002-01-01
An aeroelastic analysis system for flutter and forced response analysis of turbomachines based on a two-dimensional linearized unsteady Euler solver has been developed. The ASTROP2 code, an aeroelastic stability analysis program for turbomachinery, was used as a basis for this development. The ASTROP2 code uses strip theory to couple a two dimensional aerodynamic model with a three dimensional structural model. The code was modified to include forced response capability. The formulation was also modified to include aeroelastic analysis with mistuning. A linearized unsteady Euler solver, LINFLX2D is added to model the unsteady aerodynamics in ASTROP2. By calculating the unsteady aerodynamic loads using LINFLX2D, it is possible to include the effects of transonic flow on flutter and forced response in the analysis. The stability is inferred from an eigenvalue analysis. The revised code, ASTROP2-LE for ASTROP2 code using Linearized Euler aerodynamics, is validated by comparing the predictions with those obtained using linear unsteady aerodynamic solutions.
LINFLUX-AE: A Turbomachinery Aeroelastic Code Based on a 3-D Linearized Euler Solver
Reddy, T. S. R.; Bakhle, M. A.; Trudell, J. J.; Mehmed, O.; Stefko, G. L.
2004-01-01
This report describes the development and validation of LINFLUX-AE, a turbomachinery aeroelastic code based on the linearized unsteady 3-D Euler solver, LINFLUX. A helical fan with flat plate geometry is selected as the test case for numerical validation. The steady solution required by LINFLUX is obtained from the nonlinear Euler/Navier Stokes solver TURBO-AE. The report briefly describes the salient features of LINFLUX and the details of the aeroelastic extension. The aeroelastic formulation is based on a modal approach. An eigenvalue formulation is used for flutter analysis. The unsteady aerodynamic forces required for flutter are obtained by running LINFLUX for each mode, interblade phase angle and frequency of interest. The unsteady aerodynamic forces for forced response analysis are obtained from LINFLUX for the prescribed excitation, interblade phase angle, and frequency. The forced response amplitude is calculated from the modal summation of the generalized displacements. The unsteady pressures, work done per cycle, eigenvalues and forced response amplitudes obtained from LINFLUX are compared with those obtained from LINSUB, TURBO-AE, ASTROP2, and ANSYS.
A proof of image Euler Number formula
Institute of Scientific and Technical Information of China (English)
LIN Xiaozhu; SHA Yun; JI Junwei; WANG Yanmin
2006-01-01
Euler Number is one of the most important characteristics in topology. In two- dimension digital images, the Euler characteristic is locally computable. The form of Euler Number formula is different under 4-connected and 8-connected conditions. Based on the definition of the Foreground Segment and Neighbor Number, a formula of the Euler Number computing is proposed and is proved in this paper. It is a new idea to locally compute Euler Number of 2D image.
Adaptively Refined Euler and Navier-Stokes Solutions with a Cartesian-Cell Based Scheme
Coirier, William J.; Powell, Kenneth G.
1995-01-01
A Cartesian-cell based scheme with adaptive mesh refinement for solving the Euler and Navier-Stokes equations in two dimensions has been developed and tested. Grids about geometrically complicated bodies were generated automatically, by recursive subdivision of a single Cartesian cell encompassing the entire flow domain. Where the resulting cells intersect bodies, N-sided 'cut' cells were created using polygon-clipping algorithms. The grid was stored in a binary-tree data structure which provided a natural means of obtaining cell-to-cell connectivity and of carrying out solution-adaptive mesh refinement. The Euler and Navier-Stokes equations were solved on the resulting grids using an upwind, finite-volume formulation. The inviscid fluxes were found in an upwinded manner using a linear reconstruction of the cell primitives, providing the input states to an approximate Riemann solver. The viscous fluxes were formed using a Green-Gauss type of reconstruction upon a co-volume surrounding the cell interface. Data at the vertices of this co-volume were found in a linearly K-exact manner, which ensured linear K-exactness of the gradients. Adaptively-refined solutions for the inviscid flow about a four-element airfoil (test case 3) were compared to theory. Laminar, adaptively-refined solutions were compared to accepted computational, experimental and theoretical results.
Detecting and tracking eddies in oceanic flows: A vorticity based Euler-Lagrangian method
Vortmeyer-Kley, Rahel; Gräwe, Ulf; Feudel, Ulrike
2016-04-01
Algae blooms as recurrent events in the Baltic Sea are an increasing natural hazard. Sandulescu et al. show in numerical simulation in [1] that eddies can play the role of an incubator for an algae bloom. Inside the eddy nutrients and plankton are trapped and can then be transported across rather long distances. To gain insight in mechanisms of algae bloom evolution detection and tracking of eddies is of interest. Based on the idea to interpret an eddy as a region that is bounded by manifolds and has an elliptic fixed point inside them, we develop an Euler-Lagrangian eddytracking tool using the idea of Lagrangian descriptors [2] and the vorticity. To test how well the tool detects eddy tracks and shapes, and estimates eddy lifetimes, the method is applied to a synthetic van Karman-Vortex Street. The results are compared to an eddytracking tool by Nencioli et al. [3]. Even velocity fields incorporated with different types of noise are taken into account to test the robustness of the tool. Finally, both methods are applied to velocity fields of the Baltic Sea. [1] M. Sandulescu, C. Lopez, E. Hernandez-Garcia and U. Feudel, Nonlinear Proc. Geophys., 14, 443-454, (2007). [2] J. Jimenez-Madrid and A. Mancho, Chaos, 19, 013111-1-18, (2009). [3] F. Nencioli, C. Dong, T. Dickey, L. Washburn, and J.C. McWilliams, J. Atmos. Ocean Tech., 27, 564-579, (2010).
Stötzel, Claudia; Röblitz, Susanna; Siebert, Heike
2015-01-01
In this paper, we present a systematic transition scheme for a large class of ordinary differential equations (ODEs) into Boolean networks. Our transition scheme can be applied to any system of ODEs whose right hand sides can be written as sums and products of monotone functions. It performs an Euler-like step which uses the signs of the right hand sides to obtain the Boolean update functions for every variable of the corresponding discrete model. The discrete model can, on one hand, be considered as another representation of the biological system or, alternatively, it can be used to further the analysis of the original ODE model. Since the generic transformation method does not guarantee any property conservation, a subsequent validation step is required. Depending on the purpose of the model this step can be based on experimental data or ODE simulations and characteristics. Analysis of the resulting Boolean model, both on its own and in comparison with the ODE model, then allows to investigate system properties not accessible in a purely continuous setting. The method is exemplarily applied to a previously published model of the bovine estrous cycle, which leads to new insights regarding the regulation among the components, and also indicates strongly that the system is tailored to generate stable oscillations.
Directory of Open Access Journals (Sweden)
Claudia Stötzel
Full Text Available In this paper, we present a systematic transition scheme for a large class of ordinary differential equations (ODEs into Boolean networks. Our transition scheme can be applied to any system of ODEs whose right hand sides can be written as sums and products of monotone functions. It performs an Euler-like step which uses the signs of the right hand sides to obtain the Boolean update functions for every variable of the corresponding discrete model. The discrete model can, on one hand, be considered as another representation of the biological system or, alternatively, it can be used to further the analysis of the original ODE model. Since the generic transformation method does not guarantee any property conservation, a subsequent validation step is required. Depending on the purpose of the model this step can be based on experimental data or ODE simulations and characteristics. Analysis of the resulting Boolean model, both on its own and in comparison with the ODE model, then allows to investigate system properties not accessible in a purely continuous setting. The method is exemplarily applied to a previously published model of the bovine estrous cycle, which leads to new insights regarding the regulation among the components, and also indicates strongly that the system is tailored to generate stable oscillations.
Larios, Adam; Titi, Edriss S; Wingate, Beth
2015-01-01
We report the results of a computational investigation of two recently proved blow-up criteria for the 3D incompressible Euler equations. These criteria are based on an inviscid regularization of the Euler equations known as the 3D Euler-Voigt equations. The latter are known to be globally well-posed. Moreover, simulations of the 3D Euler-Voigt equations also require less resolution than simulations of the 3D Euler equations for fixed values of the regularization parameter $\\alpha>0$. Therefore, the new blow-up criteria allow one to gain information about possible singularity formation in the 3D Euler equations indirectly; namely, by simulating the better-behaved 3D Euler-Voigt equations. The new criteria are only known to be sufficient criteria for blow-up. Therefore, to test the robustness of the inviscid-regularization approach, we also investigate analogous criteria for blow-up of the 1D Burgers equation, where blow-up is well-known to occur.
Degenerate Euler zeta function
Kim, Taekyun
2015-01-01
Recently, T. Kim considered Euler zeta function which interpolates Euler polynomials at negative integer (see [3]). In this paper, we study degenerate Euler zeta function which is holomorphic function on complex s-plane associated with degenerate Euler polynomials at negative integers.
Institute of Scientific and Technical Information of China (English)
Guo Can-Can; Xiong Sheng-Qing; Xue Dian-Jun; Wang Lin-Fei
2014-01-01
We propose a new automatic method for the interpretation of potentialfi eld data, called the RDAS-Euler method, which is based on Euler’s deconvolution and analytic signal methods. The proposed method can estimate the horizontal and vertical extent of geophysical anomalies without prior information of the nature of the anomalies (structural index). It also avoids inversion errors because of the erroneous choice of the structural index N in the conventional Euler deconvolution method. The method was tested using model gravity anomalies. In all cases, the misfi t between theoretical values and inversion results is less than 10%. Relative to the conventional Euler deconvolution method, the RDAS-Euler method produces inversion results that are more stable and accurate. Finally, we demonstrate the practicability of the method by applying it to Hulin Basin in Heilongjiang province, where the proposed method produced more accurate data regarding the distribution of faults.
Neural dynamics based on the recognition of neural fingerprints
Directory of Open Access Journals (Sweden)
José Luis eCarrillo-Medina
2015-03-01
Full Text Available Experimental evidence has revealed the existence of characteristic spiking features in different neural signals, e.g. individual neural signatures identifying the emitter or functional signatures characterizing specific tasks. These neural fingerprints may play a critical role in neural information processing, since they allow receptors to discriminate or contextualize incoming stimuli. This could be a powerful strategy for neural systems that greatly enhances the encoding and processing capacity of these networks. Nevertheless, the study of information processing based on the identification of specific neural fingerprints has attracted little attention. In this work, we study (i the emerging collective dynamics of a network of neurons that communicate with each other by exchange of neural fingerprints and (ii the influence of the network topology on the self-organizing properties within the network. Complex collective dynamics emerge in the network in the presence of stimuli. Predefined inputs, i.e. specific neural fingerprints, are detected and encoded into coexisting patterns of activity that propagate throughout the network with different spatial organization. The patterns evoked by a stimulus can survive after the stimulation is over, which provides memory mechanisms to the network. The results presented in this paper suggest that neural information processing based on neural fingerprints can be a plausible, flexible and powerful strategy.
Neural Network based Consumption Forecasting
DEFF Research Database (Denmark)
Madsen, Per Printz
2016-01-01
This paper describe a Neural Network based method for consumption forecasting. This work has been financed by the The ENCOURAGE project. The aims of The ENCOURAGE project is to develop embedded intelligence and integration technologies that will directly optimize energy use in buildings and enable...
Directory of Open Access Journals (Sweden)
Song Fu
2015-01-01
Full Text Available Although the uniform theory of diffraction (UTD could be theoretically applied to arbitrarilyshaped convex objects modeled by nonuniform rational B-splines (NURBS, one of the great challenges in calculation of the UTD surface diffracted fields is the difficulty in determining the geodesic paths along which the creeping waves propagate on arbitrarilyshaped NURBS surfaces. In differential geometry, geodesic paths satisfy geodesic differential equation (GDE. Hence, in this paper, a general and efficient adaptive variable step Euler method is introduced for solving the GDE on arbitrarilyshaped NURBS surfaces. In contrast with conventional Euler method, the proposed method employs a shape factor (SF ξ to efficiently enhance the accuracy of tracing and extends the application of UTD for practical engineering. The validity and usefulness of the algorithm can be verified by the numerical results.
Wing aeroelasticity analysis based on an integral boundary-layer method coupled with Euler solver
Institute of Scientific and Technical Information of China (English)
Ma Yanfeng; He Erming; Zeng Xianang; Li Junjie
2016-01-01
An interactive boundary-layer method, which solves the unsteady flow, is developed for aeroelastic computation in the time domain. The coupled method combines the Euler solver with the integral boundary-layer solver (Euler/BL) in a ‘‘semi-inverse” manner to compute flows with the inviscid and viscous interaction. Unsteady boundary conditions on moving surfaces are taken into account by utilizing the approximate small-perturbation method without moving the compu-tational grids. The steady and unsteady flow calculations for the LANN wing are presented. The wing tip displacement of high Reynolds number aero-structural dynamics (HIRENASD) Project is simulated under different angles of attack. The flutter-boundary predictions for the AGARD 445.6 wing are provided. The results of the interactive boundary-layer method are compared with those of the Euler method and experimental data. The study shows that viscous effects are signif-icant for these cases and the further data analysis confirms the validity and practicability of the cou-pled method.
Wing aeroelasticity analysis based on an integral boundary-layer method coupled with Euler solver
Directory of Open Access Journals (Sweden)
Ma Yanfeng
2016-10-01
Full Text Available An interactive boundary-layer method, which solves the unsteady flow, is developed for aeroelastic computation in the time domain. The coupled method combines the Euler solver with the integral boundary-layer solver (Euler/BL in a “semi-inverse” manner to compute flows with the inviscid and viscous interaction. Unsteady boundary conditions on moving surfaces are taken into account by utilizing the approximate small-perturbation method without moving the computational grids. The steady and unsteady flow calculations for the LANN wing are presented. The wing tip displacement of high Reynolds number aero-structural dynamics (HIRENASD Project is simulated under different angles of attack. The flutter-boundary predictions for the AGARD 445.6 wing are provided. The results of the interactive boundary-layer method are compared with those of the Euler method and experimental data. The study shows that viscous effects are significant for these cases and the further data analysis confirms the validity and practicability of the coupled method.
Generalizations of Euler Numbers and Euler Numbers of Higher Order
Institute of Scientific and Technical Information of China (English)
LUOQiu-ming; QIFeng
2005-01-01
The purpose of this paper is to define the generalized Euler numbers and the generalized Euler numbers of higher order, their recursion formula and some properties were established, accordingly Euler numbers and Euler numbers of higher order were extended.
Taylor, Jerry D.
1983-01-01
Provides a brief, condensed biography of the eighteenth-century mathematician, Leonard Euler, focusing on some of his contributions to mathematics. Also presents several problems and suggests how Euler might have solved them. (JN)
Competition Based Neural Networks for Assignment Problems
Institute of Scientific and Technical Information of China (English)
李涛; LuyuanFang
1991-01-01
Competition based neural networks have been used to solve the generalized assignment problem and the quadratic assignment problem.Both problems are very difficult and are ε approximation complete.The neural network approach has yielded highly competitive performance and good performance for the quadratic assignment problem.These neural networks are guaranteed to produce feasible solutions.
Suisky, Dieter
2008-01-01
"Euler as Physicist" analyzes the exceptional role of Leonhard Euler (1707 - 1783) in the history of science and emphasizes especially his fundamental contributions to physics. Although Euler is famous as the leading mathematician of the 18th century, his contributions to physics are as important for their innovative methods and solutions. Several books are devoted to Euler as mathematician, but none to Euler as physicist, like in this book. Euler’s contributions to mechanics are rooted in his life-long plan presented in two volume treatise programmatically entitled "Mechanics or the science of motion analytically demonstrated". Published in 1736, Euler’s treatise indicates the turn over from the traditional geometric representation of mechanics to a new approach. In writing Mechanics Euler did the first step to put the plan and his completion into practice through 1760. It is of particular interest to study how Euler made immediate use of his mathematics for mechanics and coordinated his progress in math...
Institute of Scientific and Technical Information of China (English)
刘鹏; 刘红军; 林坤; 秦荣
2016-01-01
基于 Bernoulli-Euler 梁理论，采用样条有限点法建立考虑截面高宽度沿轴线性变化的变截面 Euler 梁振动分析的计算模型，通过沿梁轴线设置一定数量的样条节点对变截面梁样条离散化，采用三次 B 样条函数对梁的位移场进行插值，基于 Hamilton 原理导出变截面 Euler 梁的振动方程，推导考虑截面尺寸变化效应的总刚度和总质量矩阵的表达式，并编制计算程序，算例分析表明，模型的变截面梁的横向自振频率解答与文献解答吻合良好，计算精度和计算效率高，且模型边界处理简单，取样条离散节点数为15时，模型可以取得较高精度且解答趋于稳定。模型可适用于不同边界、不同截面变化率和不同截面类型的变截面 Euler 梁的自由振动分析。%Based on Bernoulli-Euler beam theory,a new model was presented here to study free transverse vibration problems of tapered Euler beams by using the spline finite point method (SFPM)considering both width and height of beams'cross section linearly varying along the axial direction.With the proposed method,a beam was discretized by a set of uniformly scattered spline nodes along the axis direction instead of meshes,and the cubic-B spline interpolation functions were utilized to approximate the displacement filed of the beam.The free vibration equation of the beam was derived base on Hamilton Principle,and the global stiffness and mass matrices for the tapered beam were deduced in detail.The results of examples showed that the solutions to natural frequencies of tapered beams based on the proposed method are good in agreement with those reported in literatures;the proposed method has a higher accuracy,a lower computational cost and an easier way for boundary treatment;the solutions with a higher accuracy can be achieved by selecting the spline node number of no less than 15;the presented model is suitable for the free transverse vibration of
Symmetric Euler orientation representations for orientational averaging.
Mayerhöfer, Thomas G
2005-09-01
A new kind of orientation representation called symmetric Euler orientation representation (SEOR) is presented. It is based on a combination of the conventional Euler orientation representations (Euler angles) and Hamilton's quaternions. The properties of the SEORs concerning orientational averaging are explored and compared to those of averaging schemes that are based on conventional Euler orientation representations. To that aim, the reflectance of a hypothetical polycrystalline material with orthorhombic crystal symmetry was calculated. The calculation was carried out according to the average refractive index theory (ARIT [T.G. Mayerhöfer, Appl. Spectrosc. 56 (2002) 1194]). It is shown that the use of averaging schemes based on conventional Euler orientation representations leads to a dependence of the result from the specific Euler orientation representation that was utilized and from the initial position of the crystal. The latter problem can be overcome partly by the introduction of a weighing factor, but only for two-axes-type Euler orientation representations. In case of a numerical evaluation of the average, a residual difference remains also if a two-axes type Euler orientation representation is used despite of the utilization of a weighing factor. In contrast, this problem does not occur if a symmetric Euler orientation representation is used as a matter of principle, while the result of the averaging for both types of orientation representations converges with increasing number of orientations considered in the numerical evaluation. Additionally, the use of a weighing factor and/or non-equally spaced steps in the numerical evaluation of the average is not necessary. The symmetrical Euler orientation representations are therefore ideally suited for the use in orientational averaging procedures.
VennDIS: a JavaFX-based Venn and Euler diagram software to generate publication quality figures.
Ignatchenko, Vladimir; Ignatchenko, Alexandr; Sinha, Ankit; Boutros, Paul C; Kislinger, Thomas
2015-04-01
Venn diagrams are graphical representations of the relationships among multiple sets of objects and are often used to illustrate similarities and differences among genomic and proteomic datasets. All currently existing tools for producing Venn diagrams evince one of two traits; they require expertise in specific statistical software packages (such as R), or lack the flexibility required to produce publication-quality figures. We describe a simple tool that addresses both shortcomings, Venn Diagram Interactive Software (VennDIS), a JavaFX-based solution for producing highly customizable, publication-quality Venn, and Euler diagrams of up to five sets. The strengths of VennDIS are its simple graphical user interface and its large array of customization options, including the ability to modify attributes such as font, style and position of the labels, background color, size of the circle/ellipse, and outline color. It is platform independent and provides real-time visualization of figure modifications. The created figures can be saved as XML files for future modification or exported as high-resolution images for direct use in publications.
Inductively generating Euler diagrams.
Stapleton, Gem; Rodgers, Peter; Howse, John; Zhang, Leishi
2011-01-01
Euler diagrams have a wide variety of uses, from information visualization to logical reasoning. In all of their application areas, the ability to automatically layout Euler diagrams brings considerable benefits. In this paper, we present a novel approach to Euler diagram generation. We develop certain graphs associated with Euler diagrams in order to allow curves to be added by finding cycles in these graphs. This permits us to build Euler diagrams inductively, adding one curve at a time. Our technique is adaptable, allowing the easy specification, and enforcement, of sets of well-formedness conditions; we present a series of results that identify properties of cycles that correspond to the well-formedness conditions. This improves upon other contributions toward the automated generation of Euler diagrams which implicitly assume some fixed set of well-formedness conditions must hold. In addition, unlike most of these other generation methods, our technique allows any abstract description to be drawn as an Euler diagram. To establish the utility of the approach, a prototype implementation has been developed.
Rubillo, James M.
1987-01-01
Euler's discovery about the centroid of a triangle trisecting the line segment joining its circumference to its orthocenter is discussed. An activity that will help students review fundamental concepts is included. (MNS)
Neural Network Based 3D Surface Reconstruction
Directory of Open Access Journals (Sweden)
Vincy Joseph
2009-11-01
Full Text Available This paper proposes a novel neural-network-based adaptive hybrid-reflectance three-dimensional (3-D surface reconstruction model. The neural network combines the diffuse and specular components into a hybrid model. The proposed model considers the characteristics of each point and the variant albedo to prevent the reconstructed surface from being distorted. The neural network inputs are the pixel values of the two-dimensional images to be reconstructed. The normal vectors of the surface can then be obtained from the output of the neural network after supervised learning, where the illuminant direction does not have to be known in advance. Finally, the obtained normal vectors can be applied to integration method when reconstructing 3-D objects. Facial images were used for training in the proposed approach
EULER - A Real Virtual Library for Mathematics
Jost, Michael
2004-01-01
The EULER project completed its work in November 2002. It forms the last part of a very successful project in the specialized but global discipline of mathematics. After a successful RTD project had created the technology, a take-up project has effectively exploited it to the point where its future is assured through a not-for-profit consortium. EULER is a European based, world class, real virtual library for mathematics with up-to-date technological solutions, well accepted by users. In particular, EULER provides a world reference and delivery service, transparent to the end user and offering full coverage of the mathematics literature world-wide, including bibliographic data, peer reviews and/or abstracts, indexing, classification and search, transparent access to library services, co-operation with commercial information providers (publishers, bookstores). The EULER services provide a gateway to the electronic catalogues and repositories of participating institutions, while the latter retain complete respo...
SAR ATR Based on Convolutional Neural Network
Directory of Open Access Journals (Sweden)
Tian Zhuangzhuang
2016-06-01
Full Text Available This study presents a new method of Synthetic Aperture Radar (SAR image target recognition based on a convolutional neural network. First, we introduce a class separability measure into the cost function to improve this network’s ability to distinguish between categories. Then, we extract SAR image features using the improved convolutional neural network and classify these features using a support vector machine. Experimental results using moving and stationary target acquisition and recognition SAR datasets prove the validity of this method.
Yang, Zi-Jiang; Qin, Pan
2016-07-01
This paper considers the problem of distributed synchronisation tracking control of multiple Euler-Lagrange systems on a directed graph which contains a spanning tree with the leader node being the root. To design the high performance distributed controllers, a virtual double-integrator is introduced in each agent and is controlled by a virtual distributed linear high-gain synchronisation tracking controller, so that the position and velocity of each agent track those of the reference trajectory with arbitrarily short transient time and small ultimate tracking error. Then taking the double-integrator's position and velocity as the estimates of those of the reference trajectory, in each generalised coordinate of each Euler-Lagrange agent, a local controller with a disturbance observer and a sliding mode control term is designed, to suppress the mutual interactions among the agents and the modelling uncertainties. The boundedness of the overall signals and the synchronisation tracking control performance are analysed, and the conditions for guaranteed control performance are clarified. Simulation examples are provided to demonstrate the performance of the distributed controllers.
Dynamic Object Identification with SOM-based neural networks
Directory of Open Access Journals (Sweden)
Aleksey Averkin
2014-03-01
Full Text Available In this article a number of neural networks based on self-organizing maps, that can be successfully used for dynamic object identification, is described. Unique SOM-based modular neural networks with vector quantized associative memory and recurrent self-organizing maps as modules are presented. The structured algorithms of learning and operation of such SOM-based neural networks are described in details, also some experimental results and comparison with some other neural networks are given.
Leonhard Euler's Wave Theory of Light
DEFF Research Database (Denmark)
Pedersen, Kurt Møller
2008-01-01
is wrong. Most of his mathematical arguments were, however, guesswork without any solid physical reasoning. Guesswork is not always a bad thing in physics if it leads to new experiments or makes the theory coherent with other theories. And Euler tried to find such experiments. He saw the construction......Euler's wave theory of light developed from a mere description of this notion based on an analogy between sound and light to a more and more mathematical elaboration on that notion. He was very successful in predicting the shape of achromatic lenses based on a new dispersion law that we now know...
Neural Network-Based Hyperspectral Algorithms
2016-06-07
Neural Network-Based Hyperspectral Algorithms Walter F. Smith, Jr. and Juanita Sandidge Naval Research Laboratory Code 7340, Bldg 1105 Stennis Space...our effort is development of robust numerical inversion algorithms , which will retrieve inherent optical properties of the water column as well as...validate the resulting inversion algorithms with in-situ data and provide estimates of the error bounds associated with the inversion algorithm . APPROACH
Van der Kallen, Wilberd
2015-01-01
Let R be a noetherian ring of dimension d and let n be an integer so that n≤d≤2n-3. Let (a
The Hamiltonian Canonical Form for Euler-Lagrange Equations
Institute of Scientific and Technical Information of China (English)
ZHENG Yu
2002-01-01
Based on the theory of calculus of variation, some suffcient conditions are given for some Euler-Lagrangcequations to be equivalently represented by finite or even infinite many Hamiltonian canonical equations. Meanwhile,some further applications for equations such as the KdV equation, MKdV equation, the general linear Euler Lagrangeequation and the cylindric shell equations are given.
A New Flux Splitting Scheme Based on Toro-Vazquez and HLL Schemes for the Euler Equations
Directory of Open Access Journals (Sweden)
Pascalin Tiam Kapen
2014-01-01
Full Text Available This paper presents a new flux splitting scheme for the Euler equations. The proposed scheme termed TV-HLL is obtained by following the Toro-Vazquez splitting (Toro and Vázquez-Cendón, 2012 and using the HLL algorithm with modified wave speeds for the pressure system. Here, the intercell velocity for the advection system is taken as the arithmetic mean. The resulting scheme is more accurate when compared to the Toro-Vazquez schemes and also enjoys the property of recognition of contact discontinuities and shear waves. Accuracy, efficiency, and other essential features of the proposed scheme are evaluated by analyzing shock propagation behaviours for both the steady and unsteady compressible flows. The accuracy of the scheme is shown in 1D test cases designed by Toro.
Espino, Daniel M; Shepherd, Duncan E T; Hukins, David W L
2014-01-01
A transient multi-physics model of the mitral heart valve has been developed, which allows simultaneous calculation of fluid flow and structural deformation. A recently developed contact method has been applied to enable simulation of systole (the stage when blood pressure is elevated within the heart to pump blood to the body). The geometry was simplified to represent the mitral valve within the heart walls in two dimensions. Only the mitral valve undergoes deformation. A moving arbitrary Lagrange-Euler mesh is used to allow true fluid-structure interaction (FSI). The FSI model requires blood flow to induce valve closure by inducing strains in the region of 10-20%. Model predictions were found to be consistent with existing literature and will undergo further development.
Convolutional Neural Network Based dem Super Resolution
Chen, Zixuan; Wang, Xuewen; Xu, Zekai; Hou, Wenguang
2016-06-01
DEM super resolution is proposed in our previous publication to improve the resolution for a DEM on basis of some learning examples. Meanwhile, the nonlocal algorithm is introduced to deal with it and lots of experiments show that the strategy is feasible. In our publication, the learning examples are defined as the partial original DEM and their related high measurements due to this way can avoid the incompatibility between the data to be processed and the learning examples. To further extent the applications of this new strategy, the learning examples should be diverse and easy to obtain. Yet, it may cause the problem of incompatibility and unrobustness. To overcome it, we intend to investigate a convolutional neural network based method. The input of the convolutional neural network is a low resolution DEM and the output is expected to be its high resolution one. A three layers model will be adopted. The first layer is used to detect some features from the input, the second integrates the detected features to some compressed ones and the final step transforms the compressed features as a new DEM. According to this designed structure, some learning DEMs will be taken to train it. Specifically, the designed network will be optimized by minimizing the error of the output and its expected high resolution DEM. In practical applications, a testing DEM will be input to the convolutional neural network and a super resolution will be obtained. Many experiments show that the CNN based method can obtain better reconstructions than many classic interpolation methods.
Energy Technology Data Exchange (ETDEWEB)
Egorov, Yurii V [Institute de Mathematique de Toulouse, Toulouse (France)
2013-04-30
We consider the classical problem on the tallest column which was posed by Euler in 1757. Bernoulli-Euler theory serves today as the basis for the design of high buildings. This problem is reduced to the problem of finding the potential for the Sturm-Liouville equation corresponding to the maximum of the first eigenvalue. The problem has been studied by many mathematicians but we give the first rigorous proof of the existence and uniqueness of the optimal column and we give new formulae which let us find it. Our method is based on a new approach consisting in the study of critical points of a related nonlinear functional. Bibliography: 6 titles.
A Direct Feedback Control Based on Fuzzy Recurrent Neural Network
Institute of Scientific and Technical Information of China (English)
李明; 马小平
2002-01-01
A direct feedback control system based on fuzzy-recurrent neural network is proposed, and a method of training weights of fuzzy-recurrent neural network was designed by applying modified contract mapping genetic algorithm. Computer simul ation results indicate that fuzzy-recurrent neural network controller has perfect dynamic and static performances .
SOLVING INVERSE KINEMATICS OF REDUNDANT MANIPULATOR BASED ON NEURAL NETWORK
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
For the redundant manipulators, neural network is used to tackle the velocity inverse kinematics of robot manipulators. The neural networks utilized are multi-layered perceptions with a back-propagation training algorithm. The weight table is used to save the weights solving the inverse kinematics based on the different optimization performance criteria. Simulations verify the effectiveness of using neural network.
Prediction based chaos control via a new neural network
Energy Technology Data Exchange (ETDEWEB)
Shen Liqun [School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001 (China)], E-mail: liqunshen@gmail.com; Wang Mao [Space Control and Inertia Technology Research Center, Harbin Institute of Technology, Harbin 150001 (China); Liu Wanyu [School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001 (China); Sun Guanghui [Space Control and Inertia Technology Research Center, Harbin Institute of Technology, Harbin 150001 (China)
2008-11-17
In this Letter, a new chaos control scheme based on chaos prediction is proposed. To perform chaos prediction, a new neural network architecture for complex nonlinear approximation is proposed. And the difficulty in building and training the neural network is also reduced. Simulation results of Logistic map and Lorenz system show the effectiveness of the proposed chaos control scheme and the proposed neural network.
Euler's Amazing Way to Solve Equations.
Flusser, Peter
1992-01-01
Presented is a series of examples that illustrate a method of solving equations developed by Leonhard Euler based on an unsubstantiated assumption. The method integrates aspects of recursion relations and sequences of converging ratios and can be extended to polynomial equation with infinite exponents. (MDH)
Directory of Open Access Journals (Sweden)
Maria Anna De Rosa
2014-01-01
Full Text Available The free vibration response of double-walled carbon nanotubes (DWCNTs is investigated. The DWCNTs are modelled as two beams, interacting between them through the van der Waals forces, and the nonlocal Euler-Bernoulli beam theory is used. The governing equations of motion are derived using a variational approach and the free frequencies of vibrations are obtained employing two different approaches. In the first method, the two double-walled carbon nanotubes are discretized by means of the so-called “cell discretization method” (CDM in which each nanotube is reduced to a set of rigid bars linked together by elastic cells. The resulting discrete system takes into account nonlocal effects, constraint elasticities, and the van der Waals forces. The second proposed approach, belonging to the semianalytical methods, is an optimized version of the classical Rayleigh quotient, as proposed originally by Schmidt. The resulting conditions are solved numerically. Numerical examples end the paper, in which the two approaches give lower-upper bounds to the true values, and some comparisons with existing results are offered. Comparisons of the present numerical results with those from the open literature show an excellent agreement.
Institute of Scientific and Technical Information of China (English)
LIU Wei-qun; MIAO Xie-xing
2006-01-01
The overbroken rock mass of gob areas is made up of broken and accumulated rock blocks compressed to some extent by the overlying strata. The bearing pressure of the gob can directly affect the safety of mining fields, formation of road retained along the next goaf and seepage of water and methane through the gob. In this paper, the software RFPA'2000 is used to construct numerical models. Especially the Euler method of control volume is proposed to solve the simulation difficulty arising from plastically finite deformations. The results show that three characteristic regions occurred in the gob area: (1) a naturally accumulated region, 0-10 m away from unbroken surrounding rock walls, where the bearing pressure is nearly zero; (2) an overcompacted region, 10-20 m away from unbroken walls, where the bearing pressure results in the maximum value of the gob area; (3) a stable compaction region, more than 20 m away from unbroken walls and occupying absolutely most of the gob area, where the bearing pressures show basically no differences. Such a characteristic can explain the easy-seepaged "O"-ring phenomena around mining fields very well.
De Rosa, Maria Anna; Lippiello, Maria
2014-01-01
The free vibration response of double-walled carbon nanotubes (DWCNTs) is investigated. The DWCNTs are modelled as two beams, interacting between them through the van der Waals forces, and the nonlocal Euler-Bernoulli beam theory is used. The governing equations of motion are derived using a variational approach and the free frequencies of vibrations are obtained employing two different approaches. In the first method, the two double-walled carbon nanotubes are discretized by means of the so-called "cell discretization method" (CDM) in which each nanotube is reduced to a set of rigid bars linked together by elastic cells. The resulting discrete system takes into account nonlocal effects, constraint elasticities, and the van der Waals forces. The second proposed approach, belonging to the semianalytical methods, is an optimized version of the classical Rayleigh quotient, as proposed originally by Schmidt. The resulting conditions are solved numerically. Numerical examples end the paper, in which the two approaches give lower-upper bounds to the true values, and some comparisons with existing results are offered. Comparisons of the present numerical results with those from the open literature show an excellent agreement.
Analysis of Neural Networks through Base Functions
Zwaag, van der B.J.; Slump, C.H.; Spaanenburg, L.
2002-01-01
Problem statement. Despite their success-story, neural networks have one major disadvantage compared to other techniques: the inability to explain comprehensively how a trained neural network reaches its output; neural networks are not only (incorrectly) seen as a "magic tool" but possibly even more
Nonlinear system identification and control based on modular neural networks.
Puscasu, Gheorghe; Codres, Bogdan
2011-08-01
A new approach for nonlinear system identification and control based on modular neural networks (MNN) is proposed in this paper. The computational complexity of neural identification can be greatly reduced if the whole system is decomposed into several subsystems. This is obtained using a partitioning algorithm. Each local nonlinear model is associated with a nonlinear controller. These are also implemented by neural networks. The switching between the neural controllers is done by a dynamical switcher, also implemented by neural networks, that tracks the different operating points. The proposed multiple modelling and control strategy has been successfully tested on simulated laboratory scale liquid-level system.
Cancer classification based on gene expression using neural networks.
Hu, H P; Niu, Z J; Bai, Y P; Tan, X H
2015-12-21
Based on gene expression, we have classified 53 colon cancer patients with UICC II into two groups: relapse and no relapse. Samples were taken from each patient, and gene information was extracted. Of the 53 samples examined, 500 genes were considered proper through analyses by S-Kohonen, BP, and SVM neural networks. Classification accuracy obtained by S-Kohonen neural network reaches 91%, which was more accurate than classification by BP and SVM neural networks. The results show that S-Kohonen neural network is more plausible for classification and has a certain feasibility and validity as compared with BP and SVM neural networks.
Evolving Chart Pattern Sensitive Neural Network Based Forex Trading Agents
Sher, Gene I
2011-01-01
Though machine learning has been applied to the foreign exchange market for quiet some time now, and neural networks have been shown to yield good results, in modern approaches neural network systems are optimized through the traditional methods, and their input signals are vectors containing prices and other indicator elements. The aim of this paper is twofold, the presentation and testing of the application of topology and weight evolving artificial neural network (TWEANN) systems to automated currency trading, and the use of chart images as input to a geometrical regularity aware indirectly encoded neural network systems. This paper presents the benchmark results of neural network based automated currency trading systems evolved using TWEANNs, and compares the generalization capabilities of these direct encoded neural networks which use the standard price vector inputs, and the indirect (substrate) encoded neural networks which use chart images as input. The TWEANN algorithm used to evolve these currency t...
Multispectral thermometry based on neural network
Institute of Scientific and Technical Information of China (English)
孙晓刚; 戴景民
2003-01-01
In order to overcome the effect of the assumption between emissivity and wavelength on the measurement of true temperature and spectral emissivity for most engineering materials, a neural network based method is proposed for data processing while a blackbody furnace and three optical filters with known spectral transmittance curves were used to make up a true target. The experimental results show that the calculated temperatures are in good agreement with the temperature of the blackbody furnace, and the calculated spectral emissivity curves are in good agreement with the spectral transmittance curves of the filters. The method proposed has been proved to be an effective method for solving the problem of true temperature and emissivity measurement, and it can overcome the effect of the assumption between emissivity and wavelength on the measurement of true temperature and spectral emissivity for most engineering materials.
Implementation of neural network based non-linear predictive
DEFF Research Database (Denmark)
Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole
1998-01-01
-linear systems. GPC is model-based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis on an efficient Quasi...
Implementation of neural network based non-linear predictive control
DEFF Research Database (Denmark)
Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole
1999-01-01
of non-linear systems. GPC is model based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model, a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis...
Neural mass model-based tracking of anesthetic brain states
Kuhlmann, Levin; Freestone, Dean R.; Manton, Jonathan H.; Heyse, Bjorn; Vereecke, Hugo E. M.; Lipping, Tarmo; Struys, Michel M. R. F.; Liley, David T. J.
2016-01-01
Neural mass model-based tracking of brain states from electroencephalographic signals holds the promise of simultaneously tracking brain states while inferring underlying physiological changes in various neuroscientific and clinical applications. Here, neural mass model-based tracking of brain state
Neural-networks-based Modelling and a Fuzzy Neural Networks Controller of MCFC
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
Molten Carbonate Fuel Cells (MCFC) are produced with a highly efficient and clean power generation technology which will soon be widely utilized. The temperature characters of MCFC stack are briefly analyzed. A radial basis function (RBF) neural networks identification technology is applied to set up the temperature nonlinear model of MCFC stack, and the identification structure, algorithm and modeling training process are given in detail. A fuzzy controller of MCFC stack is designed. In order to improve its online control ability, a neural network trained by the I/O data of a fuzzy controller is designed. The neural networks can memorize and expand the inference rules of the fuzzy controller and substitute for the fuzzy controller to control MCFC stack online. A detailed design of the controller is given. The validity of MCFC stack modelling based on neural networks and the superior performance of the fuzzy neural networks controller are proved by Simulations.
A Neural Network-Based Interval Pattern Matcher
Directory of Open Access Journals (Sweden)
Jing Lu
2015-07-01
Full Text Available One of the most important roles in the machine learning area is to classify, and neural networks are very important classifiers. However, traditional neural networks cannot identify intervals, let alone classify them. To improve their identification ability, we propose a neural network-based interval matcher in our paper. After summarizing the theoretical construction of the model, we take a simple and a practical weather forecasting experiment, which show that the recognizer accuracy reaches 100% and that is promising.
Generalization of the Euler Angles
Bauer, Frank H. (Technical Monitor); Shuster, Malcolm D.; Markley, F. Landis
2002-01-01
It is shown that the Euler angles can be generalized to axes other than members of an orthonormal triad. As first shown by Davenport, the three generalized Euler axes, hereafter: Davenport axes, must still satisfy the constraint that the first two and the last two axes be mutually perpendicular if these axes are to define a universal set of attitude parameters. Expressions are given which relate the generalized Euler angles, hereafter: Davenport angles, to the 3-1-3 Euler angles of an associated direction-cosine matrix. The computation of the Davenport angles from the attitude matrix and their kinematic equation are presented. The present work offers a more direct development of the Davenport angles than Davenport's original publication and offers additional results.
Pulliam, Tom; Kwak, Dochan (Technical Monitor)
1997-01-01
Implicit methods have been the workhorse for the Euler and Navier-Stokes equations for the last 25 years. The ground breaking work of Dr. Joe Steger in implementing such techniques in practical Euler and Navier-Stokes codes provided the basis for all the success in this area. This presentation will highlight his contribution and technical excellence in the area of implicit methods for CFD.
Digital Watermarking Algorithm Based on Wavelet Transform and Neural Network
Institute of Scientific and Technical Information of China (English)
WANG Zhenfei; ZHAI Guangqun; WANG Nengchao
2006-01-01
An effective blind digital watermarking algorithm based on neural networks in the wavelet domain is presented. Firstly, the host image is decomposed through wavelet transform. The significant coefficients of wavelet are selected according to the human visual system (HVS) characteristics. Watermark bits are added to them. And then effectively cooperates neural networks to learn the characteristics of the embedded watermark related to them. Because of the learning and adaptive capabilities of neural networks, the trained neural networks almost exactly recover the watermark from the watermarked image. Experimental results and comparisons with other techniques prove the effectiveness of the new algorithm.
INDUCTION OF DECISION TREES BASED ON A FUZZY NEURAL NETWORK
Institute of Scientific and Technical Information of China (English)
Tang Bin; Hu Guangrui; Mao Xiaoquan
2002-01-01
Based on a fuzzy neural network, the letter presents an approach for the induction of decision trees. The approach makes use of the weights of fuzzy mappings in the fuzzy neural network which has been trained. It can realize the optimization of fuzzy decision trees by branch cutting, and improve the ratio of correctness and efficiency of the induction of decision trees.
Chang, S.-C.; Himansu, A.; Loh, C.-Y.; Wang, X.-Y.; Yu, S.-T.J.
2005-01-01
This paper reports on a significant advance in the area of nonreflecting boundary conditions (NRBCs) for unsteady flow computations. As a part of t he development of t he space-time conservation element and solution element (CE/SE) method, sets of NRBCs for 1D Euler problems are developed without using any characteristics- based techniques. These conditions are much simpler than those commonly reported in the literature, yet so robust that they are applicable to subsonic, transonic and supersonic flows even in the presence of discontinuities. In addition, the straightforward multidimensional extensions of the present 1D NRBCs have been shown numerically to be equally simple and robust. The paper details the theoretical underpinning of these NRBCs, and explains t heir unique robustness and accuracy in terms of t he conservation of space-time fluxes. Some numerical results for an extended Sod's shock-tube problem, illustrating the effectiveness of the present NRBCs are included, together with an associated simple Fortran computer program. As a preliminary to the present development, a review of the basic CE/SE schemes is also included.
CDMA and TDMA based neural nets.
Herrero, J C
2001-06-01
CDMA and TDMA telecommunication techniques were established long time ago, but they have acquired a renewed presence due to the rapidly increasing mobile phones demand. In this paper, we are going to see they are suitable for neural nets, if we leave the concept "connection" between processing units and we adopt the concept "messages" exchanged between them. This may open the door to neural nets with a higher number of processing units and flexible configuration.
Brain tumor grading based on Neural Networks and Convolutional Neural Networks.
Yuehao Pan; Weimin Huang; Zhiping Lin; Wanzheng Zhu; Jiayin Zhou; Wong, Jocelyn; Zhongxiang Ding
2015-08-01
This paper studies brain tumor grading using multiphase MRI images and compares the results with various configurations of deep learning structure and baseline Neural Networks. The MRI images are used directly into the learning machine, with some combination operations between multiphase MRIs. Compared to other researches, which involve additional effort to design and choose feature sets, the approach used in this paper leverages the learning capability of deep learning machine. We present the grading performance on the testing data measured by the sensitivity and specificity. The results show a maximum improvement of 18% on grading performance of Convolutional Neural Networks based on sensitivity and specificity compared to Neural Networks. We also visualize the kernels trained in different layers and display some self-learned features obtained from Convolutional Neural Networks.
Caption detection from video sequence based on fuzzy neural networks
Gao, Xinbo; Xin, Hong; Li, Jie
2001-09-01
Caption graphically superimposed in video frames can provide important indexing information. The automatic detection and recognition of video captions can be of great help in querying topics of interest in digital news library. To detect the caption from video sequence, we present algorithms based on fuzzy clustering neural networks. Since neural networks have the capabilities of learning and self-organizing and parallel computing mechanism, with the great increasing of digital images and video databases, neural networks based techniques become more efficient and popular tools for multimedia processing. Experimental results show that our caption detection scheme is effective and robust.
Weather forecasting based on hybrid neural model
Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.
2017-02-01
Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.
Architecture Analysis of an FPGA-Based Hopfield Neural Network
Directory of Open Access Journals (Sweden)
Miguel Angelo de Abreu de Sousa
2014-01-01
Full Text Available Interconnections between electronic circuits and neural computation have been a strongly researched topic in the machine learning field in order to approach several practical requirements, including decreasing training and operation times in high performance applications and reducing cost, size, and energy consumption for autonomous or embedded developments. Field programmable gate array (FPGA hardware shows some inherent features typically associated with neural networks, such as, parallel processing, modular executions, and dynamic adaptation, and works on different types of FPGA-based neural networks were presented in recent years. This paper aims to address different aspects of architectural characteristics analysis on a Hopfield Neural Network implemented in FPGA, such as maximum operating frequency and chip-area occupancy according to the network capacity. Also, the FPGA implementation methodology, which does not employ multipliers in the architecture developed for the Hopfield neural model, is presented, in detail.
Neural bases of congenital amusia in tonal language speakers.
Zhang, Caicai; Peng, Gang; Shao, Jing; Wang, William S-Y
2017-03-01
Congenital amusia is a lifelong neurodevelopmental disorder of fine-grained pitch processing. In this fMRI study, we examined the neural bases of congenial amusia in speakers of a tonal language - Cantonese. Previous studies on non-tonal language speakers suggest that the neural deficits of congenital amusia lie in the music-selective neural circuitry in the right inferior frontal gyrus (IFG). However, it is unclear whether this finding can generalize to congenital amusics in tonal languages. Tonal language experience has been reported to shape the neural processing of pitch, which raises the question of how tonal language experience affects the neural bases of congenital amusia. To investigate this question, we examined the neural circuitries sub-serving the processing of relative pitch interval in pitch-matched Cantonese level tone and musical stimuli in 11 Cantonese-speaking amusics and 11 musically intact controls. Cantonese-speaking amusics exhibited abnormal brain activities in a widely distributed neural network during the processing of lexical tone and musical stimuli. Whereas the controls exhibited significant activation in the right superior temporal gyrus (STG) in the lexical tone condition and in the cerebellum regardless of the lexical tone and music conditions, no activation was found in the amusics in those regions, which likely reflects a dysfunctional neural mechanism of relative pitch processing in the amusics. Furthermore, the amusics showed abnormally strong activation of the right middle frontal gyrus and precuneus when the pitch stimuli were repeated, which presumably reflect deficits of attending to repeated pitch stimuli or encoding them into working memory. No significant group difference was found in the right IFG in either the whole-brain analysis or region-of-interest analysis. These findings imply that the neural deficits in tonal language speakers might differ from those in non-tonal language speakers, and overlap partly with the
Image watermarking capacity analysis based on Hopfield neural network
Institute of Scientific and Technical Information of China (English)
Fan Zhang(张帆); Hongbin Zhang(张鸿宾)
2004-01-01
In watermarking schemes, watermarking can be viewed as a form of communication problems. Almost all of previous works on image watermarking capacity are based on information theory, using Shannon formula to calculate the capacity of watermarking. In this paper, we present a blind watermarking algorithm using Hopfield neural network, and analyze watermarking capacity based on neural network. In our watermarking algorithm, watermarking capacity is decided by attraction basin of associative memory.
Contractor Prequalification Based on Neural Networks
Institute of Scientific and Technical Information of China (English)
ZHANG Jin-long; YANG Lan-rong
2002-01-01
Contractor Prequalification involves the screening of contractors by a project owner, according to a given set of criteria, in order to determine their competence to perform the work if awarded the construction contract. This paper introduces the capabilities of neural networks in solving problems related to contractor prequalification. The neural network systems for contractor prequalification has an input vector of 8 components and an output vector of 1 component. The output vector represents whether a contractor is qualified or not qualified to submit a bid on a project.
Human Neural Cell-Based Biosensor
2013-05-28
including incubation with factors such as SHH ) and proceed to Human Neural Progenitor Cells Dopaminergic Differentiation β-III Tubulin/TH...exposure in human embryonic stem cells. J Recept Signal Transduct Res. 2011 Jun;31(3):206-13. Gerwe BA, Angel PM, West FD, Hasneen K, Young A
MOVING TARGETS PATTERN RECOGNITION BASED ON THE WAVELET NEURAL NETWORK
Institute of Scientific and Technical Information of China (English)
Ge Guangying; Chen Lili; Xu Jianjian
2005-01-01
Based on pattern recognition theory and neural network technology, moving objects automatic detection and classification method integrating advanced wavelet analysis are discussed in detail. An algorithm of moving targets pattern recognition on the combination of inter-frame difference and wavelet neural network is presented. The experimental results indicate that the designed BP wavelet network using this algorithm can recognize and classify moving targets rapidly and effectively.
Larger bases and mixed analog/digital neural nets
Energy Technology Data Exchange (ETDEWEB)
Beiu, V.
1998-12-31
The paper overviews results dealing with the approximation capabilities of neural networks, and bounds on the size of threshold gate circuits. Based on an explicit numerical algorithm for Kolmogorov`s superpositions the authors show that minimum size neural networks--for implementing any Boolean function--have the identity function as the activation function. Conclusions and several comments on the required precision are ending the paper.
Decoupling Control Method Based on Neural Network for Missiles
Institute of Scientific and Technical Information of China (English)
ZHAN Li; LUO Xi-shuang; ZHANG Tian-qiao
2005-01-01
In order to make the static state feedback nonlinear decoupling control law for a kind of missile to be easy for implementation in practice, an improvement is discussed. The improvement method is to introduce a BP neural network to approximate the decoupling control laws which are designed for different aerodynamic characteristic points, so a new decoupling control law based on BP neural network is produced after the network training. The simulation results on an example illustrate the approach obtained feasible and effective.
Potential singularity mechanism for the Euler equations
Brenner, Michael P.; Hormoz, Sahand; Pumir, Alain
2016-12-01
Singular solutions to the Euler equations could provide essential insight into the formation of very small scales in highly turbulent flows. Previous attempts to find singular flow structures have proven inconclusive. We reconsider the problem of interacting vortex tubes, for which it has long been observed that the flattening of the vortices inhibits sustained self-amplification of velocity gradients. Here we consider an iterative mechanism, based on the transformation of vortex filaments into sheets and their subsequent instability back into filaments. Elementary fluid mechanical arguments are provided to support the formation of a singular structure via this iterated mechanism, which we analyze based on a simplified model of filament interactions.
Generalizations of Euler Numbers and Euler Numbers of Higher Order%Euler数和高阶Euler数的推广
Institute of Scientific and Technical Information of China (English)
雒秋明; 祁锋
2005-01-01
The purpose of this paper is to define the generalized Euler numbers and the generalized Euler numbers of higher order, their recursion formula and some properties were established, accordingly Euler numbers and Euler numbers of higher order were extended.
Brocard Point and Euler Function
Sastry, K. R. S.
2007-01-01
This paper takes a known point from Brocard geometry, a known result from the geometry of the equilateral triangle, and bring in Euler's [empty set] function. It then demonstrates how to obtain new Brocard Geometric number theory results from them. Furthermore, this paper aims to determine a [triangle]ABC whose Crelle-Brocard Point [omega]…
Wild, Walter J.
1980-01-01
Discusses the simplest three-body problem, known as Euler's problem. The article, intended for students in the undergraduate mathematics and physics curricula, shows how the complex equations for a specific three-body problem can be solved on a small calculator. (HM)
Euler Products Beyond the Boundary
Kimura, Taro; Koyama, Shin-ya; Kurokawa, Nobushige
2014-01-01
We investigate the behavior of the Euler products of the Riemann zeta function and Dirichlet L-functions on the critical line. A refined version of the Riemann hypothesis, which is named "the Deep Riemann Hypothesis", is examined. We also study various analogs for global function fields. We give an interpretation for the nontrivial zeros from the viewpoint of statistical mechanics.
Hopfield neural network based on ant system
Institute of Scientific and Technical Information of China (English)
洪炳镕; 金飞虎; 郭琦
2004-01-01
Hopfield neural network is a single layer feedforward neural network. Hopfield network requires some control parameters to be carefully selected, else the network is apt to converge to local minimum. An ant system is a nature inspired meta heuristic algorithm. It has been applied to several combinatorial optimization problems such as Traveling Salesman Problem, Scheduling Problems, etc. This paper will show an ant system may be used in tuning the network control parameters by a group of cooperated ants. The major advantage of this network is to adjust the network parameters automatically, avoiding a blind search for the set of control parameters.This network was tested on two TSP problems, 5 cities and 10 cities. The results have shown an obvious improvement.
Euler vector for search and retrieval of gray-tone images.
Bishnu, Arijit; Bhattacharya, Bhargab B; Kundu, Malay K; Murthy, C A; Acharya, Tinku
2005-08-01
A new combinatorial characterization of a gray-tone image called Euler Vector is proposed. The Euler number of a binary image is a well-known topological feature, which remains invariant under translation, rotation, scaling, and rubber-sheet transformation of the image. The Euler vector comprises a 4-tuple, where each element is an integer representing the Euler number of the partial binary image formed by the gray-code representation of the four most significant bit planes of the gray-tone image. Computation of Euler vector requires only integer and Boolean operations. The Euler vector is experimentally observed to be robust against noise and compression. For efficient image indexing, storage and retrieval from an image database using this vector, a bucket searching technique based on a simple modification of Kd-tree, is employed successfully. The Euler vector can also be used to perform an efficient four-dimensional range query. The set of retrieved images are finally ranked on the basis of Mahalanobis distance measure. Experiments are performed on the COIL database and results are reported. The retrieval success can be improved significantly by augmentiong the Euler vector by a few additional simple shape features. Since Euler vector can be computed very fast, the proposed technique is likely to find many applications to content-based image retrieval.
Numeral eddy current sensor modelling based on genetic neural network
Institute of Scientific and Technical Information of China (English)
Yu A-Long
2008-01-01
This paper presents a method used to the numeral eddy current sensor modelling based on the genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced. In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network (GNN) according to measurement data. So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network. The nonlinear model has the advantages of strong robustness,on-line modelling and high precision.The maximum nonlinearity error can be reduced to 0.037% by using GNN.However, the maximum nonlinearity error is 0.075% using the least square method.
Impulsive Neural Networks Algorithm Based on the Artificial Genome Model
Directory of Open Access Journals (Sweden)
Yuan Gao
2014-05-01
Full Text Available To describe gene regulatory networks, this article takes the framework of the artificial genome model and proposes impulsive neural networks algorithm based on the artificial genome model. Firstly, the gene expression and the cell division tree are applied to generate spiking neurons with specific attributes, neural network structure, connection weights and specific learning rules of each neuron. Next, the gene segment duplications and divergence model are applied to design the evolutionary algorithm of impulsive neural networks at the level of the artificial genome. The dynamic changes of developmental gene regulatory networks are controlled during the whole evolutionary process. Finally, the behavior of collecting food for autonomous intelligent agent is simulated, which is driven by nerves. Experimental results demonstrate that the algorithm in this article has the evolutionary ability on large-scale impulsive neural networks
NeuralNetwork Based 3D Surface Reconstruction
Joseph, Vincy
2009-01-01
This paper proposes a novel neural-network-based adaptive hybrid-reflectance three-dimensional (3-D) surface reconstruction model. The neural network combines the diffuse and specular components into a hybrid model. The proposed model considers the characteristics of each point and the variant albedo to prevent the reconstructed surface from being distorted. The neural network inputs are the pixel values of the two-dimensional images to be reconstructed. The normal vectors of the surface can then be obtained from the output of the neural network after supervised learning, where the illuminant direction does not have to be known in advance. Finally, the obtained normal vectors can be applied to integration method when reconstructing 3-D objects. Facial images were used for training in the proposed approach
Stability analysis of extended discrete-time BAM neural networks based on LMI approach
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
We propose a new approach for analyzing the global asymptotic stability of the extended discrete-time bidirectional associative memory (BAM) neural networks. By using the Euler rule, we discretize the continuous-time BAM neural networks as the extended discrete-time BAM neural networks with non-threshold activation functions. Here we present some conditions under which the neural networks have unique equilibrium points. To judge the global asymptotic stability of the equilibrium points, we introduce a new neural network model - standard neural network model (SNNM).For the SNNMs, we derive the sufficient conditions for the global asymptotic stability of the equilibrium points, which are formulated as some linear matrix inequalities (LMIs). We transform the discrete-time BAM into the SNNM and apply the general result about the SNNM to the determination of global asymptotic stability of the discrete-time BAM. The approach proposed extends the known stability results, has lower conservativeness, can be verified easily, and can also be applied to other forms of recurrent neural networks.
Principal symbol of Euler-Lagrange operators
Fatibene, L.; Garruto, S.
2016-07-01
We shall introduce the principal symbol for quite a general class of (quasi linear) Euler-Lagrange operators and use them to characterise well-posed initial value problems in gauge covariant field theories. We shall clarify how constraints can arise in covariant Lagrangian theories by extending the standard treatment in GR and without resorting to Hamiltonian formalism. Finally as an example of application, we sketch a quantisation procedure based on what is done in LQG by framing it in a more general context which applies to general gauge covariant field theories.
Colored Noise Prediction Based on Neural Network
Institute of Scientific and Technical Information of China (English)
Gao Fei; Zhang Xiaohui
2003-01-01
A method for predicting colored noise by introducing prediction of nonhnear time series is presented By adopting three kinds of neural networks prediction models, the colored noise prediction is studied through changing the filter bandwidth for stochastic noise and the sampling rate for colored noise The results show that colored noise can be predicted The prediction error decreases with the increasing of the sampling rate or the narrowing of the filter bandwidth. If the parameters are selected properly, the prediction precision can meet the requirement of engineering implementation. The results offer a new reference way for increasing the ability for detecting weak signal in signal processing system
Adaptive Neural Network Based Control of Noncanonical Nonlinear Systems.
Zhang, Yanjun; Tao, Gang; Chen, Mou
2016-09-01
This paper presents a new study on the adaptive neural network-based control of a class of noncanonical nonlinear systems with large parametric uncertainties. Unlike commonly studied canonical form nonlinear systems whose neural network approximation system models have explicit relative degree structures, which can directly be used to derive parameterized controllers for adaptation, noncanonical form nonlinear systems usually do not have explicit relative degrees, and thus their approximation system models are also in noncanonical forms. It is well-known that the adaptive control of noncanonical form nonlinear systems involves the parameterization of system dynamics. As demonstrated in this paper, it is also the case for noncanonical neural network approximation system models. Effective control of such systems is an open research problem, especially in the presence of uncertain parameters. This paper shows that it is necessary to reparameterize such neural network system models for adaptive control design, and that such reparameterization can be realized using a relative degree formulation, a concept yet to be studied for general neural network system models. This paper then derives the parameterized controllers that guarantee closed-loop stability and asymptotic output tracking for noncanonical form neural network system models. An illustrative example is presented with the simulation results to demonstrate the control design procedure, and to verify the effectiveness of such a new design method.
Network Traffic Prediction based on Particle Swarm BP Neural Network
Directory of Open Access Journals (Sweden)
Yan Zhu
2013-11-01
Full Text Available The traditional BP neural network algorithm has some bugs such that it is easy to fall into local minimum and the slow convergence speed. Particle swarm optimization is an evolutionary computation technology based on swarm intelligence which can not guarantee global convergence. Artificial Bee Colony algorithm is a global optimum algorithm with many advantages such as simple, convenient and strong robust. In this paper, a new BP neural network based on Artificial Bee Colony algorithm and particle swarm optimization algorithm is proposed to optimize the weight and threshold value of BP neural network. After network traffic prediction experiment, we can conclude that optimized BP network traffic prediction based on PSO-ABC has high prediction accuracy and has stable prediction performance.
Image Restoration Technology Based on Discrete Neural network
Directory of Open Access Journals (Sweden)
Zhou Duoying
2015-01-01
Full Text Available With the development of computer science and technology, the development of artificial intelligence advances rapidly in the field of image restoration. Based on the MATLAB platform, this paper constructs a kind of image restoration technology of artificial intelligence based on the discrete neural network and feedforward network, and carries out simulation and contrast of the restoration process by the use of the bionic algorithm. Through the application of simulation restoration technology, this paper verifies that the discrete neural network has a good convergence and identification capability in the image restoration technology with a better effect than that of the feedforward network. The restoration technology based on the discrete neural network can provide a reliable mathematical model for this field.
Data Mining and Neural Network Techniques in Case Based System
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
This paper first puts forward a case-based system framework basedon data mining techniques. Then the paper examines the possibility of using neural n etworks as a method of retrieval in such a case-based system. In this system we propose data mining algorithms to discover case knowledge and other algorithms.
Unfolding code for neutron spectrometry based on neural nets technology
Energy Technology Data Exchange (ETDEWEB)
Ortiz R, J. M.; Vega C, H. R., E-mail: morvymm@yahoo.com.mx [Universidad Autonoma de Zacatecas, Unidad Academica de Ingenieria Electrica, Apdo. Postal 336, 98000 Zacatecas (Mexico)
2012-10-15
The most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Neural Networks have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This unfolding code called Neutron Spectrometry and Dosimetry by means of Artificial Neural Networks was designed in a graphical interface under LabVIEW programming environment. The core of the code is an embedded neural network architecture, previously optimized by the {sup R}obust Design of Artificial Neural Networks Methodology{sup .} The main features of the code are: is easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a {sup 6}Lil(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, only seven rate counts measurement with a Bonner spheres spectrometer are required for simultaneously unfold the 60 energy bins of the neutron spectrum and to calculate 15 dosimetric quantities, for radiation protection porpoises. This code generates a full report in html format with all relevant information. (Author)
Forest Fire Image Intelligent Recognition based on the Neural Network
Yan Qiang; Bo Pei; Juanjuan Zhao
2014-01-01
To avoid the drawbacks caused by the long-distance and large-area features of the outdoor forest fires in the traditional fire detection methods. A new forest fire recognition method based on the neural network is proposed, which recognizes the fire based on the static and dynamic features of the fire. The method combines the multiple parameters of the flames and the shapes of the fire to distinguish fire image. Then the extracted features were tested by the Back Propagation Neural Network. T...
A Robust Digital Watermark Extracting Method Based on Neural Network
Institute of Scientific and Technical Information of China (English)
GUOLihua; YANGShutang; LIJianhua
2003-01-01
Since watermark removal software, such as StirMark, has succeeded in washing watermarks away for most of the known watermarking systems, it is necessary to improve the robustness of watermarking systems. A watermark extracting method based on the error Back propagation (BP) neural network is presented in this paper, which can efficiently improve the robustness of watermarking systems. Experiments show that even if the watermarking systems are attacked by the StirMark software, the extracting method based on neural network can still efficiently extract the whole watermark information.
Neural network-based sensor signal accelerator.
Energy Technology Data Exchange (ETDEWEB)
Vogt, M. C.
2000-10-16
A strategy has been developed to computationally accelerate the response time of a generic electronic sensor. The strategy can be deployed as an algorithm in a control system or as a physical interface (on an embedded microcontroller) between a slower responding external sensor and a higher-speed control system. Optional code implementations are available to adjust algorithm performance when computational capability is limited. In one option, the actual sensor signal can be sampled at the slower rate with adaptive linear neural networks predicting the sensor's future output and interpolating intermediate synthetic output values. In another option, a synchronized collection of predictors sequentially controls the corresponding synthetic output voltage. Error is adaptively corrected in both options. The core strategy has been demonstrated with automotive oxygen sensor data. A prototype interface device is under construction. The response speed increase afforded by this strategy could greatly offset the cost of developing a replacement sensor with a faster physical response time.
The neural bases of orthographic working memory
Directory of Open Access Journals (Sweden)
Jeremy Purcell
2014-04-01
First, these results reveal a neurotopography of OWM lesion sites that is well-aligned with results from neuroimaging of orthographic working memory in neurally intact participants (Rapp & Dufor, 2011. Second, the dorsal neurotopography of the OWM lesion overlap is clearly distinct from what has been reported for lesions associated with either lexical or sublexical deficits (e.g., Henry, Beeson, Stark, & Rapcsak, 2007; Rapcsak & Beeson, 2004; these have, respectively, been identified with the inferior occipital/temporal and superior temporal/inferior parietal regions. These neurotopographic distinctions support the claims of the computational distinctiveness of long-term vs. working memory operations. The specific lesion loci raise a number of questions to be discussed regarding: (a the selectivity of these regions and associated deficits to orthographic working memory vs. working memory more generally (b the possibility that different lesion sub-regions may correspond to different components of the OWM system.
Directory of Open Access Journals (Sweden)
D. Vivek
2016-11-01
Full Text Available In this paper, the improved Euler method is used for solving hybrid fuzzy fractional differential equations (HFFDE of order $q \\in (0, 1 $ under Caputo-type fuzzy fractional derivatives. This method is based on the fractional Euler method and generalized Taylor's formula. The accuracy and efficiency of the proposed method is demonstrated by solving numerical examples.
Data Process of Diagnose Expert System based on Neural Network
Directory of Open Access Journals (Sweden)
Shupeng Zhao
2013-12-01
Full Text Available Engine fault has a high rate in the car. Considering about the distinguishing feature of the engine, Engine Diagnosis Expert System was investigated based on Diagnosis Tree module, Fuzzy Neural Network module, and commix reasoning module. It was researched including Knowledge base and Reasoning machine, and so on. In Diagnosis Tree module, the origin problem was searched in right method. In which module distinguishing rate and low error and least cost was the aim. By means of synthesize judge and fuzzy relation reasoning to get fault origin from symptom, fuzzy synthesize reasoning diagnosis module was researched. Expert knowledge included failure symptom, engine system failure and engine part failure. In the system, Self-diagnosis method and general instruments method worked together, complex failure diagnosis became efficient. The system was intelligent, which was combined by fuzzy logic reasoning and the traditional neural network system. And it became more convenience for failure origin searching, because of utilizing the three methods. The system fuzzy neural networks were combined with fuzzy reasoning and traditional neural networks. Fuzzy neural network failure diagnosis module of system, as a important model was applied to engine diagnosis, with more advantages such as higher efficiency of searching and higher self-learning ability, which was compared with the traditional BP network
From neural-based object recognition toward microelectronic eyes
Sheu, Bing J.; Bang, Sa Hyun
1994-01-01
Engineering neural network systems are best known for their abilities to adapt to the changing characteristics of the surrounding environment by adjusting system parameter values during the learning process. Rapid advances in analog current-mode design techniques have made possible the implementation of major neural network functions in custom VLSI chips. An electrically programmable analog synapse cell with large dynamic range can be realized in a compact silicon area. New designs of the synapse cells, neurons, and analog processor are presented. A synapse cell based on Gilbert multiplier structure can perform the linear multiplication for back-propagation networks. A double differential-pair synapse cell can perform the Gaussian function for radial-basis network. The synapse cells can be biased in the strong inversion region for high-speed operation or biased in the subthreshold region for low-power operation. The voltage gain of the sigmoid-function neurons is externally adjustable which greatly facilitates the search of optimal solutions in certain networks. Various building blocks can be intelligently connected to form useful industrial applications. Efficient data communication is a key system-level design issue for large-scale networks. We also present analog neural processors based on perceptron architecture and Hopfield network for communication applications. Biologically inspired neural networks have played an important role towards the creation of powerful intelligent machines. Accuracy, limitations, and prospects of analog current-mode design of the biologically inspired vision processing chips and cellular neural network chips are key design issues.
ENTROPIES AND FLUX-SPLITTINGS FOR THE ISENTROPIC EULER EQUATIONS
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
The authors establish the existence of a large class of mathematical entropies (the so-called weak entropies) associated with the Euler equations for an isentropic, compressible fluid governed by a general pressure law. A mild assumption on the behavior of the pressure law near the vacuum is solely required. The analysis is based on an asymptotic expansion of the fundamental solution (called here the entropy kernel) of a highly singular Euler-Poisson-Darboux equation. The entropy kernel is only H lder continuous and its regularity is carefully investigated. Relying on a notion introduced earlier by the authors, it is also proven that, for the Euler equations, the set of entropy flux-splittings coincides with the set of entropies-entropy fluxes. These results imply the existence of a flux-splitting consistent with all of the entropy inequalities.
Mitchell, Peter
2011-01-01
This article outlines an extension exercise that is based on the elementary geometrical constructions in the National Curriculum. The challenge for students is to organise and execute an extended construction. The hidden, parallel agenda is proof, for the teacher's role is to convince students that they "must" succeed and that doing so is a…
Neural bases for addictive properties of benzodiazepines.
Tan, Kelly R; Brown, Matthew; Labouèbe, Gwenaël; Yvon, Cédric; Creton, Cyril; Fritschy, Jean-Marc; Rudolph, Uwe; Lüscher, Christian
2010-02-11
Benzodiazepines are widely used in clinics and for recreational purposes, but will lead to addiction in vulnerable individuals. Addictive drugs increase the levels of dopamine and also trigger long-lasting synaptic adaptations in the mesolimbic reward system that ultimately may induce the pathological behaviour. The neural basis for the addictive nature of benzodiazepines, however, remains elusive. Here we show that benzodiazepines increase firing of dopamine neurons of the ventral tegmental area through the positive modulation of GABA(A) (gamma-aminobutyric acid type A) receptors in nearby interneurons. Such disinhibition, which relies on alpha1-containing GABA(A) receptors expressed in these cells, triggers drug-evoked synaptic plasticity in excitatory afferents onto dopamine neurons and underlies drug reinforcement. Taken together, our data provide evidence that benzodiazepines share defining pharmacological features of addictive drugs through cell-type-specific expression of alpha1-containing GABA(A) receptors in the ventral tegmental area. The data also indicate that subunit-selective benzodiazepines sparing alpha1 may be devoid of addiction liability.
Electronic implementation of associative memory based on neural network models
Moopenn, A.; Lambe, John; Thakoor, A. P.
1987-01-01
An electronic embodiment of a neural network based associative memory in the form of a binary connection matrix is described. The nature of false memory errors, their effect on the information storage capacity of binary connection matrix memories, and a novel technique to eliminate such errors with the help of asymmetrical extra connections are discussed. The stability of the matrix memory system incorporating a unique local inhibition scheme is analyzed in terms of local minimization of an energy function. The memory's stability, dynamic behavior, and recall capability are investigated using a 32-'neuron' electronic neural network memory with a 1024-programmable binary connection matrix.
Clustering in mobile ad hoc network based on neural network
Institute of Scientific and Technical Information of China (English)
CHEN Ai-bin; CAI Zi-xing; HU De-wen
2006-01-01
An on-demand distributed clustering algorithm based on neural network was proposed. The system parameters and the combined weight for each node were computed, and cluster-heads were chosen using the weighted clustering algorithm, then a training set was created and a neural network was trained. In this algorithm, several system parameters were taken into account, such as the ideal node-degree, the transmission power, the mobility and the battery power of the nodes. The algorithm can be used directly to test whether a node is a cluster-head or not. Moreover, the clusters recreation can be speeded up.
Neural Network Model Based Cluster Head Selection for Power Control
Directory of Open Access Journals (Sweden)
Krishan Kumar
2011-01-01
Full Text Available Mobile ad-hoc network has challenge of the limited power to prolong the lifetime of the network, because power is a valuable resource in mobile ad-hoc network. The status of power consumption should be continuously monitored after network deployment. In this paper, we propose coverage aware neural network based power control routing with the objective of maximizing the network lifetime. Cluster head selection is proposed using adaptive learning in neural networks followed by coverage. The simulation results show that the proposed scheme can be used in wide area of applications in mobile ad-hoc network.
Neural-Based Models of Semiconductor Devices for SPICE Simulator
Directory of Open Access Journals (Sweden)
Hanene B. Hammouda
2008-01-01
Full Text Available The paper addresses a simple and fast new approach to implement Artificial Neural Networks (ANN models for the MOS transistor into SPICE. The proposed approach involves two steps, the modeling phase of the device by NN providing its input/output patterns, and the SPICE implementation process of the resulting model. Using the Taylor series expansion, a neural based small-signal model is derived. The reliability of our approach is validated through simulations of some circuits in DC and small-signal analyses.
Manipulator Neural Network Control Based on Fuzzy Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
The three-layer forward neural networks are used to establish the inverse kinem a tics models of robot manipulators. The fuzzy genetic algorithm based on the line ar scaling of the fitness value is presented to update the weights of neural net works. To increase the search speed of the algorithm, the crossover probability and the mutation probability are adjusted through fuzzy control and the fitness is modified by the linear scaling method in FGA. Simulations show that the propo sed method improves considerably the precision of the inverse kinematics solutio ns for robot manipulators and guarantees a rapid global convergence and overcome s the drawbacks of SGA and the BP algorithm.
Nonlinear system identification based on internal recurrent neural networks.
Puscasu, Gheorghe; Codres, Bogdan; Stancu, Alexandru; Murariu, Gabriel
2009-04-01
A novel approach for nonlinear complex system identification based on internal recurrent neural networks (IRNN) is proposed in this paper. The computational complexity of neural identification can be greatly reduced if the whole system is decomposed into several subsystems. This approach employs internal state estimation when no measurements coming from the sensors are available for the system states. A modified backpropagation algorithm is introduced in order to train the IRNN for nonlinear system identification. The performance of the proposed design approach is proven on a car simulator case study.
Control of Unknown Chaotic Systems Based on Neural Predictive Control
Institute of Scientific and Technical Information of China (English)
LI Dong-Mei; WANG Zheng-Ou
2003-01-01
We introduce the predictive control into the control of chaotic system and propose a neural networkcontrol algorithm based on predictive control. The proposed control system stabilizes the chaotic motion in an unknownchaotic system onto the desired target trajectory. The proposed algorithm is simple and its convergence speed is muchhigher than existing similar algorithms. The control system can control hyperchaos. We analyze the stability of thecontrol system and prove the convergence property of the neural controller. The theoretic derivation and simulationsdemonstrate the effectiveness of the algorithm.
Control of Unknown Chaotic Systems Based on Neural Predictive Control
Institute of Scientific and Technical Information of China (English)
LIDong-Mei; WANGZheng-Ou
2003-01-01
We introduce the predictive control into the control of chaotic system and propose a neural network control algorithm based on predictive control. The proposed control system stabilizes the chaotic motion in an unknown chaotic system onto the desired target trajectory. The proposed algorithm is simple and its convergence speed is much higher than existing similar algorithms. The control system can control hyperchaos. We analyze the stability of the control system and prove the convergence property of the neural controller. The theoretic derivation and simulations demonstrate the effectiveness of the algorithm.
Adaptive Synchronization of Memristor-based Chaotic Neural Systems
Directory of Open Access Journals (Sweden)
Xiaofang Hu
2014-11-01
Full Text Available Chaotic neural networks consisting of a great number of chaotic neurons are able to reproduce the rich dynamics observed in biological nervous systems. In recent years, the memristor has attracted much interest in the efficient implementation of artificial synapses and neurons. This work addresses adaptive synchronization of a class of memristor-based neural chaotic systems using a novel adaptive backstepping approach. A systematic design procedure is presented. Simulation results have demonstrated the effectiveness of the proposed adaptive synchronization method and its potential in practical application of memristive chaotic oscillators in secure communication.
Neural Network-Based Active Control for Offshore Platforms
Institute of Scientific and Technical Information of China (English)
周亚军; 赵德有
2003-01-01
A new active control scheme, based on neural network, for the suppression of oscillation in multiple-degree-of-freedom (MDOF) offshore platforms, is studied in this paper. With the main advantages of neural network, i.e. the inherent robustness, fault tolerance, and generalized capability of its parallel massive interconnection structure, the active structural control of offshore platforms under random waves is accomplished by use of the BP neural network model. The neural network is trained offline with the data generated from numerical analysis, and it simulates the process of Classical Linear Quadratic Regular Control for the platform under random waves. After the learning phase, the trained network has learned about the nonlinear dynamic behavior of the active control system, and is capable of predicting the active control forces of the next time steps. The results obtained show that the active control is feasible and effective, and it finally overcomes time delay owing to the robustness, fault tolerance, and generalized capability of artificial neural network.
A rule-based neural controller for inverted pendulum system.
Hao, J; Vandewalle, J; Tan, S
1993-03-01
This paper tries to demonstrate how a heuristic neural control approach can be used to solve a complex nonlinear control problem. The control task is to swing up a pendulum mounted on a cart from its stable position (vertically down) to the zero state (up right) and keep it there by applying a sequence of two opposing constant forces of equal magnitude to the mass center of the cart. In addition, the displacement of the cart itself is confined to within a preset limit during the swinging up action and it will eventually be brought to the origin of the track. This is truly a nontrivial nonlinear regulation problem and is considerably difficult compared to the pendulum balancing problem (and its variations) widely adopted as a benchmarking test system for neural controllers. Through the solution of this specific control problem, we try to illustrate a heuristic neural control approach with task decomposition, control rule extraction and neural net rule implementation as its basic elements. Specializing to the pendulum problem, the global control task is decomposed into subtasks namely pendulum positioning and cart positioning. Accordingly, three separate neural subcontrollers are designed to cater to the subtasks and their coordination, i.e., pendulum subcontroller (PSC), cart subcontroller (CSC) and the switching subcontroller (SSC). Each of the subcontrollers is designed based on the rules and guidelines obtained from the experiences of a human operator. The simulation result is included to show the actual performance of the controller.
Artificial Neural Network Based State Estimators Integrated into Kalmtool
DEFF Research Database (Denmark)
Bayramoglu, Enis; Ravn, Ole; Poulsen, Niels Kjølstad
2012-01-01
In this paper we present a toolbox enabling easy evaluation and comparison of dierent ltering algorithms. The toolbox is called Kalmtool and is a set of MATLAB tools for state estimation of nonlinear systems. The toolbox now contains functions for Articial Neural Network Based State Estimation...
On the Nature, Modeling, and Neural Bases of Social Ties
F.A.A.M. Winden, van (Frans); M. Stallen (Mirre); K.R. Ridderinkhof (Richard)
2008-01-01
textabstractThis paper addresses the nature, formalization, and neural bases of (affective) social ties and discusses the relevance of ties for health economics. A social tie is defined as an affective weight attached by an individual to the well-being of another individual (‘utility interdependence
Detecting danger labels with RAM-based neural networks
DEFF Research Database (Denmark)
Jørgensen, T.M.; Christensen, S.S.; Andersen, A.W.
1996-01-01
An image processing system for the automatic location of danger labels on the back of containers is presented. The system uses RAM-based neural networks to locate and classify labels after a pre-processing step involving specially designed non-linear edge filters and RGB-to-HSV conversion. Results...
Microprocessor-Based Neural-Pulse-Wave Analyzer
Kojima, G. K.; Bracchi, F.
1983-01-01
Microprocessor-based system analyzes amplitudes and rise times of neural waveforms. Displaying histograms of measured parameters helps researchers determine how many nerves contribute to signal and specify waveform characteristics of each. Results are improved noise rejection, full or partial separation of overlapping peaks, and isolation and identification of related peaks in different histograms. 2
Neural network-based retrieval from software reuse repositories
Eichmann, David A.; Srinivas, Kankanahalli
1992-01-01
A significant hurdle confronts the software reuser attempting to select candidate components from a software repository - discriminating between those components without resorting to inspection of the implementation(s). We outline an approach to this problem based upon neural networks which avoids requiring the repository administrators to define a conceptual closeness graph for the classification vocabulary.
Rolling Bearing Diagnosis Based on LMD and Neural Network
Directory of Open Access Journals (Sweden)
Baoshan Huang
2013-01-01
Full Text Available Inner ring pitting, the outer indentation and rolling element wear are typical faults of rolling bearing. In order to diagnose these faults rapidly and accurately, the paper proposes a novel diagnosis method of rolling bearing based on the energy characteristics of PF component and neural network by the vibration signal of local mean decomposition(Local mean decomposition, LMD. The vibration signal is decomposed into several PF components by the local mean decomposition, the calculated energy characteristics of the PF component are inputted to the neural network to identify the type of rolling bearing faults. At the same time, the genetic algorithm is introduced to optimize the structure parameters of neural network, which improves diagnostic rate and accuracy of faults. The results show that this method has a higher diagnosis and recognition rate for the typical faults of rolling bearing.
Hazardous Odor Recognition by CMAC Based Neural Networks
Directory of Open Access Journals (Sweden)
Bekir Karlık
2009-09-01
Full Text Available Electronic noses are being developed as systems for the automated detection and classification of odors, vapors, and gases. Artificial neural networks (ANNs have been used to analyze complex data and to recognize patterns, and have shown promising results in recognition of volatile compounds and odors in electronic nose applications. When an ANN is combined with a sensor array, the number of detectable chemicals is generally greater than the number of unique sensor types. The odor sensing system should be extended to new areas since its standard style where the output pattern from multiple sensors with partially overlapped specificity is recognized by a neural network or multivariate analysis. This paper describes the design, implementation and performance evaluations of the application developed for hazardous odor recognition using Cerebellar Model Articulation Controller (CMAC based neural networks.
Techniques of Image Processing Based on Artificial Neural Networks
Institute of Scientific and Technical Information of China (English)
LI Wei-qing; WANG Qun; WANG Cheng-biao
2006-01-01
This paper presented an online quality inspection system based on artificial neural networks. Chromatism classification and edge detection are two difficult problems in glass steel surface quality inspection. Two artificial neural networks were made and the two problems were solved. The one solved chromatism classification. Hue,saturation and their probability of three colors, whose appearing probabilities were maximum in color histogram,were selected as input parameters, and the number of output node could be adjusted with the change of requirement. The other solved edge detection. In this neutral network, edge detection of gray scale image was able to be tested with trained neural networks for a binary image. It prevent the difficulty that the number of needed training samples was too large if gray scale images were directly regarded as training samples. This system is able to be applied to not only glass steel fault inspection but also other product online quality inspection and classification.
Hazardous Odor Recognition by CMAC Based Neural Networks.
Bucak, Ihsan Ömür; Karlık, Bekir
2009-01-01
Electronic noses are being developed as systems for the automated detection and classification of odors, vapors, and gases. Artificial neural networks (ANNs) have been used to analyze complex data and to recognize patterns, and have shown promising results in recognition of volatile compounds and odors in electronic nose applications. When an ANN is combined with a sensor array, the number of detectable chemicals is generally greater than the number of unique sensor types. The odor sensing system should be extended to new areas since its standard style where the output pattern from multiple sensors with partially overlapped specificity is recognized by a neural network or multivariate analysis. This paper describes the design, implementation and performance evaluations of the application developed for hazardous odor recognition using Cerebellar Model Articulation Controller (CMAC) based neural networks.
A new angle on the Euler angles
Markley, F. Landis; Shuster, Malcolm D.
1995-01-01
We present a generalization of the Euler angles to axes beyond the twelve conventional sets. The generalized Euler axes must satisfy the constraint that the first and the third are orthogonal to the second; but the angle between the first and third is arbitrary, rather than being restricted to the values 0 and pi/2, as in the conventional sets. This is the broadest generalization of the Euler angles that provides a representation of an arbitrary rotation matrix. The kinematics of the generalized Euler angles and their relation to the attitude matrix are presented. As a side benefit, the equations for the generalized Euler angles are universal in that they incorporate the equations for the twelve conventional sets of Euler angles in a natural way.
Boyd, John P
2010-01-01
When a function $f(x)$ is singular at a point $x_{s}$ on the real axis, its Fourier series, when truncated at the $N$-th term, gives a pointwise error of only $O(1/N)$ over the entire real axis. Such singularities spontaneously arise as "fronts" in meteorology and oceanography and "shocks" in other branches of fluid mechanics. It has been previously shown that it is possible to recover an exponential rate of convegence at all points away from the singularity in the sense that $|f(x) - f_{N}^{\\sigma}(x) | \\sim O(\\exp(- q(x) N))$ where $f_{N}^{\\sigma}(x)$ is the result of applying a filter or summability method to the partial sum $f_{N}(x)$ and $q(x)$ is a proportionality constant that is a function of $d(x) \\equiv |x-x_{s}|$, the distance from $x$ to the singularity. Here we give an elementary proof of great generality using conformal mapping in a dummy variable $z$; this is equivalent to applying the Euler acceleration. We show that $q(x) \\approx \\log(\\cos(d(x)/2))$ for the Euler filter when the Fourier perio...
An improved front tracking method for the Euler equations
Witteveen, J.A.S.; Koren, B.; Bakker, P.G.
2007-01-01
An improved front tracking method for hyperbolic conservation laws is presented. The improved method accurately resolves discontinuities as well as continuous phenomena. The method is based on an improved front interaction model for a physically more accurate modeling of the Euler equations, as comp
Two Identities for the Bernoulli-Euler Numbers
Gauthier, N.
2008-01-01
Two identities for the Bernoulli and for the Euler numbers are derived. These identities involve two special cases of central combinatorial numbers. The approach is based on a set of differential identities for the powers of the secant. Generalizations of the Mittag-Leffler series for the secant are introduced and used to obtain closed-form…
Euler's Identity, Leibniz Tables, and the Irrationality of Pi
Jones, Timothy W.
2012-01-01
Using techniques that show that e and pi are transcendental, we give a short, elementary proof that pi is irrational based on Euler's identity. The proof involves evaluations of a polynomial using repeated applications of Leibniz formula as organized in a Leibniz table.
Euler potentials for the MHD Kamchatnov-Hopf soliton solution
Semenov, VS; Korovinski, DB; Biernat, HK
2002-01-01
In the MHD description of plasma phenomena the concept of magnetic helicity turns out to be very useful. We present here an example of introducing Euler potentials into a topological MHD soliton which has non-trivial helicity. The MHD soliton solution (Kamchatnov, 1982) is based on the Hopf invarian
Recursive Neural Networks Based on PSO for Image Parsing
2013-01-01
This paper presents an image parsing algorithm which is based on Particle Swarm Optimization (PSO) and Recursive Neural Networks (RNNs). State-of-the-art method such as traditional RNN-based parsing strategy uses L-BFGS over the complete data for learning the parameters. However, this could cause problems due to the nondifferentiable objective function. In order to solve this problem, the PSO algorithm has been employed to tune the weights of RNN for minimizing the objective. Experimental res...
Quantum Neural Network Based Machine Translator for Hindi to English
Ravi Narayan; Singh, V. P.; S. Chakraverty
2014-01-01
This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze t...
Spoon, Corrie; Grant, Wally
2011-03-01
Vestibular hair cell bundles in the inner ear contain a single kinocilium composed of a 9+2 microtubule structure. Kinocilia play a crucial role in transmitting movement of the overlying mass, otoconial membrane or cupula to the mechanotransducing portion of the hair cell bundle. Little is known regarding the mechanical deformation properties of the kinocilium. Using a force-deflection technique, we measured two important mechanical properties of kinocilia in the utricle of a turtle, Trachemys (Pseudemys) scripta elegans. First, we measured the stiffness of kinocilia with different heights. These kinocilia were assumed to be homogenous cylindrical rods and were modeled as both isotropic Euler-Bernoulli beams and transversely isotropic Timoshenko beams. Two mechanical properties of the kinocilia were derived from the beam analysis: flexural rigidity (EI) and shear rigidity (kGA). The Timoshenko model produced a better fit to the experimental data, predicting EI=10,400 pN μm(2) and kGA=247 pN. Assuming a homogenous rod, the shear modulus (G=1.9 kPa) was four orders of magnitude less than Young's modulus (E=14.1 MPa), indicating that significant shear deformation occurs within deflected kinocilia. When analyzed as an Euler-Bernoulli beam, which neglects translational shear, EI increased linearly with kinocilium height, giving underestimates of EI for shorter kinocilia. Second, we measured the rotational stiffness of the kinocilium insertion (κ) into the hair cell's apical surface. Following BAPTA treatment to break the kinocilial links, the kinocilia remained upright, and κ was measured as 177±47 pN μm rad(-1). The mechanical parameters we quantified are important for understanding how forces arising from head movement are transduced and encoded by hair cells.
Hand Gesture and Neural Network Based Human Computer Interface
Directory of Open Access Journals (Sweden)
Aekta Patel
2014-06-01
Full Text Available Computer is used by every people either at their work or at home. Our aim is to make computers that can understand human language and can develop a user friendly human computer interfaces (HCI. Human gestures are perceived by vision. The research is for determining human gestures to create an HCI. Coding of these gestures into machine language demands a complex programming algorithm. In this project, We have first detected, recognized and pre-processing the hand gestures by using General Method of recognition. Then We have found the recognized image’s properties and using this, mouse movement, click and VLC Media player controlling are done. After that we have done all these functions thing using neural network technique and compared with General recognition method. From this we can conclude that neural network technique is better than General Method of recognition. In this, I have shown the results based on neural network technique and comparison between neural network method & general method.
Neural Network Predictive Control Based Power System Stabilizer
Directory of Open Access Journals (Sweden)
Ali Mohamed Yousef
2012-04-01
Full Text Available The present study investigates the power system stabilizer based on neural predictive control for improving power system dynamic performance over a wide range of operating conditions. In this study a design and application of the Neural Network Model Predictive Controller (NN-MPC on a simple power system composed of a synchronous generator connected to an infinite bus through a transmission line is proposed. The synchronous machine is represented in detail, taking into account the effect of the machine saliency and the damper winding. Neural network model predictive control combines reliable prediction of neural network model with excellent performance of model predictive control using nonlinear Levenberg-Marquardt optimization. This control system is used the rotor speed deviation as a feedback signal. Furthermore, the using performance system of the proposed controller is compared with the system performance using conventional one (PID controller through simulation studies. Digital simulation has been carried out in order to validate the effectiveness proposed NN-MPC power system stabilizer for achieving excellent performance. The results demonstrate that the effectiveness and superiority of the proposed controller in terms of fast response and small settling time.
UNMANNED AIR VEHICLE STABILIZATION BASED ON NEURAL NETWORK REGULATOR
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S. S. Andropov
2016-09-01
Full Text Available A problem of stabilizing for the multirotor unmanned aerial vehicle in an environment with external disturbances is researched. A classic proportional-integral-derivative controller is analyzed, its flaws are outlined: inability to respond to changing of external conditions and the need for manual adjustment of coefficients. The paper presents an adaptive adjustment method for coefficients of the proportional-integral-derivative controller based on neural networks. A neural network structure, its input and output data are described. Neural networks with three layers are used to create an adaptive stabilization system for the multirotor unmanned aerial vehicle. Training of the networks is done with the back propagation method. Each neural network produces regulator coefficients for each angle of stabilization as its output. A method for network training is explained. Several graphs of transition process on different stages of learning, including processes with external disturbances, are presented. It is shown that the system meets stabilization requirements with sufficient number of iterations. Described adjustment method for coefficients can be used in remote control of unmanned aerial vehicles, operating in the changing environment.
Euler-Heisenberg lagrangian through Krein regularization
Refaei, A
2013-01-01
The Euler-Heisenberg effective action at the one-loop for a constant electromagnetic field is derived in Krein space quantization with Ford's idea of uctuated light-cone. In this work we present a perturbative, but convergent solution of the effective action. Without using any renormalization procedure, the result coincides with the famous renormalized Euler-Heisenberg action.
Euler and His Contribution Number Theory
Len, Amy; Scott, Paul
2004-01-01
Born in 1707, Leonhard Euler was the son of a Protestant minister from the vicinity of Basel, Switzerland. With the aim of pursuing a career in theology, Euler entered the University of Basel at the age of thirteen, where he was tutored in mathematics by Johann Bernoulli (of the famous Bernoulli family of mathematicians). He developed an interest…
Multiple Twisted -Euler Numbers and Polynomials Associated with -Adic -Integrals
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Jang Lee-Chae
2008-01-01
Full Text Available By using -adic -integrals on , we define multiple twisted -Euler numbers and polynomials. We also find Witt's type formula for multiple twisted -Euler numbers and discuss some characterizations of multiple twisted -Euler Zeta functions. In particular, we construct multiple twisted Barnes' type -Euler polynomials and multiple twisted Barnes' type -Euler Zeta functions. Finally, we define multiple twisted Dirichlet's type -Euler numbers and polynomials, and give Witt's type formula for them.
Recursive Neural Networks Based on PSO for Image Parsing
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Guo-Rong Cai
2013-01-01
Full Text Available This paper presents an image parsing algorithm which is based on Particle Swarm Optimization (PSO and Recursive Neural Networks (RNNs. State-of-the-art method such as traditional RNN-based parsing strategy uses L-BFGS over the complete data for learning the parameters. However, this could cause problems due to the nondifferentiable objective function. In order to solve this problem, the PSO algorithm has been employed to tune the weights of RNN for minimizing the objective. Experimental results obtained on the Stanford background dataset show that our PSO-based training algorithm outperforms traditional RNN, Pixel CRF, region-based energy, simultaneous MRF, and superpixel MRF.
ADAPTATIVE IMAGE WATERMARKING SCHEME BASED ON NEURAL NETWORK
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BASSEL SOLAIMANE
2011-01-01
Full Text Available Digital image watermarking has been proposed as a method to enhance medical data security, confidentiality and integrity. Medical image watermarking requires extreme care when embedding additional data, given their importance to clinical diagnosis, treatment, and research. In this paper, a novel image watermarking approach based on the human visual system (HVS model and neural network technique is proposed. The watermark was inserted into the middle frequency coefficients of the cover image’s blocked DCT based transform domain. In order to make the watermark stronger and less susceptible to different types of attacks, it is essential to find the maximum amount of interested watermark before the watermark becomes visible. In this paper, neural networks are used to implement an automated system of creating maximum-strength watermarks. The experimental results show that such method can survive of common image processing operations and has good adaptability for automated watermark embedding.
Fuzzy neural network image filter based on GA
Institute of Scientific and Technical Information of China (English)
刘涵; 刘丁; 李琦
2004-01-01
A new nonlinear image filter using fuzzy neural network based on genetic algorithm is proposed. The learning of network parameters is performed by genetic algorithm with the efficient binary encoding scheme. In the following,fuzzy reasoning embedded in the network aims at restoring noisy pixels without degrading the quality of fine details. It is shown by experiments that the filter is very effective in removing impulse noise and significantly outperforms conventional filters.
CONTROL SCHEMES FOR CMAC NEURAL NETWORK-BASED VISUAL SERVOING
Institute of Scientific and Technical Information of China (English)
Wang Huaming; Xi Wenming; Zhu Jianying
2003-01-01
In IBVS (image based visual servoing), the error signal in image space should be transformed into the control signal in the input space quickly. To avoid the iterative adjustment and complicated inverse solution of image Jacobian, CMAC (cerebellar model articulation controller) neural network is inserted into visual servo control loop to implement the nonlinear mapping. Two control schemes are used. Simulation results on two schemes are provided, which show a better tracking precision and stability can be achieved using scheme 2.
Genuinely Multidimensional Kinetic Scheme For Euler Equations
Tiwari, Praveer
2015-01-01
A new framework based on Boltzmann equation which is genuinely multidimensional and mesh-less is developed for solving Euler's equations. The idea is to use the method of moment of Boltzmann equation to operate in multidimensions using polar coordinates. The aim is to develop a framework which is genuinely multidimensional and can be implemented with different methodologies, no matter whether it is in finite difference, finite volume or finite element form. There is a considerable improvement in capturing shocks and other discontinuities. Also, since the method is multidimensional, the flow features are captured isotropically. The method is further extended to second order using 'Arc of Approach' concept. The framework is developed as a finite difference method (called as GINEUS) and is tested on the benchmark test cases. The results are compared against Kinetic Flux Vector Splitting Method.
Data systems and computer science: Neural networks base R/T program overview
Gulati, Sandeep
1991-01-01
The research base, in the U.S. and abroad, for the development of neural network technology is discussed. The technical objectives are to develop and demonstrate adaptive, neural information processing concepts. The leveraging of external funding is also discussed.
Feature Selection for Neural Network Based Stock Prediction
Sugunnasil, Prompong; Somhom, Samerkae
We propose a new methodology of feature selection for stock movement prediction. The methodology is based upon finding those features which minimize the correlation relation function. We first produce all the combination of feature and evaluate each of them by using our evaluate function. We search through the generated set with hill climbing approach. The self-organizing map based stock prediction model is utilized as the prediction method. We conduct the experiment on data sets of the Microsoft Corporation, General Electric Co. and Ford Motor Co. The results show that our feature selection method can improve the efficiency of the neural network based stock prediction.
Using fuzzy logic to integrate neural networks and knowledge-based systems
Yen, John
1991-01-01
Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems.
Neural Net Gains Estimation Based on an Equivalent Model
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Karen Alicia Aguilar Cruz
2016-01-01
Full Text Available A model of an Equivalent Artificial Neural Net (EANN describes the gains set, viewed as parameters in a layer, and this consideration is a reproducible process, applicable to a neuron in a neural net (NN. The EANN helps to estimate the NN gains or parameters, so we propose two methods to determine them. The first considers a fuzzy inference combined with the traditional Kalman filter, obtaining the equivalent model and estimating in a fuzzy sense the gains matrix A and the proper gain K into the traditional filter identification. The second develops a direct estimation in state space, describing an EANN using the expected value and the recursive description of the gains estimation. Finally, a comparison of both descriptions is performed; highlighting the analytical method describes the neural net coefficients in a direct form, whereas the other technique requires selecting into the Knowledge Base (KB the factors based on the functional error and the reference signal built with the past information of the system.
Neural Net Gains Estimation Based on an Equivalent Model
Aguilar Cruz, Karen Alicia; Medel Juárez, José de Jesús; Fernández Muñoz, José Luis; Esmeralda Vigueras Velázquez, Midory
2016-01-01
A model of an Equivalent Artificial Neural Net (EANN) describes the gains set, viewed as parameters in a layer, and this consideration is a reproducible process, applicable to a neuron in a neural net (NN). The EANN helps to estimate the NN gains or parameters, so we propose two methods to determine them. The first considers a fuzzy inference combined with the traditional Kalman filter, obtaining the equivalent model and estimating in a fuzzy sense the gains matrix A and the proper gain K into the traditional filter identification. The second develops a direct estimation in state space, describing an EANN using the expected value and the recursive description of the gains estimation. Finally, a comparison of both descriptions is performed; highlighting the analytical method describes the neural net coefficients in a direct form, whereas the other technique requires selecting into the Knowledge Base (KB) the factors based on the functional error and the reference signal built with the past information of the system. PMID:27366146
Research on Transformer Fault Based on Probabilistic Neural Network
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Li Yingshun
2015-01-01
Full Text Available With the development of computer science and technology, and increasingly intelligent industrial production, the application of big data in industry also advances rapidly, and the development of artificial intelligence in the aspect of fault diagnosis is particularly prominent. On the basis of MATLAB platform, this paper constructs a fault diagnosis expert system of artificial intelligence machine based on the probabilistic neural network, and it also carries out a simulation of production process by the use of bionic algorithm. This paper makes a diagnosis of transformer fault by the use of an expert system developed by this paper, and verifies that the probabilistic neural network has a good convergence, fault-tolerant ability and big data handling capability in the fault diagnosis. It is suitable for industrial production, which can provide a reliable mathematical model for the construction of fault diagnosis expert system in the industrial production.
Parametric Jominy profiles predictor based on neural networks
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Valentini, R.
2005-12-01
Full Text Available The paper presents a method for the prediction of the Jominy hardness profiles of steels for microalloyed Boron steel which is based on neural networks. The Jominy profile has been parameterized and the parameters, which are a sort of "compact representation" of the profile itself, are linked to the steel chemical composition through a neural network. Numerical results are presented and discussed.
El trabajo presenta un método de estimación de perfiles de dureza Jominy para aceros microaleados al boro basado en redes neuronales. Los parámetros de perfil Jominy, que constituyen una especie de "representación compacta" del perfil mismo, son determinados y puestos en relación con la composición química del acero mediante una red neuronal. Los resultados numéricos son expuestos y discutidos.
Neural network based dynamic controllers for industrial robots.
Oh, S Y; Shin, W C; Kim, H G
1995-09-01
The industrial robot's dynamic performance is frequently measured by positioning accuracy at high speeds and a good dynamic controller is essential that can accurately compute robot dynamics at a servo rate high enough to ensure system stability. A real-time dynamic controller for an industrial robot is developed here using neural networks. First, an efficient time-selectable hidden layer architecture has been developed based on system dynamics localized in time, which lends itself to real-time learning and control along with enhanced mapping accuracy. Second, the neural network architecture has also been specially tuned to accommodate servo dynamics. This not only facilitates the system design through reduced sensing requirements for the controller but also enhances the control performance over the control architecture neglecting servo dynamics. Experimental results demonstrate the controller's excellent learning and control performances compared with a conventional controller and thus has good potential for practical use in industrial robots.
Neighborhood based Levenberg-Marquardt algorithm for neural network training.
Lera, G; Pinzolas, M
2002-01-01
Although the Levenberg-Marquardt (LM) algorithm has been extensively applied as a neural-network training method, it suffers from being very expensive, both in memory and number of operations required, when the network to be trained has a significant number of adaptive weights. In this paper, the behavior of a recently proposed variation of this algorithm is studied. This new method is based on the application of the concept of neural neighborhoods to the LM algorithm. It is shown that, by performing an LM step on a single neighborhood at each training iteration, not only significant savings in memory occupation and computing effort are obtained, but also, the overall performance of the LM method can be increased.
Distribution network planning algorithm based on Hopfield neural network
Institute of Scientific and Technical Information of China (English)
GAO Wei-xin; LUO Xian-jue
2005-01-01
This paper presents a new algorithm based on Hopfield neural network to find the optimal solution for an electric distribution network. This algorithm transforms the distribution power network-planning problem into a directed graph-planning problem. The Hopfield neural network is designed to decide the in-degree of each node and is in combined application with an energy function. The new algorithm doesn't need to code city streets and normalize data, so the program is easier to be realized. A case study applying the method to a district of 29 street proved that an optimal solution for the planning of such a power system could be obtained by only 26 iterations. The energy function and algorithm developed in this work have the following advantages over many existing algorithms for electric distribution network planning: fast convergence and unnecessary to code all possible lines.
XDANNG: XML based Distributed Artificial Neural Network with Globus Toolkit
Mahini, Hamidreza; Ghofrani, Javad
2009-01-01
Artificial Neural Network is one of the most common AI application fields. This field has direct and indirect usages most sciences. The main goal of ANN is to imitate biological neural networks for solving scientific problems. But the level of parallelism is the main problem of ANN systems in comparison with biological systems. To solve this problem, we have offered a XML-based framework for implementing ANN on the Globus Toolkit Platform. Globus Toolkit is well known management software for multipurpose Grids. Using the Grid for simulating the neuron network will lead to a high degree of parallelism in the implementation of ANN. We have used the XML for improving flexibility and scalability in our framework.
Nanotechnology-Based Approaches for Guiding Neural Regeneration.
Shah, Shreyas; Solanki, Aniruddh; Lee, Ki-Bum
2016-01-19
The mammalian brain is a phenomenal piece of "organic machinery" that has fascinated scientists and clinicians for centuries. The intricate network of tens of billions of neurons dispersed in a mixture of chemical and biochemical constituents gives rise to thoughts, feelings, memories, and life as we know it. In turn, subtle imbalances or damage to this system can cause severe complications in physical, motor, psychological, and cognitive function. Moreover, the inevitable loss of nerve tissue caused by degenerative diseases and traumatic injuries is particularly devastating because of the limited regenerative capabilities of the central nervous system (i.e., the brain and spinal cord). Among current approaches, stem-cell-based regenerative medicine has shown the greatest promise toward repairing and regenerating destroyed neural tissue. However, establishing controlled and reliable methodologies to guide stem cell differentiation into specialized neural cells of interest (e.g., neurons and oligodendrocytes) has been a prevailing challenge in the field. In this Account, we summarize the nanotechnology-based approaches our group has recently developed to guide stem-cell-based neural regeneration. We focus on three overarching strategies that were adopted to selectively control this process. First, soluble microenvironmental factors play a critical role in directing the fate of stem cells. Multiple factors have been developed in the form of small-molecule drugs, biochemical analogues, and DNA/RNA-based vectors to direct neural differentiation. However, the delivery of these factors with high transfection efficiency and minimal cytotoxicity has been challenging, especially to sensitive cell lines such as stem cells. In our first approach, we designed nanoparticle-based systems for the efficient delivery of such soluble factors to control neural differentiation. Our nanoparticles, comprising either organic or inorganic elements, were biocompatible and offered
Wavelet neural network based fault diagnosis in nonlinear analog circuits
Institute of Scientific and Technical Information of China (English)
Yin Shirong; Chen Guangju; Xie Yongle
2006-01-01
The theories of diagnosing nonlinear analog circuits by means of the transient response testing are studied. Wavelet analysis is made to extract the transient response signature of nonlinear circuits and compress the signature dada. The best wavelet function is selected based on the between-category total scatter of signature. The fault dictionary of nonlinear circuits is constructed based on improved back-propagation(BP) neural network. Experimental results demonstrate that the method proposed has high diagnostic sensitivity and fast fault identification and deducibility.
Flexible and Highly Biocompatible Nanofiber-Based Electrodes for Neural Surface Interfacing.
Heo, Dong Nyoung; Kim, Han-Jun; Lee, Yi Jae; Heo, Min; Lee, Sang Jin; Lee, Donghyun; Do, Sun Hee; Lee, Soo Hyun; Kwon, Il Keun
2017-03-28
Polyimide (PI)-based electrodes have been widely used as flexible biosensors in implantable device applications for recording biological signals. However, the long-term quality of neural signals obtained from PI-based nerve electrodes tends to decrease due to nerve damage by neural tissue compression, mechanical mismatch, and insufficient fluid exchange between the neural tissue and electrodes. Here, we resolve these problems with a developed PI nanofiber (NF)-based nerve electrode for stable neural signal recording, which can be fabricated via electrospinning and inkjet printing. We demonstrate an NF-based nerve electrode that can be simply fabricated and easily applied due to its high permeability, flexibility, and biocompatibility. Furthermore, the electrode can record stable neural signals for extended periods of time, resulting in decreased mechanical mismatch, neural compression, and contact area. NF-based electrodes with highly flexible and body-fluid-permeable properties could enable future neural interfacing applications.
On Generalized Euler Spirals in E^3
Saracoglu, Semra
2012-01-01
The Cornu spirals on plane are the curves whose curvatures are linear. Generalized planar cornu spirals and Euler spirals in E^3, the curves whose curvatures are linear are defined in [1,5]. In this study, these curves are presented as the ratio of two rational linear functions. Also here, generalized Euler spirals in E^3 has been defined and given their some various characterizations. The approach I used in this paper is useful in understanding the role of Euler spirals in E^3 in differentia...
Neural Network Based Popularity Prediction For IPTV System
Directory of Open Access Journals (Sweden)
Jun Li
2012-12-01
Full Text Available Internet protocol television (IPTV, being an emerging Internet application, plays an important and indispensable role in our daily life. In order to maximize user experience and on the same time to minimize service cost, we must take into pay attention to how to reduce the storage and transport costs. A lot of previous work has been done before to do this. There is a challenging problem in this: how to predict the popularities of videos as accurate as possible. To solve the problem, this paper presents a Neural Network model for the popularity prediction of the programs in the IPTV system. And we use the actual historical logs to validate our method. The historical logs are divided to two parts, one is used to train the neural network by extract input/output vectors, and the other part is used to verify the model. The experimental results from our validation show the Neural Network based method can gain better accuracy than the comparative method.
Compact Lie groups: Euler constructions and generalized Dyson conjecture
Cacciatori, S L; Scotti, A
2012-01-01
In this paper we present a very general method to construct generalized Euler parameterizations for compact simple Lie groups w.r.t. maximally symmetrically embedded simple Lie groups. Our construction is based on a detailed analysis of the geometry of these groups, which moreover gives rise to an interesting connection with certain generalized Dyson integrals. In particular, we obtain a geometry based proof of the generalized Macdonald conjecture correspondent to the root systems associated to all irreducible symmetric spaces.
Fuzzy Neural Network Based Traffic Prediction and Congestion Control in High-Speed Networks
Institute of Scientific and Technical Information of China (English)
费翔; 何小燕; 罗军舟; 吴介一; 顾冠群
2000-01-01
Congestion control is one of the key problems in high-speed networks, such as ATM. In this paper, a kind of traffic prediction and preventive congestion control scheme is proposed using neural network approach. Traditional predictor using BP neural network has suffered from long convergence time and dissatisfying error. Fuzzy neural network developed in this paper can solve these problems satisfactorily. Simulations show the comparison among no-feedback control scheme,reactive control scheme and neural network based control scheme.
Directory of Open Access Journals (Sweden)
Carlos López-Franco
2015-01-01
Full Text Available We present an inverse optimal neural controller for a nonholonomic mobile robot with parameter uncertainties and unknown external disturbances. The neural controller is based on a discrete-time recurrent high order neural network (RHONN trained with an extended Kalman filter. The reference velocities for the neural controller are obtained with a visual sensor. The effectiveness of the proposed approach is tested by simulations and real-time experiments.
Convolutional Neural Network Based Fault Detection for Rotating Machinery
Janssens, Olivier; Slavkovikj, Viktor; Vervisch, Bram; Stockman, Kurt; Loccufier, Mia; Verstockt, Steven; Van de Walle, Rik; Van Hoecke, Sofie
2016-09-01
Vibration analysis is a well-established technique for condition monitoring of rotating machines as the vibration patterns differ depending on the fault or machine condition. Currently, mainly manually-engineered features, such as the ball pass frequencies of the raceway, RMS, kurtosis an crest, are used for automatic fault detection. Unfortunately, engineering and interpreting such features requires a significant level of human expertise. To enable non-experts in vibration analysis to perform condition monitoring, the overhead of feature engineering for specific faults needs to be reduced as much as possible. Therefore, in this article we propose a feature learning model for condition monitoring based on convolutional neural networks. The goal of this approach is to autonomously learn useful features for bearing fault detection from the data itself. Several types of bearing faults such as outer-raceway faults and lubrication degradation are considered, but also healthy bearings and rotor imbalance are included. For each condition, several bearings are tested to ensure generalization of the fault-detection system. Furthermore, the feature-learning based approach is compared to a feature-engineering based approach using the same data to objectively quantify their performance. The results indicate that the feature-learning system, based on convolutional neural networks, significantly outperforms the classical feature-engineering based approach which uses manually engineered features and a random forest classifier. The former achieves an accuracy of 93.61 percent and the latter an accuracy of 87.25 percent.
Willführ, Alper; Brandenberger, Christina; Piatkowski, Tanja; Grothausmann, Roman; Nyengaard, Jens Randel; Ochs, Matthias; Mühlfeld, Christian
2015-12-01
The lung parenchyma provides a maximal surface area of blood-containing capillaries that are in close contact with a large surface area of the air-containing alveoli. Volume and surface area of capillaries are the classic stereological parameters to characterize the alveolar capillary network (ACN) and have provided essential structure-function information of the lung. When loss (rarefaction) or gain (angiogenesis) of capillaries occurs, these parameters may not be sufficient to provide mechanistic insight. Therefore, it would be desirable to estimate the number of capillaries, as it contains more distinct and mechanistically oriented information. Here, we present a new stereological method to estimate the number of capillary loops in the ACN. One advantage of this method is that it is independent of the shape, size, or distribution of the capillaries. We used consecutive, 1 μm-thick sections from epoxy resin-embedded material as a physical disector. The Euler-Poincaré characteristic of capillary networks can be estimated by counting the easily recognizable topological constellations of "islands," "bridges," and "holes." The total number of capillary loops in the ACN can then be calculated from the Euler-Poincaré characteristic. With the use of the established estimator of alveolar number, it is possible to obtain the mean number of capillary loops per alveolus. In conclusion, estimation of alveolar capillaries by design-based stereology is an efficient and unbiased method to characterize the ACN and may be particularly useful for studies on emphysema, pulmonary hypertension, or lung development.
Content Based Image Retrieval : Classification Using Neural Networks
Directory of Open Access Journals (Sweden)
Shereena V.B
2014-10-01
Full Text Available In a content-based image retrieval system (CBIR, the main issue is to extract the image features that effectively represent the image contents in a database. Such an extraction requires a detailed evaluation of retrieval performance of image features. This paper presents a review of fundamental aspects of content based image retrieval including feature extraction of color and texture features. Commonly used color features including color moments, color histogram and color correlogram and Gabor texture are compared. The paper reviews the increase in efficiency of image retrieval when the color and texture features are combined. The similarity measures based on which matches are made and images are retrieved are also discussed. For effective indexing and fast searching of images based on visual features, neural network based pattern learning can be used to achieve effective classification.
Content Based Image Retrieval : Classification Using Neural Networks
Directory of Open Access Journals (Sweden)
Shereena V.B
2014-11-01
Full Text Available In a content-based image retrieval system (CBIR, the main issue is to extract the image features that effectively represent the image contents in a database. Such an extraction requires a detailed evaluation of retrieval performance of image features. This paper presents a review of fundamental aspects of content based image retrieval including feature extraction of color and texture features. Commonly used color features including color moments, color histogram and color correlogram and Gabor texture are compared. The paper reviews the increase in efficiency of image retrieval when the color and texture features are combined. The similarity measures based on which matches are made and images are retrieved are also discussed. For effective indexing and fast searching of images based on visual features, neural network based pattern learning can be used to achieve effective classification.
Neural Network approach for image retrieval based on preference elicitation
Directory of Open Access Journals (Sweden)
B.Veera Jyothi,
2010-07-01
Full Text Available Multimedia technologies have been developing rapidly over the last few years and have yielded a large number of databases containing graphical documents. Tools for content-based search of graphical objects have been the subject of intensive research, but their performance is still unsatisfactory for many applications, opening up afield for further research and technology development. Up till now, all popular Internet search engines have been only text-based, including those that search for images. In this paper We propose an image retrieval system based on neural networks. The advantage of using the neural network is that the amount of semantic gap can be reduced when compared to other techniques which may be based onclustering. The methodology proposed below is designed for a specific class of objects, which can be broken down into subobjects in such a way that the main object can be classified by shape, color distribution and texture of the sub objects and the spatial spatial relations between the sub-objects in a 2- dimensional image. We also assume that translation, scaling and 2D rotation do not change the class of the object, but we do not consider 3Dtransformation.Therefore, photos of the same 3D object from different positions for example are considered to be objects belonging to different class.
Neural Network Based Montioring and Control of Fluidized Bed.
Energy Technology Data Exchange (ETDEWEB)
Bodruzzaman, M.; Essawy, M.A.
1996-04-01
The goal of this project was to develop chaos analysis and neural network-based modeling techniques and apply them to the pressure-drop data obtained from the Fluid Bed Combustion (FBC) system (a small scale prototype model) located at the Federal Energy Technology Center (FETC)-Morgantown. The second goal was to develop neural network-based chaos control techniques and provide a suggestive prototype for possible real-time application to the FBC system. The experimental pressure data were collected from a cold FBC experimental set-up at the Morgantown Center. We have performed several analysis on these data in order to unveil their dynamical and chaotic characteristics. The phase-space attractors were constructed from the one dimensional time series data, using the time-delay embedding method, for both normal and abnormal conditions. Several identifying parameters were also computed from these attractors such as the correlation dimension, the Kolmogorov entropy, and the Lyapunov exponents. These chaotic attractor parameters can be used to discriminate between the normal and abnormal operating conditions of the FBC system. It was found that, the abnormal data has higher correlation dimension, larger Kolmogorov entropy and larger positive Lyapunov exponents as compared to the normal data. Chaotic system control using neural network based techniques were also investigated and compared to conventional chaotic system control techniques. Both types of chaotic system control techniques were applied to some typical chaotic systems such as the logistic, the Henon, and the Lorenz systems. A prototype model for real-time implementation of these techniques has been suggested to control the FBC system. These models can be implemented for real-time control in a next phase of the project after obtaining further measurements from the experimental model. After testing the control algorithms developed for the FBC model, the next step is to implement them on hardware and link them to
Black holes with constant topological Euler density
Bargueño, Pedro
2016-01-01
A class of four dimensional spherically symmetric and static geometries with constant topological Euler density is studied. These geometries are shown to solve the coupled Einstein-Maxwell system when non-linear Born-Infeld-like electrodynamics is employed.
Implementation of pattern recognition algorithm based on RBF neural network
Bouchoux, Sophie; Brost, Vincent; Yang, Fan; Grapin, Jean Claude; Paindavoine, Michel
2002-12-01
In this paper, we present implementations of a pattern recognition algorithm which uses a RBF (Radial Basis Function) neural network. Our aim is to elaborate a quite efficient system which realizes real time faces tracking and identity verification in natural video sequences. Hardware implementations have been realized on an embedded system developed by our laboratory. This system is based on a DSP (Digital Signal Processor) TMS320C6x. The optimization of implementations allow us to obtain a processing speed of 4.8 images (240x320 pixels) per second with a correct rate of 95% of faces tracking and identity verification.
SEGMENTATION OF RANGE IMAGE BASED ON KOHONEN NEURAL NETWORK
Institute of Scientific and Technical Information of China (English)
Zou Ning; Liu Jian; Zhou Manli; Li Qing
2001-01-01
This paper presents an unsupervised range image segmentation based on Kohonen neural network. At first, the derivative and partial derivative of each point are calculated and the normal in each points is gotten. With the character vectors including normal and range value, self-organization map is introduced to cluster. The normal analysis is used to eliminate over-segmentation and the last result is gotten. This method avoid selecting original seeds and uses fewer samples, moreover computes rapidly. The experiment shows the better performance.
Fast neural-net based fake track rejection
De Cian, Michel; Seyfert, Paul; Stahl, Sascha
2017-01-01
A neural-network based algorithm to identify fake tracks in the LHCb pattern recognition is presented. This algorithm, called ghost probability, is fast enough to fit into the CPU time budget of the software trigger farm. It allows reducing the fake rate and consequently the combinatorics of the decay reconstructions, as well as the number of tracks that need to be processed by the particle identification algorithms. As a result, it strongly contributes to the achievement of having the same reconstruction online and offline in the LHCb experiment.
An Improved Minimum Distance Method Based on Artificial Neural Networks
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
MDM (minimum distance method) is a very popular algorithm in state recognition. But it has a presupposition, that is, the distance within one class must be shorter enough than the distance between classes. When this presupposition is not satisfied, the method is no longer valid. In order to overcome the shortcomings of MDM, an improved mi nimum distance method (IMDM) based on ANN (artificial neural networks) is presented. The simulation results demonstrate that IMDM has two advantages, that is, the rate of recognition is faster and the accuracy of recognition is higher compared with MDM.
Pulse frequency classification based on BP neural network
Institute of Scientific and Technical Information of China (English)
WANG Rui; WANG Xu; YANG Dan; FU Rong
2006-01-01
In Traditional Chinese Medicine (TCM), it is an important parameter of the clinic disease diagnosis to analysis the pulse frequency. This article accords to pulse eight major essentials to identify pulse type of the pulse frequency classification based on back-propagation neural networks (BPNN). The pulse frequency classification includes slow pulse, moderate pulse, rapid pulse etc. By feature parameter of the pulse frequency analysis research and establish to identify system of pulse frequency features. The pulse signal from detecting system extracts period, frequency etc feature parameter to compare with standard feature value of pulse type. The result shows that identify-rate attains 92.5% above.
Fuzzy neural network based on a Sigmoid chaotic neuron
Institute of Scientific and Technical Information of China (English)
Zhang Yi; Wang Xing-Yuan
2012-01-01
The theories of intelligent information processing are urgently needed for the rapid development of modem science.In this paper,a novel fuzzy chaotic neural network,which is the combination of fuzzy logic system,artificial neuralnetwork system,and chaotic system,is proposed.We design its model structure which is based on the Sigmoid map,derive its mathematical model,and analyse its chaotic characteristics.Finally the relationship between the accuracy of map and the membership function is illustrated by simulation.
Star pattern recognition method based on neural network
Institute of Scientific and Technical Information of China (English)
LI Chunyan; LI Ke; ZHANG Longyun; JIN Shengzhen; ZU Jifeng
2003-01-01
Star sensor is an avionics instrument used to provide the absolute 3-axis attitude of a spacecraft by utilizing star observations. The key function is to recognize the observed stars by comparing them with the reference catalogue. Autonomous star pattern recognition requires that similar patterns can be distinguished from each other with a small training set. Therefore, a new method based on neural network technology is proposed and a recognition system containing parallel backpropagation (BP) multi-subnets is designed. The simulation results show that the method performs much better than traditional algorithms and the proposed system can achieve both higher recognition accuracy and faster recognition speed.
ARTIFICIAL NEURAL NETWORKS BASED GEARS MATERIAL SELECTION HYBRID INTELLIGENT SYSTEM
Institute of Scientific and Technical Information of China (English)
X.C. Li; W.X. Zhu; G. Chen; D.S. Mei; J. Zhang; K.M. Chen
2003-01-01
An artificial neural networks(ANNs) based gear material selection hybrid intelligent system is established by analyzing the individual advantages and weakness of expert system (ES) and ANNs and the applications in material select of them. The system mainly consists of tow parts: ES and ANNs. By being trained with much data samples,the back propagation (BP) ANN gets the knowledge of gear materials selection, and is able to inference according to user input. The system realizes the complementing of ANNs and ES. Using this system, engineers without materials selection experience can conveniently deal with gear materials selection.
Pseudo Random Number Generator Based on Back Propagation Neural Network
Institute of Scientific and Technical Information of China (English)
WANG Bang-ju; WANG Yu-hua; NIU Li-ping; ZHANG Huan-guo
2007-01-01
Random numbers play an increasingly important role in secure wire and wireless communication.Thus the design quality of random number generator(RNG) is significant in information security.A novel pseudo RNG is proposed for improving the security of network communication.The back propagation neural network(BPNN) is nonlinear,which can be used to improve the traditional RNG.The novel pseudo RNG is based on BPNN techniques.The result of test suites standardized by the U.S shows that the RNG can satisfy the security of communication.
Structural Health Monitoring Using Neural Network Based Vibrational System Identification
Sofge, Donald A
2007-01-01
Composite fabrication technologies now provide the means for producing high-strength, low-weight panels, plates, spars and other structural components which use embedded fiber optic sensors and piezoelectric transducers. These materials, often referred to as smart structures, make it possible to sense internal characteristics, such as delaminations or structural degradation. In this effort we use neural network based techniques for modeling and analyzing dynamic structural information for recognizing structural defects. This yields an adaptable system which gives a measure of structural integrity for composite structures.
Movement-Based Priming: Clinical Applications and Neural Mechanisms.
Stoykov, Mary Ellen; Corcos, Daniel Montie; Madhavan, Sangeetha
2017-01-01
Priming can be described as behavior change generated by preceding stimuli. Although various types of priming have been long studied in the field of psychology, priming that targets motor cortex is a relatively new topic of research in the fields of motor control and rehabilitation. In reference to a rehabilitation intervention, priming is categorized as a restorative approach. There are a myriad of possible priming approaches including noninvasive brain stimulation, motor imagery, and sensory-based priming, to name a few. The authors report on movement-based priming which, compared to other priming types, is less frequently examined and under reported. Movement-based priming includes, but is not limited to, bilateral motor priming, unilateral priming, and aerobic exercise. Clinical and neural mechanistic aspects of movement-based priming techniques are explored.
2014-01-01
valid OMB control number. 1. REPORT DATE 2014 2. REPORT TYPE 3. DATES COVERED 00-00-2014 to 00-00-2014 4. TITLE AND SUBTITLE A Polynomial-Based...solve Eq. (2.5) is kept under control by a sufficiently effective preconditioner. The RTB test case is an example of a dynamical scenario that can be run...program element 121670. We also would like to thank Michal Kopera and several anonymous reviewers for their helpful sug- gestions for improving the
Classification data mining method based on dynamic RBF neural networks
Zhou, Lijuan; Xu, Min; Zhang, Zhang; Duan, Luping
2009-04-01
With the widely application of databases and sharp development of Internet, The capacity of utilizing information technology to manufacture and collect data has improved greatly. It is an urgent problem to mine useful information or knowledge from large databases or data warehouses. Therefore, data mining technology is developed rapidly to meet the need. But DM (data mining) often faces so much data which is noisy, disorder and nonlinear. Fortunately, ANN (Artificial Neural Network) is suitable to solve the before-mentioned problems of DM because ANN has such merits as good robustness, adaptability, parallel-disposal, distributing-memory and high tolerating-error. This paper gives a detailed discussion about the application of ANN method used in DM based on the analysis of all kinds of data mining technology, and especially lays stress on the classification Data Mining based on RBF neural networks. Pattern classification is an important part of the RBF neural network application. Under on-line environment, the training dataset is variable, so the batch learning algorithm (e.g. OLS) which will generate plenty of unnecessary retraining has a lower efficiency. This paper deduces an incremental learning algorithm (ILA) from the gradient descend algorithm to improve the bottleneck. ILA can adaptively adjust parameters of RBF networks driven by minimizing the error cost, without any redundant retraining. Using the method proposed in this paper, an on-line classification system was constructed to resolve the IRIS classification problem. Experiment results show the algorithm has fast convergence rate and excellent on-line classification performance.
WELL-POSEDNESS OF INITIAL VALUE PROBLEM FOR EULER EQUATIONS OF INVISCID COMPRESSIBLE ADIABATIC FLUID
Institute of Scientific and Technical Information of China (English)
WANG Yue-peng
2005-01-01
The well-posedness of the initial value problem of the Euler equations was mainly discussed based on the stratification theory, and the necessary and sufficient conditions of well-posedness are presented for some representative initial or boundary value problem, thus the structure of solution space for local (exact) solution of the Euler equations is determined. Moreover the computation formulas of the analytical solution of the well-posed problem are also given.
Generating Functions for q-Apostol Type Frobenius–Euler Numbers and Polynomials
Directory of Open Access Journals (Sweden)
Yilmaz Simsek
2012-12-01
Full Text Available The aim of this paper is to construct generating functions, related to nonnegative real parameters, for q-Eulerian type polynomials and numbers (or q-Apostol type Frobenius–Euler polynomials and numbers. We derive some identities for these polynomials and numbers based on the generating functions and functional equations. We also give multiplication formula for the generalized Apostol type Frobenius–Euler polynomials.
Stability analysis of discrete-time BAM neural networks based on standard neural network models
Institute of Scientific and Technical Information of China (English)
ZHANG Sen-lin; LIU Mei-qin
2005-01-01
To facilitate stability analysis of discrete-time bidirectional associative memory (BAM) neural networks, they were converted into novel neural network models, termed standard neural network models (SNNMs), which interconnect linear dynamic systems and bounded static nonlinear operators. By combining a number of different Lyapunov functionals with S-procedure, some useful criteria of global asymptotic stability and global exponential stability of the equilibrium points of SNNMs were derived. These stability conditions were formulated as linear matrix inequalities (LMIs). So global stability of the discrete-time BAM neural networks could be analyzed by using the stability results of the SNNMs. Compared to the existing stability analysis methods, the proposed approach is easy to implement, less conservative, and is applicable to other recurrent neural networks.
WLAN indoor location method based on artificial neural network
Institute of Scientific and Technical Information of China (English)
Zhou Mu; Sun Ying; Xu Yubin; Deng Zhian; Meng Weixiao
2010-01-01
WLAN indoor location method based on artificial neural network (ANN) is analyzed.A three layer feed-forward ANN model offers the benefits of reducing time cost of the layout of an indoor location system, saving storage cost of the radio map establishment and enhancing real-time capacity in the on-line phase.According to the analysis of SNR distributions of recorded beacon signal samples and discussion about the multi-mode phenomenon, the one map method is proposed for the purpose of simplifying ANN input values and increasing location performances.Based on the simulations and comparison analysis with other two typical indoor location methods, K-nearest neighbor (KNN) and probability, the feasibility and effectiveness of ANN-based indoor location method are verified with average location error of 2.37m and location accuracy of 78.6% in 3m.
Wlan-Based Indoor Localization Using Neural Networks
Saleem, Fasiha; Wyne, Shurjeel
2016-07-01
Wireless indoor localization has generated recent research interest due to its numerous applications. This work investigates Wi-Fi based indoor localization using two variants of the fingerprinting approach. Specifically, we study the application of an artificial neural network (ANN) for implementing the fingerprinting approach and compare its localization performance with a probabilistic fingerprinting method that is based on maximum likelihood estimation (MLE) of the user location. We incorporate spatial correlation of fading into our investigations, which is often neglected in simulation studies and leads to erroneous location estimates. The localization performance is quantified in terms of accuracy, precision, robustness, and complexity. Multiple methods for handling the case of missing APs in online stage are investigated. Our results indicate that ANN-based fingerprinting outperforms the probabilistic approach for all performance metrics considered in this work.
Neural Network Based Intrusion Detection System for Critical Infrastructures
Energy Technology Data Exchange (ETDEWEB)
Todd Vollmer; Ondrej Linda; Milos Manic
2009-07-01
Resiliency and security in control systems such as SCADA and Nuclear plant’s in today’s world of hackers and malware are a relevant concern. Computer systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly improve the security of such systems. The IDS-NNM – Intrusion Detection System using Neural Network based Modeling, is presented in this paper. The main contributions of this work are: 1) the use and analyses of real network data (data recorded from an existing critical infrastructure); 2) the development of a specific window based feature extraction technique; 3) the construction of training dataset using randomly generated intrusion vectors; 4) the use of a combination of two neural network learning algorithms – the Error-Back Propagation and Levenberg-Marquardt, for normal behavior modeling. The presented algorithm was evaluated on previously unseen network data. The IDS-NNM algorithm proved to be capable of capturing all intrusion attempts presented in the network communication while not generating any false alerts.
VoIP attacks detection engine based on neural network
Safarik, Jakub; Slachta, Jiri
2015-05-01
The security is crucial for any system nowadays, especially communications. One of the most successful protocols in the field of communication over IP networks is Session Initiation Protocol. It is an open-source project used by different kinds of applications, both open-source and proprietary. High penetration and text-based principle made SIP number one target in IP telephony infrastructure, so security of SIP server is essential. To keep up with hackers and to detect potential malicious attacks, security administrator needs to monitor and evaluate SIP traffic in the network. But monitoring and following evaluation could easily overwhelm the security administrator in networks, typically in networks with a number of SIP servers, users and logically or geographically separated networks. The proposed solution lies in automatic attack detection systems. The article covers detection of VoIP attacks through a distributed network of nodes. Then the gathered data analyze aggregation server with artificial neural network. Artificial neural network means multilayer perceptron network trained with a set of collected attacks. Attack data could also be preprocessed and verified with a self-organizing map. The source data is detected by distributed network of detection nodes. Each node contains a honeypot application and traffic monitoring mechanism. Aggregation of data from each node creates an input for neural networks. The automatic classification on a centralized server with low false positive detection reduce the cost of attack detection resources. The detection system uses modular design for easy deployment in final infrastructure. The centralized server collects and process detected traffic. It also maintains all detection nodes.
Speech Recognition Method Based on Multilayer Chaotic Neural Network
Institute of Scientific and Technical Information of China (English)
REN Xiaolin; HU Guangrui
2001-01-01
In this paper,speech recognitionusing neural networks is investigated.Especially,chaotic dynamics is introduced to neurons,and a mul-tilayer chaotic neural network (MLCNN) architectureis built.A learning algorithm is also derived to trainthe weights of the network.We apply the MLCNNto speech recognition and compare the performanceof the network with those of recurrent neural net-work (RNN) and time-delay neural network (TDNN).Experimental results show that the MLCNN methodoutperforms the other neural networks methods withrespect to average recognition rate.
Application of Artificial Intelligence For Euler Solutions Clustering
Mikhailov, V.; Galdeano, A.; Diament, M.; Gvishiani, A.; Agayan, S.; Bogoutdinov, Sh.; Graeva, E.; Sailhac, P.
Results of Euler deconvolution strongly depend on the selection of viable solutions. Synthetic calculations using multiple causative sources show that Euler solutions clus- ter in the vicinity of causative bodies even when they do not group densely about perimeter of the bodies. We have developed a clustering technique to serve as a tool for selecting appropriate solutions. The method RODIN, employed in this study, is based on artificial intelligence and was originally designed for problems of classification of large data sets. It is based on a geometrical approach to study object concentration in a finite metric space of any dimension. The method uses a formal definition of cluster and includes free parameters that facilitate the search for clusters of given proper- ties. Test on synthetic and real data showed that the clustering technique successfully outlines causative bodies more accurate than other methods of discriminating Euler solutions. In complicated field cases such as the magnetic field in the Gulf of Saint Malo region (Brittany, France), the method provides geologically insightful solutions. Other advantages of the clustering method application are: - Clusters provide solutions associated with particular bodies or parts of bodies permitting the analysis of different clusters of Euler solutions separately. This may allow computation of average param- eters for individual causative bodies. - Those measurements of the anomalous field that yield clusters also form dense clusters themselves. The application of cluster- ing technique thus outlines areas where the influence of different causative sources is more prominent. This allows one to focus on areas for reinterpretation, using different window sizes, structural indices and so on.
Quantum Neural Network Based Machine Translator for Hindi to English
Directory of Open Access Journals (Sweden)
Ravi Narayan
2014-01-01
Full Text Available This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze the effectiveness of the proposed approach, 2600 sentences have been evaluated during simulation and evaluation. The accuracy achieved on BLEU score is 0.7502, on NIST score is 6.5773, on ROUGE-L score is 0.9233, and on METEOR score is 0.5456, which is significantly higher in comparison with Google Translation and Bing Translation for Hindi to English Machine Translation.
Manganese oxide microswitch for electronic memory based on neural networks
Ramesham, R.; Daud, T.; Moopenn, A.; Thakoor, A. P.; Khanna, S. K.
1989-01-01
A solid-state, resistance tailorable, programmable-once, binary, nonvolatile memory switch based on manganese oxide thin films is reported. MnO(x) exhibits irreversible memory switching from conducting (on) to insulating (off) state, with the off and on resistance ratio of greater than 10,000. The switching mechanism is current-triggered chemical transformation of a conductive MnO(2-Delta) to an insulating Mn2O3 state. The energy required for switching is of the order of 4-20 nJ/sq micron. The low switching energy, stability of the on and off states, and tailorability of the on state resistance make these microswitches well suited as programmable binary synapses in electronic associative memories based on neural network models.
Neural Network Based Parking via Google Map Guidance
Directory of Open Access Journals (Sweden)
A.Saranya
2015-02-01
Full Text Available Intelligent transportation systems (ITS focus to generate and spread creative services related to different transport modes for traffic management and hence enables the passenger informed about the traffic and to use the transport networks in a better way. Intelligent Trip Modeling System (ITMS uses machine learning to forecast the traveling speed profile for a selected route based on the traffic information available at the trip starting time. The intelligent Parking Information Guidance System provides an eminent Neural Network based intelligence system which provides automatic allocate ion of parking's through the Global Information system across the path of the users travel. In this project using efficient lookup table searches and a Lagrange-multiplier bisection search, Computational Optimized Allocation Algorithm converges faster to the optimal solution than existing techniques. The purpose of this project is to simulate and implement a real parking environment that allocates vacant parking slots using Allocation algorithm.
Quantum neural network based machine translator for Hindi to English.
Narayan, Ravi; Singh, V P; Chakraverty, S
2014-01-01
This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze the effectiveness of the proposed approach, 2600 sentences have been evaluated during simulation and evaluation. The accuracy achieved on BLEU score is 0.7502, on NIST score is 6.5773, on ROUGE-L score is 0.9233, and on METEOR score is 0.5456, which is significantly higher in comparison with Google Translation and Bing Translation for Hindi to English Machine Translation.
Deep Neural Network Based Demand Side Short Term Load Forecasting
Directory of Open Access Journals (Sweden)
Seunghyoung Ryu
2016-12-01
Full Text Available In the smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analyses to recent machine learning approaches and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management, including individual load forecasting, is becoming critical. In this paper, we propose deep neural network (DNN-based load forecasting models and apply them to a demand side empirical load database. DNNs are trained in two different ways: a pre-training restricted Boltzmann machine and using the rectified linear unit without pre-training. DNN forecasting models are trained by individual customer’s electricity consumption data and regional meteorological elements. To verify the performance of DNNs, forecasting results are compared with a shallow neural network (SNN, a double seasonal Holt–Winters (DSHW model and the autoregressive integrated moving average (ARIMA. The mean absolute percentage error (MAPE and relative root mean square error (RRMSE are used for verification. Our results show that DNNs exhibit accurate and robust predictions compared to other forecasting models, e.g., MAPE and RRMSE are reduced by up to 17% and 22% compared to SNN and 9% and 29% compared to DSHW.
Adaptive neural-based fuzzy modeling for biological systems.
Wu, Shinq-Jen; Wu, Cheng-Tao; Chang, Jyh-Yeong
2013-04-01
The inverse problem of identifying dynamic biological networks from their time-course response data set is a cornerstone of systems biology. Hill and Michaelis-Menten model, which is a forward approach, provides local kinetic information. However, repeated modifications and a large amount of experimental data are necessary for the parameter identification. S-system model, which is composed of highly nonlinear differential equations, provides the direct identification of an interactive network. However, the identification of skeletal-network structure is challenging. Moreover, biological systems are always subject to uncertainty and noise. Are there suitable candidates with the potential to deal with noise-contaminated data sets? Fuzzy set theory is developed for handing uncertainty, imprecision and complexity in the real world; for example, we say "driving speed is high" wherein speed is a fuzzy variable and high is a fuzzy set, which uses the membership function to indicate the degree of a element belonging to the set (words in Italics to denote fuzzy variables or fuzzy sets). Neural network possesses good robustness and learning capability. In this study we hybrid these two together into a neural-fuzzy modeling technique. A biological system is formulated to a multi-input-multi-output (MIMO) Takagi-Sugeno (T-S) fuzzy system, which is composed of rule-based linear subsystems. Two kinds of smooth membership functions (MFs), Gaussian and Bell-shaped MFs, are used. The performance of the proposed method is tested with three biological systems.
Neural Network based Modeling and Simulation of Transformer Inrush Current
Directory of Open Access Journals (Sweden)
Puneet Kumar Singh
2012-05-01
Full Text Available Inrush current is a very important phenomenon which occurs during energization of transformer at no load due to temporary over fluxing. It depends on several factors like magnetization curve, resistant and inductance of primary winding, supply frequency, switching angle of circuit breaker etc. Magnetizing characteristics of core represents nonlinearity which requires improved nonlinearity solving technique to know the practical behavior of inrush current. Since several techniques still working on modeling of transformer inrush current but neural network ensures exact modeling with experimental data. Therefore, the objective of this study was to develop an Artificial Neural Network (ANN model based on data of switching angle and remanent flux for predicting peak of inrush current. Back Propagation with Levenberg-Marquardt (LM algorithm was used to train the ANN architecture and same was tested for the various data sets. This research work demonstrates that the developed ANN model exhibits good performance in prediction of inrush current’s peak with an average of percentage error of -0.00168 and for modeling of inrush current with an average of percentage error of -0.52913.
Comparison Of Power Quality Disturbances Classification Based On Neural Network
Directory of Open Access Journals (Sweden)
Nway Nway Kyaw Win
2015-07-01
Full Text Available Abstract Power quality disturbances PQDs result serious problems in the reliability safety and economy of power system network. In order to improve electric power quality events the detection and classification of PQDs must be made type of transient fault. Software analysis of wavelet transform with multiresolution analysis MRA algorithm and feed forward neural network probabilistic and multilayer feed forward neural network based methodology for automatic classification of eight types of PQ signals flicker harmonics sag swell impulse fluctuation notch and oscillatory will be presented. The wavelet family Db4 is chosen in this system to calculate the values of detailed energy distributions as input features for classification because it can perform well in detecting and localizing various types of PQ disturbances. This technique classifies the types of PQDs problem sevents.The classifiers classify and identify the disturbance type according to the energy distribution. The results show that the PNN can analyze different power disturbance types efficiently. Therefore it can be seen that PNN has better classification accuracy than MLFF.
Neural Network Based Model for Predicting Housing Market Performance
Institute of Scientific and Technical Information of China (English)
Ahmed Khalafallah
2008-01-01
The United States real estate market is currently facing its worst hit in two decades due to the slowdown of housing sales. The most affected by this decline are real estate investors and home develop-ers who are currently struggling to break-even financially on their investments. For these investors, it is of utmost importance to evaluate the current status of the market and predict its performance over the short-term in order to make appropriate financial decisions. This paper presents the development of artificial neu-ral network based models to support real estate investors and home developers in this critical task. The pa-per describes the decision variables, design methodology, and the implementation of these models. The models utilize historical market performance data sets to train the artificial neural networks in order to pre-dict unforeseen future performances. An application example is analyzed to demonstrate the model capabili-ties in analyzing and predicting the market performance. The model testing and validation showed that the error in prediction is in the range between -2% and +2%.
Advanced neural network-based computational schemes for robust fault diagnosis
Mrugalski, Marcin
2014-01-01
The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practica...
Interpolation Functions of -Extensions of Apostol's Type Euler Polynomials
Directory of Open Access Journals (Sweden)
Kim Young-Hee
2009-01-01
Full Text Available The main purpose of this paper is to present new -extensions of Apostol's type Euler polynomials using the fermionic -adic integral on . We define the - -Euler polynomials and obtain the interpolation functions and the Hurwitz type zeta functions of these polynomials. We define -extensions of Apostol type's Euler polynomials of higher order using the multivariate fermionic -adic integral on . We have the interpolation functions of these - -Euler polynomials. We also give -extensions of Apostol's type Euler polynomials of higher order and have the multiple Hurwitz type zeta functions of these - -Euler polynomials.
González, M.; Lamela, H.; Jiménez, M.; Gimeno, J.; Ruiz-Llata, M.
2007-04-01
In this paper we present the scheme for a control circuit used in an image processing system which is to be implemented in a neural network which has a high level of connectivity and reconfiguration of neurons for integration and trigger based on the Address-Event Representation. This scheme will be employed as a pre-processing stage for a vision system which employs as its core processing an Optical Broadcast Neural Network (OBNN). [Optical Engineering letters 42 (9), 2488(2003)]. The proposed vision system allows the possibility to introduce patterns from any acquisition system of images, for posterior processing.
Neural Network Based Color Recognition for Bobbin Sorting Machine
Directory of Open Access Journals (Sweden)
Mu Zhang
2013-07-01
Full Text Available Winding is a key process in the manufacturing process of textile industry. The normal and effective operation of winding process plays a very important role on the textiles’ quality and economic effects. At present, a large proportion of bobbins which collected from winder still have yarn left over. The bobbin recycling is severely limited and quick running of winder is seriously restricted, the invention of the the automatic bobbin sorting machine has solved this problem. The ability to distinguish bobbin which has yarn left over from the rest and the classification accuracy of color are the two important performance indicators for bobbin sorting machine. According to the development and application of the color recognition technology and the artificial intelligence method, this study proposes a novel color recognition method that based on BP neural networks. The result shows that the accuracy of color recognition reaches 98%.
Image restoration techniques based on fuzzy neural networks
Institute of Scientific and Technical Information of China (English)
刘普寅; 李洪兴
2002-01-01
By establishing some suitable partitions of input and output spaces, a novel fuzzy neuralnetwork (FNN) which is called selection type FNN is developed. Such a system is a multilayerfeedforward neural network, which can be a universal approximator with maximum norm. Based ona family of fuzzy inference rules that are of real senses, a simple and useful inference type FNN isconstructed. As a result, the fusion of selection type FNN and inference type FNN results in a novelfilter-FNN filter. It is simple in structure. And also it is convenient to design the learning algorithmfor structural parameters. Further, FNN filter can efficiently suppress impulse noise superimposed onimage and preserve fine image structure, simultaneously. Some examples are simulated to confirmthe advantages of FNN filter over other filters, such as median filter and adaptive weighted fuzzymean (AWFM) filter and so on, in suppression of noises and preservation of image structure.
Neurally based measurement and evaluation of environmental noise
Soeta, Yoshiharu
2015-01-01
This book deals with methods of measurement and evaluation of environmental noise based on an auditory neural and brain-oriented model. The model consists of the autocorrelation function (ACF) and the interaural cross-correlation function (IACF) mechanisms for signals arriving at the two ear entrances. Even when the sound pressure level of a noise is only about 35 dBA, people may feel annoyed due to the aspects of sound quality. These aspects can be formulated by the factors extracted from the ACF and IACF. Several examples of measuring environmental noise—from outdoor noise such as that of aircraft, traffic, and trains, and indoor noise such as caused by floor impact, toilets, and air-conditioning—are demonstrated. According to the noise measurement and evaluation, applications for sound design are discussed. This book provides an excellent resource for students, researchers, and practitioners in a wide range of fields, such as the automotive, railway, and electronics industries, and soundscape, architec...
Sub-pixel mapping method based on BP neural network
Institute of Scientific and Technical Information of China (English)
LI Jiao; WANG Li-guo; ZHANG Ye; GU Yan-feng
2009-01-01
A new sub-pixel mapping method based on BP neural network is proposed in order to determine the spatial distribution of class components in each mixed pixel. The network was used to train a model that describes the relationship between spatial distribution of target components in mixed pixel and its neighboring information. Then the sub-pixel scaled target could be predicted by the trained model. In order to improve the performance of BP network, BP learning algorithm with momentum was employed. The experiments were conducted both on synthetic images and on hyperspectral imagery (HSI). The results prove that this method is capable of estimating land covers fairly accurately and has a great superiority over some other sub-pixel mapping methods in terms of computational complexity.
A Rapid Aerodynamic Design Procedure Based on Artificial Neural Networks
Rai, Man Mohan
2001-01-01
An aerodynamic design procedure that uses neural networks to model the functional behavior of the objective function in design space has been developed. This method incorporates several improvements to an earlier method that employed a strategy called parameter-based partitioning of the design space in order to reduce the computational costs associated with design optimization. As with the earlier method, the current method uses a sequence of response surfaces to traverse the design space in search of the optimal solution. The new method yields significant reductions in computational costs by using composite response surfaces with better generalization capabilities and by exploiting synergies between the optimization method and the simulation codes used to generate the training data. These reductions in design optimization costs are demonstrated for a turbine airfoil design study where a generic shape is evolved into an optimal airfoil.
Neural network based cluster creation in the ATLAS Pixel Detector
Andreazza, A; The ATLAS collaboration
2012-01-01
The read-out from individual pixels on planar semi-conductor sensors are grouped into clusters to reconstruct the location where a charged particle passed through the sensor. The resolution given by individual pixel sizes is significantly improved by using the information from the charge sharing be- tween pixels. Such analog cluster creation techniques have been used by the ATLAS experiment for many years to obtain an excellent performance. How- ever, in dense environments, such as those inside high-energy jets, clusters have an increased probability of merging the charge deposited by multiple particles. Recently, a neural network based algorithm which estimates both the cluster position and whether a cluster should be split has been developed for the ATLAS Pixel Detector. The algorithm significantly reduces ambigui- ties in the assignment of pixel detector measurement to tracks and improves the position accuracy with respect to standard techniques by taking into account the 2-dimensional charge distribution.
Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control
Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.
1997-01-01
One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.
Optimization of Component Based Software Engineering Model Using Neural Network
Directory of Open Access Journals (Sweden)
Gaurav Kumar
2014-10-01
Full Text Available The goal of Component Based Software Engineering (CBSE is to deliver high quality, more reliable and more maintainable software systems in a shorter time and within limited budget by reusing and combining existing quality components. A high quality system can be achieved by using quality components, framework and integration process that plays a significant role. So, techniques and methods used for quality assurance and assessment of a component based system is different from those of the traditional software engineering methodology. In this paper, we are presenting a model for optimizing Chidamber and Kemerer (CK metric values of component-based software. A deep analysis of a series of CK metrics of the software components design patterns is done and metric values are drawn from them. By using unsupervised neural network- Self Organizing Map, we have proposed a model that provides an optimized model for Software Component engineering model based on reusability that depends on CK metric values. Average, standard deviated and optimized values for the CK metric are compared and evaluated to show the optimized reusability of component based model.
Gain Scheduling Control of Nonlinear Systems Based on Neural State Space Models
DEFF Research Database (Denmark)
Bendtsen, Jan Dimon; Stoustrup, Jakob
2003-01-01
This paper presents a novel method for gain scheduling control of nonlinear systems based on extraction of local linear state space models from neural networks with direct application to robust control. A neural state space model of the system is first trained based on in- and output training...
OPTIMAL PWM BASED ON REAL—TIME SOLUTION WITH NEURAL NETWORK
Institute of Scientific and Technical Information of China (English)
ShenZhongting; YanYangguang
2002-01-01
A novel concept of neural network based control in pulse-width modulation(PWM)voltage source inverters is presented.On the one hand,the optimal switching an-gles are obtained in real time by the neural network based controller；on the other hand,the output voltage is ad-justed to fit the expected value by neural network when input voltage or loads change.The structure of neural network is simple and easy to be realized by DSP hard-ware system.No large memory used for the existing opti-mal PWM schemes is required in the system.Theoreticalanlysis of the proposed so-called sparse neural network is provided,and the stability of the system is proved.Un-der the control of neural network the error of output volt-age descends sharply,and the system outputs ac voltage with high precision.
Term Structure of Interest Rates Based on Artificial Neural Network
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
In light of the nonlinear approaching capability of artificial neural networks ( ANN), the term structure of interest rates is predicted using The generalized regression neural network (GRNN) and back propagation (BP) neural networks models. The prediction performance is measured with US interest rate data. Then, RBF and BP models are compared with Vasicek's model and Cox-Ingersoll-Ross (CIR) model. The comparison reveals that neural network models outperform Vasicek's model and CIR model,which are more precise and closer to the real market situation.
Neural Network Inverse Adaptive Controller Based on Davidon Least Square
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
General neural network inverse adaptive controller haa two flaws: the first is the slow convergence speed; the second is the invalidation to the non-minimum phase system.These defects limit the scope in which the neural network inverse adaptive controller is used.We employ Davidon least squares in training the multi-layer feedforward neural network used in approximating the inverse model of plant to expedite the convergence,and then through constructing the pseudo-plant,a neural network inverse adaptive controller is put forward which is still effective to the nonlinear non-minimum phase system.The simulation results show the validity of this scheme.
Heidari, Mohammad; Heidari, Ali; Homaei, Hadi
2014-01-01
The static pull-in instability of beam-type microelectromechanical systems (MEMS) is theoretically investigated. Two engineering cases including cantilever and double cantilever microbeam are considered. Considering the midplane stretching as the source of the nonlinearity in the beam behavior, a nonlinear size-dependent Euler-Bernoulli beam model is used based on a modified couple stress theory, capable of capturing the size effect. By selecting a range of geometric parameters such as beam lengths, width, thickness, gaps, and size effect, we identify the static pull-in instability voltage. A MAPLE package is employed to solve the nonlinear differential governing equations to obtain the static pull-in instability voltage of microbeams. Radial basis function artificial neural network with two functions has been used for modeling the static pull-in instability of microcantilever beam. The network has four inputs of length, width, gap, and the ratio of height to scale parameter of beam as the independent process variables, and the output is static pull-in voltage of microbeam. Numerical data, employed for training the network, and capabilities of the model have been verified in predicting the pull-in instability behavior. The output obtained from neural network model is compared with numerical results, and the amount of relative error has been calculated. Based on this verification error, it is shown that the radial basis function of neural network has the average error of 4.55% in predicting pull-in voltage of cantilever microbeam. Further analysis of pull-in instability of beam under different input conditions has been investigated and comparison results of modeling with numerical considerations shows a good agreement, which also proves the feasibility and effectiveness of the adopted approach. The results reveal significant influences of size effect and geometric parameters on the static pull-in instability voltage of MEMS.
Analogues of Euler and Poisson Summation Formulae
Indian Academy of Sciences (India)
Vivek V Rane
2003-08-01
Euler–Maclaurin and Poisson analogues of the summations $\\sum_{a < n ≤ b}(n)f(n), \\sum_{a < n ≤ b}d(n) f(n), \\sum_{a < n ≤ b}d(n)(n) f(n)$ have been obtained in a unified manner, where (()) is a periodic complex sequence; () is the divisor function and () is a sufficiently smooth function on [, ]. We also state a generalised Abel's summation formula, generalised Euler's summation formula and Euler's summation formula in several variables.
SOFM Neural Network Based Hierarchical Topology Control for Wireless Sensor Networks
2014-01-01
Well-designed network topology provides vital support for routing, data fusion, and target tracking in wireless sensor networks (WSNs). Self-organization feature map (SOFM) neural network is a major branch of artificial neural networks, which has self-organizing and self-learning features. In this paper, we propose a cluster-based topology control algorithm for WSNs, named SOFMHTC, which uses SOFM neural network to form a hierarchical network structure, completes cluster head selection by the...
Stevens, R. H.; K. Najafi
1992-01-01
Artificial neural networks were trained to recognize the test selection patterns of students' successful solutions to seven immunology computer based simulations. When new student's test selections were presented to the trained neural network, their problem solutions were correctly classified as successful or non-successful > 90% of the time. Examination of the neural networks output weights after each test selection revealed a progressive increase for the relevant problem suggesting that a s...
Query Based Approach Towards Spam Attacks Using Artificial Neural Network
Directory of Open Access Journals (Sweden)
Gaurav Kumar Tak
2010-10-01
Full Text Available Currently, spam and scams are passive attack over the inbox which can initiated to steal someconfidential information, to spread Worms, Viruses, Trojans, cookies and Sometimes they are used forphishing attacks. Spam mails are the major issue over mail boxes as well as over the internet. Spam mailscan be the cause of phishing attack, hacking of banking accounts, attacks on confidential data. Spammingis growing at a rapid rate since sending a flood of mails is easy and very cheap. Spam mails disturb themind-peace, waste time and consume various resources e.g., memory space and network bandwidth, sofiltering of spam mails is a big issue in cyber security.This paper presents an novel approach of spam filtering which is based on some query generatedapproach on the knowledge base and also use some artificial neural network methods to detect the spammails based on their behavior. analysis of the mail header, cross validation. Proposed methodologyincludes the 7 several steps which are well defined and achieve the higher accuracy. It works well with allkinds of spam mails (text based spam as well as image spam. Our tested data and experiments resultsshows promising results, and spam’s are detected out at least 98.17 % with 0.12% false positive.
Query Based Approach Towards Spam Attacks Using Artificial Neural Network
Directory of Open Access Journals (Sweden)
Gaurav Kumar Tak
2010-10-01
Full Text Available Currently, spam and scams are passive attack over the inbox which can initiated to steal some confidential information, to spread Worms, Viruses, Trojans, cookies and Sometimes they are used for phishing attacks. Spam mails are the major issue over mail boxes as well as over the internet. Spam mails can be the cause of phishing attack, hacking of banking accounts, attacks on confidential data. Spamming is growing at a rapid rate since sending a flood of mails is easy and very cheap. Spam mails disturb the mind-peace, waste time and consume various resources e.g., memory space and network bandwidth, so filtering of spam mails is a big issue in cyber security. This paper presents an novel approach of spam filtering which is based on some query generated approach on the knowledge base and also use some artificial neural network methods to detect the spam mails based on their behavior. analysis of the mail header, cross validation. Proposed methodology includes the 7 several steps which are well defined and achieve the higher accuracy. It works well with all kinds of spam mails (text based spam as well as image spam. Our tested data and experiments results shows promising results, and spam’s are detected out at least 98.17 % with 0.12% false positive.
Adaptive PID control based on orthogonal endocrine neural networks.
Milovanović, Miroslav B; Antić, Dragan S; Milojković, Marko T; Nikolić, Saša S; Perić, Staniša Lj; Spasić, Miodrag D
2016-12-01
A new intelligent hybrid structure used for online tuning of a PID controller is proposed in this paper. The structure is based on two adaptive neural networks, both with built-in Chebyshev orthogonal polynomials. First substructure network is a regular orthogonal neural network with implemented artificial endocrine factor (OENN), in the form of environmental stimuli, to its weights. It is used for approximation of control signals and for processing system deviation/disturbance signals which are introduced in the form of environmental stimuli. The output values of OENN are used to calculate artificial environmental stimuli (AES), which represent required adaptation measure of a second network-orthogonal endocrine adaptive neuro-fuzzy inference system (OEANFIS). OEANFIS is used to process control, output and error signals of a system and to generate adjustable values of proportional, derivative, and integral parameters, used for online tuning of a PID controller. The developed structure is experimentally tested on a laboratory model of the 3D crane system in terms of analysing tracking performances and deviation signals (error signals) of a payload. OENN-OEANFIS performances are compared with traditional PID and 6 intelligent PID type controllers. Tracking performance comparisons (in transient and steady-state period) showed that the proposed adaptive controller possesses performances within the range of other tested controllers. The main contribution of OENN-OEANFIS structure is significant minimization of deviation signals (17%-79%) compared to other controllers. It is recommended to exploit it when dealing with a highly nonlinear system which operates in the presence of undesirable disturbances.
Web based educational tool for neural network robot control
Directory of Open Access Journals (Sweden)
Jure Čas
2007-05-01
Full Text Available Abstract— This paper describes the application for teleoperations of the SCARA robot via the internet. The SCARA robot is used by students of mehatronics at the University of Maribor as a remote educational tool. The developed software consists of two parts i.e. the continuous neural network sliding mode controller (CNNSMC and the graphical user interface (GUI. Application is based on two well-known commercially available software packages i.e. MATLAB/Simulink and LabVIEW. Matlab/Simulink and the DSP2 Library for Simulink are used for control algorithm development, simulation and executable code generation. While this code is executing on the DSP-2 Roby controller and through the analog and digital I/O lines drives the real process, LabVIEW virtual instrument (VI, running on the PC, is used as a user front end. LabVIEW VI provides the ability for on-line parameter tuning, signal monitoring, on-line analysis and via Remote Panels technology also teleoperation. The main advantage of a CNNSMC is the exploitation of its self-learning capability. When friction or an unexpected impediment occurs for example, the user of a remote application has no information about any changed robot dynamic and thus is unable to dispatch it manually. This is not a control problem anymore because, when a CNNSMC is used, any approximation of changed robot dynamic is estimated independently of the remote’s user. Index Terms—LabVIEW; Matlab/Simulink; Neural network control; remote educational tool; robotics
The Euler-Maclaurin Formula and Extensions - An Elementary Approach
Gearhart, W. B.; Qian, Maijian
2005-01-01
This note offers a derivation of the Euler-Maclaurin formula that is simple and elementary. In addition, the paper shows that the derivation provides Euler-Maclaurin formulas for a variety of functionals other than the trapezoid rule.
HIGHER ORDER MULTIVARIABLE NORLUND EULER-BERNOULLI POLYNOMIALS
Institute of Scientific and Technical Information of China (English)
刘国栋
2002-01-01
The definitions of higher order multivariable Norlund Euler polynomials and Norlund Bernoulli polynomials are presented and some of their important properties are expounded. Some identities involving recurrence sequences and higher order multivariable Norlund Euler-Bernoulli polynomials are established.
The Cognitive, Perceptual, and Neural Bases of Skilled Performance
1994-02-01
recognition. In Proceedings of the international joint conference on neural networks, Seattle, II, 151-156. Piscat - away, NJ: IEEE Service Center...joint conference on neural networks, Seattle, I, 655-660. Piscat - away, NJ: IEEE Service Center. (#=) 35. Cruthirds, D., Gove, A., Grossberg, S
Expert System Based on Data Mining and Neural Networks
Institute of Scientific and Technical Information of China (English)
NI Zhi-wei; JIA Rui-yu
2001-01-01
On the basis of data mining and neural network, this paper proposes a general framework of the neural network expert system and discusses the key techniques in this kind of system. We apply these ideas on agricultural expert system to find some unknown useful knowledge and get some satisfactory results.
Thermoelastic steam turbine rotor control based on neural network
Rzadkowski, Romuald; Dominiczak, Krzysztof; Radulski, Wojciech; Szczepanik, R.
2015-12-01
Considered here are Nonlinear Auto-Regressive neural networks with eXogenous inputs (NARX) as a mathematical model of a steam turbine rotor for controlling steam turbine stress on-line. In order to obtain neural networks that locate critical stress and temperature points in the steam turbine during transient states, an FE rotor model was built. This model was used to train the neural networks on the basis of steam turbine transient operating data. The training included nonlinearity related to steam turbine expansion, heat exchange and rotor material properties during transients. Simultaneous neural networks are algorithms which can be implemented on PLC controllers. This allows for the application neural networks to control steam turbine stress in industrial power plants.
Chinese word sense disambiguation based on neural networks
Institute of Scientific and Technical Information of China (English)
LIU Ting; LU Zhi-mao; LANG Jun; LI Sheng
2005-01-01
The input of a network is the key problem for Chinese word sense disambiguation utilizing the neural network. This paper presents an input model of the neural network that calculates the mutual information between contextual words and the ambiguous word by using statistical methodology and taking the contextual words of a certain number beside the ambiguous word according to ( - M, + N). The experiment adopts triple-layer BP Neural Network model and proves how the size of a training set and the value of M and N affect the performance of the Neural Network Model. The experimental objects are six pseudowords owning three word-senses constructed according to certain principles. The tested accuracy of our approach on a closed-corpus reaches 90. 31% ,and 89. 62% on an open-corpus. The experiment proves that the Neural Network Model has a good performance on Word Sense Disambiguation.
Stevens, R H; Najafi, K
1992-01-01
Artificial neural networks were trained to recognize the test selection patterns of students' successful solutions to seven immunology computer based simulations. When new student's test selections were presented to the trained neural network, their problem solutions were correctly classified as successful or non-successful > 90% of the time. Examination of the neural networks output weights after each test selection revealed a progressive increase for the relevant problem suggesting that a successful solution was represented by the neural network as the accumulation of relevant tests. Unsuccessful problem solutions revealed two patterns of students performances. The first pattern was characterized by low neural network output weights for all seven problems reflecting extensive searching and lack of recognition of relevant information. In the second pattern, the output weights from the neural network were biased towards one of the remaining six incorrect problems suggesting that the student mis-represented the current problem as an instance of a previous problem.
Study on the Robot Robust Adaptive Control Based on Neural Networks
Institute of Scientific and Technical Information of China (English)
温淑焕; 王洪瑞; 吴丽艳
2003-01-01
Force control based on neural networks is presented. Under the framework of hybrid control, an RBF neural network is used to compensate for all the uncertainties from robot dynamics and unknown environment first. The technique will improve the adaptability to environment stiffness when the end-effector is in contact with the environment, and does not require any a priori knowledge on the upper bound of syste uncertainties. Moreover, it need not compute the inverse of inertia matrix. Learning algorithms for neural networks to minimize the force error directly are designed. Simulation results have shown a better force/position tracking when neural network is used.
Financial integration in Europe : Evidence from Euler equation tests
Lemmen, J.J.G.; Eijffinger, S.C.W.
1995-01-01
This paper applies Obstfeld's Euler equation tests to assess the degree of financial integration in the European Union. In addition, we design a new Euler equation test which is intimately related to Obstfeld's Euler equation tests. Using data from the latest Penn World Table (Mark 6), we arrive at
Regularity and Energy Conservation for the Compressible Euler Equations
Feireisl, Eduard; Gwiazda, Piotr; Świerczewska-Gwiazda, Agnieszka; Wiedemann, Emil
2017-03-01
We give sufficient conditions on the regularity of solutions to the inhomogeneous incompressible Euler and the compressible isentropic Euler systems in order for the energy to be conserved. Our strategy relies on commutator estimates similar to those employed by Constantin et al. for the homogeneous incompressible Euler equations.
Image recognition based on artificial neural network%基于神经网络的图像识别
Institute of Scientific and Technical Information of China (English)
王强; 张小溪; 韩一红
2012-01-01
神经网络图像识别技术是随着当代计算机技术、图像处理、人工智能、模式识别理论等发展起来的一种新型图像识别技术。在进行图像识别之前需要利用数字图像处理技术进行图像预处理以及特征提取。本文选取字符图像0—9作为识别目标，对图像预处理过程进行了叙述，并在此基础上选取字符图像矩阵每行的与每列的黑色像素点之和以及图像欧拉数这两个特征作为BP神经网络的输入样本。经实验仿真表明图像的平均识别率为89％，这表明图像预处理的结果和提取的特征是合适的、有效的。设计的BP网络也较好的完成了模式分类识别工作。%Image recognition based on artificial neural network is a new type of image recognition technology that develops with the development of modern computer technology, image processing, artificial intelligence, pattern recognition theory and so on. It is necessary to make the image pre-processing as well as the feature extraction work using the digital image processing technology before carrying on the image recognition. In this paper, the character image from 0 to 9 is chosen to expound the process of image pre-processing and based on this, the sum of black pixels of each row and column and the Euler numbers are extracted as the BP neural network input sample. The results of experiment, average recognition rate of 89%, can prove that the results of the character image pre-processing and extraction of features are appropriate and effective and the designed BP network is also better to complete the pattern classification and recognition.
An Automatic Proof of Euler's Formula
Directory of Open Access Journals (Sweden)
Jun Zhang
2005-05-01
Full Text Available In this information age, everything is digitalized. The encoding of functions and the automatic proof of functions are important. This paper will discuss the automatic calculation for Taylor expansion coefficients, as an example, it can be applied to prove Euler's formula automatically.
Discretization vs. Rounding Error in Euler's Method
Borges, Carlos F.
2011-01-01
Euler's method for solving initial value problems is an excellent vehicle for observing the relationship between discretization error and rounding error in numerical computation. Reductions in stepsize, in order to decrease discretization error, necessarily increase the number of steps and so introduce additional rounding error. The problem is…
Euler and the Fundamental Theorem of Algebra.
Duham, William
1991-01-01
The complexity of the proof of the Fundamental Theorem of Algebra makes it inaccessible to lower level students. Described are more understandable attempts of proving the theorem and a historical account of Euler's efforts that relates the progression of the mathematical process used and indicates some of the pitfalls encountered. (MDH)
Summing up the Euler [phi] Function
Loomis, Paul; Plytage, Michael; Polhill, John
2008-01-01
The Euler [phi] function counts the number of positive integers less than and relatively prime to a positive integer n. Here we look at perfect totient numbers, number for which [phi](n) + [phi]([phi](n)) + [phi]([phi]([phi](n))) + ... + 1 = n.
Euler Polynomials, Fourier Series and Zeta Numbers
DEFF Research Database (Denmark)
Scheufens, Ernst E
2012-01-01
Fourier series for Euler polynomials is used to obtain information about values of the Riemann zeta function for integer arguments greater than one. If the argument is even we recover the well-known exact values, if the argument is odd we find integral representations and rapidly convergent series....
Electromyogram-based neural network control of transhumeral prostheses
Directory of Open Access Journals (Sweden)
Christopher L. Pulliam, MS
2011-07-01
Full Text Available Upper-limb amputation can cause a great deal of functional impairment for patients, particularly for those with amputation at or above the elbow. Our long-term objective is to improve functional outcomes for patients with amputation by integrating a fully implanted electromyographic (EMG recording system with a wireless telemetry system that communicates with the patient's prosthesis. We believe that this should generate a scheme that will allow patients to robustly control multiple degrees of freedom simultaneously. The goal of this study is to evaluate the feasibility of predicting dynamic arm movements (both flexion/extension and pronation/supination based on EMG signals from a set of muscles that would likely be intact in patients with transhumeral amputation. We recorded movement kinematics and EMG signals from seven muscles during a variety of movements with different complexities. Time-delayed artificial neural networks were then trained offline to predict the measured arm trajectories based on features extracted from the measured EMG signals. We evaluated the relative effectiveness of various muscle subsets. Predicted movement trajectories had average root-mean-square errors of approximately 15.7° and 24.9° and average R2 values of approximately 0.81 and 0.46 for elbow flexion/extension and forearm pronation/supination, respectively.
Neural stem cell-based treatment for neurodegenerative diseases.
Kim, Seung U; Lee, Hong J; Kim, Yun B
2013-10-01
Human neurodegenerative diseases such as Parkinson's disease (PD), Huntington's disease (HD), amyotrophic lateral sclerosis (ALS) and Alzheimer's disease (AD) are caused by a loss of neurons and glia in the brain or spinal cord. Neurons and glial cells have successfully been generated from stem cells such as embryonic stem cells (ESCs), mesenchymal stem cells (MSCs) and neural stem cells (NSCs), and stem cell-based cell therapies for neurodegenerative diseases have been developed. A recent advance in generation of a new class of pluripotent stem cells, induced pluripotent stem cells (iPSCs), derived from patients' own skin fibroblasts, opens doors for a totally new field of personalized medicine. Transplantation of NSCs, neurons or glia generated from stem cells in animal models of neurodegenerative diseases, including PD, HD, ALS and AD, demonstrates clinical improvement and also life extension of these animals. Additional therapeutic benefits in these animals can be provided by stem cell-mediated gene transfer of therapeutic genes such as neurotrophic factors and enzymes. Although further research is still needed, cell and gene therapy based on stem cells, particularly using neurons and glia derived from iPSCs, ESCs or NSCs, will become a routine treatment for patients suffering from neurodegenerative diseases and also stroke and spinal cord injury.
Prediction and Research on Vegetable Price Based on Genetic Algorithm and Neural Network Model
Institute of Scientific and Technical Information of China (English)
2011-01-01
Considering the complexity of vegetables price forecast,the prediction model of vegetables price was set up by applying the neural network based on genetic algorithm and using the characteristics of genetic algorithm and neural work.Taking mushrooms as an example,the parameters of the model are analyzed through experiment.In the end,the results of genetic algorithm and BP neural network are compared.The results show that the absolute error of prediction data is in the scale of 10%;in the scope that the absolute error in the prediction data is in the scope of 20% and 15%.The accuracy of genetic algorithm based on neutral network is higher than the BP neutral network model,especially the absolute error of prediction data is within the scope of 20%.The accuracy of genetic algorithm based on neural network is obviously better than BP neural network model,which represents the favorable generalization capability of the model.
Batch Process Modelling and Optimal Control Based on Neural Network Models
Institute of Scientific and Technical Information of China (English)
Jie Zhang
2005-01-01
This paper presents several neural network based modelling, reliable optimal control, and iterative learning control methods for batch processes. In order to overcome the lack of robustness of a single neural network, bootstrap aggregated neural networks are used to build reliable data based empirical models. Apart from improving the model generalisation capability, a bootstrap aggregated neural network can also provide model prediction confidence bounds. A reliable optimal control method by incorporating model prediction confidence bounds into the optimisation objective function is presented. A neural network based iterative learning control strategy is presented to overcome the problem due to unknown disturbances and model-plant mismatches. The proposed methods are demonstrated on a simulated batch polymerisation process.
Synthetical Control of AGC/LPC System Based on Neural Networks Internal Model Control
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
One synthetical control method of AGC/LPC system based on intelligence control theory-neural networks internal model control method is presented. Genetic algorithm (GA) is applied to optimize the parameters of the neural networks. Simulation results prove that this method is effective.
Di Noia, A.; Hasekamp, O.P.; Harten, G. van; Rietjens, J.H.H.; Smit, J.M.; Snik, F.; Henzing, J.S.; Boer, J. de; Keller, C.U.; Volten, H.
2015-01-01
In this paper, the use of a neural network algorithm for the retrieval of the aerosol properties from ground-based spectropolarimetric measurements is discussed. The neural network is able to retrieve the aerosol properties with an accuracy that is almost comparable to that of an iterative retrieval
Underwater vehicle sonar self-noise prediction based on genetic algorithms and neural network
Institute of Scientific and Technical Information of China (English)
WU Xiao-guang; SHI Zhong-kun
2006-01-01
The factors that influence underwater vehicle sonar self-noise are analyzed, and genetic algorithms and a back propagation (BP) neural network are combined to predict underwater vehicle sonar self-noise. The experimental results demonstrate that underwater vehicle sonar self-noise can be predicted accurately by a GA-BP neural network that is based on actual underwater vehicle sonar data.
Research on Feasibilityof Top-Coal Caving Based on Neural Network Technique
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Based on the neural network technique, this paper proposes a BP neural network model which integratesgeological factors which affect top-coal caving in a comprehensive index. The index of top-coal caving may be usedto forecast the mining cost of working faces, which shows the model's potential prospect of applications.
Simulation Model of Magnetic Levitation Based on NARX Neural Networks
Directory of Open Access Journals (Sweden)
Dragan Antić
2013-04-01
Full Text Available In this paper, we present analysis of different training types for nonlinear autoregressive neural network, used for simulation of magnetic levitation system. First, the model of this highly nonlinear system is described and after that the Nonlinear Auto Regressive eXogenous (NARX of neural network model is given. Also, numerical optimization techniques for improved network training are described. It is verified that NARX neural network can be successfully used to simulate real magnetic levitation system if suitable training procedure is chosen, and the best two training types, obtained from experimental results, are described in details.
Euler force actuation mechanism for siphon valving in compact disk-like microfluidic chips.
Deng, Yongbo; Fan, Jianhua; Zhou, Song; Zhou, Teng; Wu, Junfeng; Li, Yin; Liu, Zhenyu; Xuan, Ming; Wu, Yihui
2014-03-01
Based on the Euler force induced by the acceleration of compact disk (CD)-like microfluidic chip, this paper presents a novel actuation mechanism for siphon valving. At the preliminary stage of acceleration, the Euler force in the tangential direction of CD-like chip takes the primary place compared with the centrifugal force to function as the actuation of the flow, which fills the siphon and actuates the siphon valving. The Euler force actuation mechanism is demonstrated by the numerical solution of the phase-field based mathematical model for the flow in siphon valve. In addition, experimental validation is implemented in the polymethylmethacrylate-based CD-like microfluidic chip manufactured using CO2 laser engraving technique. To prove the application of the proposed Euler force actuation mechanism, whole blood separation and plasma extraction has been conducted using the Euler force actuated siphon valving. The newly introduced actuation mechanism overcomes the dependence on hydrophilic capillary filling of siphon by avoiding external manipulation or surface treatments of polymeric material. The sacrifice for highly integrated processing in pneumatic pumping technique is also prevented by excluding the volume-occupied compressed air chamber.
Evolving Neural Turing Machines for Reward-based Learning
DEFF Research Database (Denmark)
Jacobsen, Emil Juul; Risi, Sebastian; Greve, Rasmus Boll
2016-01-01
An unsolved problem in neuroevolution (NE) is to evolve artificial neural networks (ANN) that can store and use information to change their behavior online. While plastic neural networks have shown promise in this context, they have difficulties retaining information over longer periods of time...... Turing Machine (NTM) and show that such an approach greatly simplifies the neural model, generalizes better, and does not require accessing the entire memory content at each time-step. The Evolvable Neural Turing Machine (ENTM) is able to solve a simple copy tasks and for the first time, the continuous...... version of the double T-Maze, a complex reinforcement-like learning problem. In the T-Maze learning task the agent uses the memory bank to display adaptive behavior that normally requires a plastic ANN, thereby suggesting a complementary and effective mechanism for adaptive behavior in NE....
Layered learning of soccer robot based on artificial neural network
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Discusses the application of artificial neural network for MIROSOT, introduces a layered model of BP network of soccer robot for learning basic behavior and cooperative behavior, and concludes from experimental results that the model is effective.
Euler/Navier-Stokes Solvers Applied to Ducted Fan Configurations
Keith, Theo G., Jr.; Srivastava, Rakesh
1997-01-01
Due to noise considerations, ultra high bypass ducted fans have become a more viable design. These ducted fans typically consist of a rotor stage containing a wide chord fan and a stator stage. One of the concerns for this design is the classical flutter that keeps occurring in various unducted fan blade designs. These flutter are catastrophic and are to be avoided in the flight envelope of the engine. Some numerical investigations by Williams, Cho and Dalton, have suggested that a duct around a propeller makes it more unstable. This needs to be further investigated. In order to design an engine to safely perform a set of desired tasks, accurate information of the stresses on the blade during the entire cycle of blade motion is required. This requirement in turn demands that accurate knowledge of steady and unsteady blade loading be available. Aerodynamic solvers based on unsteady three-dimensional analysis will provide accurate and fast solutions and are best suited for aeroelastic analysis. The Euler solvers capture significant physics of the flowfield and are reasonably fast. An aerodynamic solver Ref. based on Euler equations had been developed under a separate grant from NASA Lewis in the past. Under the current grant, this solver has been modified to calculate the aeroelastic characteristics of unducted and ducted rotors. Even though, the aeroelastic solver based on three-dimensional Euler equations is computationally efficient, it is still very expensive to investigate the effects of multiple stages on the aeroelastic characteristics. In order to investigate the effects of multiple stages, a two-dimensional multi stage aeroelastic solver was also developed under this task, in collaboration with Dr. T. S. R. Reddy of the University of Toledo. Both of these solvers were applied to several test cases and validated against experimental data, where available.
APPROACH TO FAULT ON-LINE DETECTION AND DIAGNOSIS BASED ON NEURAL NETWORKS FOR ROBOT IN FMS
Institute of Scientific and Technical Information of China (English)
1998-01-01
Based on radial basis function (RBF) neural networks, the healthy working model of each sub-system of robot in FMS is established. A new approach to fault on-line detection and diagnosis according to neural networks model is presented. Fault double detection based on neural network model and threshold judgement and quick fault identification based on multi-layer feedforward neural networks are applied, which can meet quickness and reliability of fault detection and diagnosis for robot in FMS.
Low-dimensional recurrent neural network-based Kalman filter for speech enhancement.
Xia, Youshen; Wang, Jun
2015-07-01
This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the proposed recurrent neural network and the speech signal is then recovered from Kalman filtering. The proposed recurrent neural network is globally asymptomatically stable to the noise-constrained estimate. Because the noise-constrained estimate has a robust performance against non-Gaussian noise, the proposed recurrent neural network-based speech enhancement algorithm can minimize the estimation error of Kalman filter parameters in non-Gaussian noise. Furthermore, having a low-dimensional model feature, the proposed neural network-based speech enhancement algorithm has a much faster speed than two existing recurrent neural networks-based speech enhancement algorithms. Simulation results show that the proposed recurrent neural network-based speech enhancement algorithm can produce a good performance with fast computation and noise reduction.
RMB Exchange Rate Forecast Approach Based on BP Neural Network
Ye, Sun
RMB exchange rate system has reformed since July, 2005. This article chose RMB exchange rate data during a period from July, 2005 to September 2010 to establish BP neural network model to forecast RMB exchange rate in the future by using MATLAB software. The result showed that BP neural network is effective to forecast RMB exchange rate and also indicated that RMB exchange rate will continue to appreciate in the future.
Energy Technology Data Exchange (ETDEWEB)
Mjalli, F.S.; Al-Asheh, S. [Chemical Engineering Department, Qatar University, Doha (Qatar)
2005-10-01
In this work advanced nonlinear neural networks based control system design algorithms are adopted to control a mechanistic model for an ethanol fermentation process. The process model equations for such systems are highly nonlinear. A neural network strategy has been implemented in this work for capturing the dynamics of the mechanistic model for the fermentation process. The neural network achieved has been validated against the mechanistic model. Two neural network based nonlinear control strategies have also been adopted using the model identified. The performance of the feedback linearization technique was compared to neural network model predictive control in terms of stability and set point tracking capabilities. Under servo conditions, the feedback linearization algorithm gave comparable tracking and stability. The feedback linearization controller achieved the control target faster than the model predictive one but with vigorous and sudden controller moves. (Abstract Copyright [2005], Wiley Periodicals, Inc.)
EMP response modeling of TVS based on the recurrent neural network
Directory of Open Access Journals (Sweden)
Zhiqiang JI
2015-04-01
Full Text Available Due to the larger workload in the implementation process and the poor consistence between the test results and actual situation problems when using the transmission line pulse (TLP testing methods, a modeling method based on the recurrent neural network is proposed for EMP response forecast. Based on the TLP testing system, two categories of EMP are increased, which are the machine model ESD EMP and human metal model ESD EMP. Elman neural network, Jordan neural network and their combination namely Elman-Jordan neural network are established for response modeling of NUP2105L transient voltage suppressor (TVS forecasting the response under different EMP. The simulation results show that the recurrent neural network has satisfying modeling effects and high computation efficiency.
Quantum-based algorithm for optimizing artificial neural networks.
Tzyy-Chyang Lu; Gwo-Ruey Yu; Jyh-Ching Juang
2013-08-01
This paper presents a quantum-based algorithm for evolving artificial neural networks (ANNs). The aim is to design an ANN with few connections and high classification performance by simultaneously optimizing the network structure and the connection weights. Unlike most previous studies, the proposed algorithm uses quantum bit representation to codify the network. As a result, the connectivity bits do not indicate the actual links but the probability of the existence of the connections, thus alleviating mapping problems and reducing the risk of throwing away a potential candidate. In addition, in the proposed model, each weight space is decomposed into subspaces in terms of quantum bits. Thus, the algorithm performs a region by region exploration, and evolves gradually to find promising subspaces for further exploitation. This is helpful to provide a set of appropriate weights when evolving the network structure and to alleviate the noisy fitness evaluation problem. The proposed model is tested on four benchmark problems, namely breast cancer and iris, heart, and diabetes problems. The experimental results show that the proposed algorithm can produce compact ANN structures with good generalization ability compared to other algorithms.
Illicit material detector based on gas sensors and neural networks
Grimaldi, Vincent; Politano, Jean-Luc
1997-02-01
In accordance with its missions, le Centre de Recherches et d'Etudes de la Logistique de la Police Nationale francaise (CREL) has been conducting research for the past few years targeted at detecting drugs and explosives. We have focused our approach of the underlying physical and chemical detection principles on solid state gas sensors, in the hope of developing a hand-held drugs and explosives detector. The CREL and Laboratory and Scientific Services Directorate are research partners for this project. Using generic hydrocarbon, industrially available, metal oxide sensors as illicit material detectors, requires usage precautions. Indeed, neither the product's concentrations, nor even the products themselves, belong to the intended usage specifications. Therefore, the CREL is currently investigating two major research topics: controlling the sensor's environment: with environmental control we improve the detection of small product concentration; determining detection thresholds: both drugs and explosives disseminate low gas concentration. We are attempting to quantify the minimal concentration which triggers detection. In the long run, we foresee a computer-based tool likely to detect a target gas in a noisy atmosphere. A neural network is the suitable tool for interpreting the response of heterogeneous sensor matrix. This information processing structure, alongside with proper sensor environment control, will lessen the repercussions of common MOS sensor sensitivity characteristic dispersion.
Artificial Neural Network-Based System for PET Volume Segmentation
Directory of Open Access Journals (Sweden)
Mhd Saeed Sharif
2010-01-01
Full Text Available Tumour detection, classification, and quantification in positron emission tomography (PET imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI approaches can provide improved accuracy and save decent amount of time. Artificial neural networks (ANNs, as one of the best AI techniques, have the capability to classify and quantify precisely lesions and model the clinical evaluation for a specific problem. This paper presents a novel application of ANNs in the wavelet domain for PET volume segmentation. ANN performance evaluation using different training algorithms in both spatial and wavelet domains with a different number of neurons in the hidden layer is also presented. The best number of neurons in the hidden layer is determined according to the experimental results, which is also stated Levenberg-Marquardt backpropagation training algorithm as the best training approach for the proposed application. The proposed intelligent system results are compared with those obtained using conventional techniques including thresholding and clustering based approaches. Experimental and Monte Carlo simulated PET phantom data sets and clinical PET volumes of nonsmall cell lung cancer patients were utilised to validate the proposed algorithm which has demonstrated promising results.
Convolutional neural network features based change detection in satellite images
Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong
2016-07-01
With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.
Pattern recognition for electroencephalographic signals based on continuous neural networks.
Alfaro-Ponce, M; Argüelles, A; Chairez, I
2016-07-01
This study reports the design and implementation of a pattern recognition algorithm to classify electroencephalographic (EEG) signals based on artificial neural networks (NN) described by ordinary differential equations (ODEs). The training method for this kind of continuous NN (CNN) was developed according to the Lyapunov theory stability analysis. A parallel structure with fixed weights was proposed to perform the classification stage. The pattern recognition efficiency was validated by two methods, a generalization-regularization and a k-fold cross validation (k=5). The classifier was applied on two different databases. The first one was made up by signals collected from patients suffering of epilepsy and it is divided in five different classes. The second database was made up by 90 single EEG trials, divided in three classes. Each class corresponds to a different visual evoked potential. The pattern recognition algorithm achieved a maximum correct classification percentage of 97.2% using the information of the entire database. This value was similar to some results previously reported when this database was used for testing pattern classification. However, these results were obtained when only two classes were considered for the testing. The result reported in this study used the whole set of signals (five different classes). In comparison with similar pattern recognition methods that even considered less number of classes, the proposed CNN proved to achieve the same or even better correct classification results.
Stability and synchronization of memristor-based fractional-order delayed neural networks.
Chen, Liping; Wu, Ranchao; Cao, Jinde; Liu, Jia-Bao
2015-11-01
Global asymptotic stability and synchronization of a class of fractional-order memristor-based delayed neural networks are investigated. For such problems in integer-order systems, Lyapunov-Krasovskii functional is usually constructed, whereas similar method has not been well developed for fractional-order nonlinear delayed systems. By employing a comparison theorem for a class of fractional-order linear systems with time delay, sufficient condition for global asymptotic stability of fractional memristor-based delayed neural networks is derived. Then, based on linear error feedback control, the synchronization criterion for such neural networks is also presented. Numerical simulations are given to demonstrate the effectiveness of the theoretical results.
Neural network-based H∞ filtering for nonlinear systems with time-delays
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
A novel H∞ design methodology for a neural network-based nonlinear filtering scheme is addressed.Firstly,neural networks are employed to approximate the nonlinearities.Next,the nonlinear dynamic system is represented by the mode-dependent linear difference inclusion (LDI).Finally,based on the LDI model,a neural network-based nonlinear filter (NNBNF) is developed to minimize the upper bound of H∞ gain index of the estimation error under some linear matrix inequality (LMI) constraints.Compared with the existing nonlinear filters,NNBNF is time-invariant and numerically tractable.The validity and applicability of the proposed approach are successfully demonstrated in an illustrative example.
Zhang, Guodong; Shen, Yi
2014-07-01
This paper investigates the exponential synchronization of coupled memristor-based chaotic neural networks with both time-varying delays and general activation functions. And here, we adopt nonsmooth analysis and control theory to handle memristor-based chaotic neural networks with discontinuous right-hand side. In particular, several new criteria ensuring exponential synchronization of two memristor-based chaotic neural networks are obtained via periodically intermittent control. In addition, the new proposed results here are very easy to verify and also complement, extend the earlier publications. Numerical simulations on the chaotic systems are presented to illustrate the effectiveness of the theoretical results.
A novel compensation-based recurrent fuzzy neural network and its learning algorithm
Institute of Scientific and Technical Information of China (English)
WU Bo; WU Ke; LU JianHong
2009-01-01
Based on detailed atudy on aeveral kinds of fuzzy neural networks, we propose a novel compensation. based recurrent fuzzy neural network (CRFNN) by adding recurrent element and compensatory element to the conventional fuzzy neural network. Then, we propose a sequential learning method for the structure Identification of the CRFNN In order to confirm the fuzzy rules and their correlaUve parameters effectively. Furthermore, we Improve the BP algorithm based on the characteristics of the proposed CRFNN to train the network. By modeling the typical nonlinear systems, we draw the conclusion that the proposed CRFNN has excellent dynamic response and strong learning ability.
Extended Kalman Filter Based Neural Networks Controller For Hot Strip Rolling mill
Moussaoui, A. K.; Abbassi, H. A.; Bouazza, S.
2008-06-01
The present paper deals with the application of an Extended Kalman filter based adaptive Neural-Network control scheme to improve the performance of a hot strip rolling mill. The suggested Neural Network model was implemented using Bayesian Evidence based training algorithm. The control input was estimated iteratively by an on-line extended Kalman filter updating scheme basing on the inversion of the learned neural networks model. The performance of the controller is evaluated using an accurate model estimated from real rolling mill input/output data, and the usefulness of the suggested method is proved.
Institute of Scientific and Technical Information of China (English)
HAN Liu-xin; WANG Huan-chen; ZHANG Xian-hui
2001-01-01
A detailed study of the capabilities of artificial neural networks to diagnoses cracks in massive concrete structures is presented. This paper includes the components of the expert system such as design thought, basic structure, building of knowledge base and the implementation of neural network applied model. The realizing method of neural network based clustering algorithm in the knowledge base and selfstudy is analyzed emphatically and stimulated by means of the computer. From the above study, some important conclusions have been drawn and some new viewpoints have been suggested.
Zhang, Wei; Li, Chuandong; Huang, Tingwen; He, Xing
2015-12-01
Synchronization of an array of linearly coupled memristor-based recurrent neural networks with impulses and time-varying delays is investigated in this brief. Based on the Lyapunov function method, an extended Halanay differential inequality and a new delay impulsive differential inequality, some sufficient conditions are derived, which depend on impulsive and coupling delays to guarantee the exponential synchronization of the memristor-based recurrent neural networks. Impulses with and without delay and time-varying delay are considered for modeling the coupled neural networks simultaneously, which renders more practical significance of our current research. Finally, numerical simulations are given to verify the effectiveness of the theoretical results.
Remarks on the Einstein-Euler-Entropy system
Disconzi, Marcelo M
2013-01-01
We prove short-time existence for the Einstein-Euler-Entropy system for non-isentropic fluids with data in uniformly local Sobolev spaces. The cases of compact as well as non-compact Cauchy surfaces are covered. The method employed uses a Lagrangian description of the fluid flow which is based on techniques developed by Friedrich, hence providing a completely different proof of earlier results of Choquet-Bruhat and Lichnerowicz. This new proof is specially suited for applications to self-gravitating fluid bodies.
Photosensitive-polyimide based method for fabricating various neural electrode architectures
Directory of Open Access Journals (Sweden)
Yasuhiro X Kato
2012-06-01
Full Text Available An extensive photosensitive polyimide (PSPI-based method for designing and fabricating various neural electrode architectures was developed. The method aims to broaden the design flexibility and expand the fabrication capability for neural electrodes to improve the quality of recorded signals and integrate other functions. After characterizing PSPI’s properties for micromachining processes, we successfully designed and fabricated various neural electrodes even on a non-flat substrate using only one PSPI as an insulation material and without the time-consuming dry etching processes. The fabricated neural electrodes were an electrocorticogram electrode, a mesh intracortical electrode with a unique lattice-like mesh structure to fixate neural tissue, and a guide cannula electrode with recording microelectrodes placed on the curved surface of a guide cannula as a microdialysis probe. In vivo neural recordings using anesthetized rats demonstrated that these electrodes can be used to record neural activities repeatedly without any breakage and mechanical failures, which potentially promises stable recordings for long periods of time. These successes make us believe that this PSPI-based fabrication is a powerful method, permitting flexible design and easy optimization of electrode architectures for a variety of electrophysiological experimental research with improved neural recording performance.
Lag Synchronization of Memristor-Based Coupled Neural Networks via ω-Measure.
Li, Ning; Cao, Jinde
2016-03-01
This paper deals with the lag synchronization problem of memristor-based coupled neural networks with or without parameter mismatch using two different algorithms. Firstly, we consider the memristor-based neural networks with parameter mismatch, lag complete synchronization cannot be achieved due to parameter mismatch, the concept of lag quasi-synchronization is introduced. Based on the ω-measure method and generalized Halanay inequality, the error level is estimated, a new lag quasi-synchronization scheme is proposed to ensure that coupled memristor-based neural networks are in a state of lag synchronization with an error level. Secondly, by constructing Lyapunov functional and applying common Halanary inequality, several lag complete synchronization criteria for the memristor-based neural networks with parameter match are given, which are easy to verify. Finally, two examples are given to illustrate the effectiveness of the proposed lag quasi-synchronization or lag complete synchronization criteria, which well support theoretical results.
Establishing a fiber-optic-based optical neural interface.
Adamantidis, Antoine R; Zhang, Feng; de Lecea, Luis; Deisseroth, Karl
2014-08-01
Selective expression of opsins in genetically defined neurons makes it possible to control a subset of neurons without affecting nearby cells and processes in the intact brain, but light must still be delivered to the target brain structure. Light scattering limits the delivery of light from the surface of the brain. For this reason, we have developed a fiber-optic-based optical neural interface (ONI), which allows optical access to any brain structure in freely moving mammals. The ONI system is constructed by modifying the small animal cannula system from PlasticsOne. The system for bilateral stimulation consists of a bilateral cannula guide that has been stereotactically implanted over the target brain region, a screw cap for securing the optical fiber to the animal's head, a fiber guard modified from the internal cannula adapter, and a bare fiber whose length is customized based on the depth of the target region. For unilateral stimulation, a single-fiber system can be constructed using unilateral cannula parts from PlasticsOne. We describe here the preparation of the bilateral ONI system and its use in optical stimulation of the mouse or rat brain. Delivery of opsin-expressing virus and implantation of the ONI may be conducted in the same surgical session; alternatively, with a transgenic animal no opsin virus is delivered during the surgery. Similar procedures are useful for deep or superficial injections (even for neocortical targets, although in some cases surface light-emitting diodes or cortex-apposed fibers can be used for the most superficial cortical targets).
REAL-TIME MOTION PLANNING METHOD BASED ON NEURAL NETWORKS FOR MULTIPLE MOBILE ROBOTS
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
The motion planning of multiple mobile robots undertaking individual tasks in the same environment is studied. A motion planning method based on neural networks is proposed. By storing fuzzy rules in neural networks the method can fully make use of the association ability and high processing speed of neural networks to make robots avoid collisions with other objects in real time.Compared with rules method,the method can not only avoid building a large and complex rules base but also make robots avoid collisions and conflicts at higher speed and with higher intelligence.
Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.
2007-01-01
To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.
Feature evaluation and extraction based on neural network in analog circuit fault diagnosis
Institute of Scientific and Technical Information of China (English)
Yuan Haiying; Chen Guangju; Xie Yongle
2007-01-01
Choosing the right characteristic parameter is the key to fault diagnosis in analog circuit.The feature evaluation and extraction methods based on neural network are presented.Parameter evaluation of circuit features is realized by training results from neural network; the superior nonlinear mapping capability is competent for extracting fault features which are normalized and compressed subsequently.The complex classification problem on fault pattern recognition in analog circuit is transferred into feature processing stage by feature extraction based on neural network effectively, which improves the diagnosis efficiency.A fault diagnosis illustration validated this method.
Particle Swarm Based Approach of a Real-Time Discrete Neural Identifier for Linear Induction Motors
Directory of Open Access Journals (Sweden)
Alma Y. Alanis
2013-01-01
Full Text Available This paper focusses on a discrete-time neural identifier applied to a linear induction motor (LIM model, whose model is assumed to be unknown. This neural identifier is robust in presence of external and internal uncertainties. The proposed scheme is based on a discrete-time recurrent high-order neural network (RHONN trained with a novel algorithm based on extended Kalman filter (EKF and particle swarm optimization (PSO, using an online series-parallel con figuration. Real-time results are included in order to illustrate the applicability of the proposed scheme.
A Tool for Fast Development of Modular and Hierarchic Neural Network-based Systems
Directory of Open Access Journals (Sweden)
Francisco Reinaldo
2006-08-01
Full Text Available This paper presents PyramidNet tool as a fast and easy way to develop Modular and Hierarchic Neural Network-based Systems. This tool facilitates the fast emergence of autonomous behaviors in agents because it uses a hierarchic and modular control methodology of heterogeneous learning modules: the pyramid. Using the graphical resources of PyramidNet the user is able to specify a behavior system even having little understanding of artificial neural networks. Experimental tests have shown that a very significant speedup is attained in the development of modular and hierarchic neural network-based systems by using this tool.
A Study on Integrated Wavelet Neural Networks in Fault Diagnosis Based on Information Fusion
Institute of Scientific and Technical Information of China (English)
ANG Xue-ye
2007-01-01
The tight wavelet neural network was constituted by taking the nonlinear Morlet wavelet radices as the excitation function. The idiographic algorithm was presented. It combined the advantages of wavelet analysis and neural networks. The integrated wavelet neural network fault diagnosis system was set up based on both the information fusion technology and actual fault diagnosis, which took the sub-wavelet neural network as primary diagnosis from different sides, then came to the conclusions through decision-making fusion. The realizable policy of the diagnosis system and established principle of the sub-wavelet neural networks were given . It can be deduced from the examples that it takes full advantage of diversified characteristic information, and improves the diagnosis rate.
Model and Algorithm of BP Neural Network Based on Expanded Multichain Quantum Optimization
Directory of Open Access Journals (Sweden)
Baoyu Xu
2015-01-01
Full Text Available The model and algorithm of BP neural network optimized by expanded multichain quantum optimization algorithm with super parallel and ultra-high speed are proposed based on the analysis of the research status quo and defects of BP neural network to overcome the defects of overfitting, the random initial weights, and the oscillation of the fitting and generalization ability along with subtle changes of the network parameters. The method optimizes the structure of the neural network effectively and can overcome a series of problems existing in the BP neural network optimized by basic genetic algorithm such as slow convergence speed, premature convergence, and bad computational stability. The performance of the BP neural network controller is further improved. The simulation experimental results show that the model is with good stability, high precision of the extracted parameters, and good real-time performance and adaptability in the actual parameter extraction.
Effects of Some Neurobiological Factors in a Self-organized Critical Model Based on Neural Networks
Institute of Scientific and Technical Information of China (English)
ZHOU Li-Ming; ZHANG Ying-Yue; CHEN Tian-Lun
2005-01-01
Based on an integrate-and-fire mechanism, we investigate the effect of changing the efficacy of the synapse,the transmitting time-delayed, and the relative refractoryperiod on the self-organized criticality in our neural network model.
Stabilities for nonisentropic Euler-Poisson equations.
Cheung, Ka Luen; Wong, Sen
2015-01-01
We establish the stabilities and blowup results for the nonisentropic Euler-Poisson equations by the energy method. By analysing the second inertia, we show that the classical solutions of the system with attractive forces blow up in finite time in some special dimensions when the energy is negative. Moreover, we obtain the stabilities results for the system in the cases of attractive and repulsive forces.
Euler Characteristic and Quadrilaterals of Normal Surfaces
Indian Academy of Sciences (India)
Tejas Kalelkar
2008-05-01
Let be a compact 3-manifold with a triangulation . We give an inequality relating the Euler characteristic of a surface normally embedded in with the number of normal quadrilaterals in . This gives a relation between a topological invariant of the surface and a quantity derived from its combinatorial description. Secondly, we obtain an inequality relating the number of normal triangles and normal quadrilaterals of , that depends on the maximum number of tetrahedrons that share a vertex in .
Neural Network Based Forecasting of Foreign Currency Exchange Rates
Directory of Open Access Journals (Sweden)
S. Kumar Chandar
2014-06-01
Full Text Available The foreign currency exchange market is the highest and most liquid of the financial markets, with an estimated $1 trillion traded every day. Foreign exchange rates are the most important economic indices in the international financial markets. The prediction of them poses many theoretical and experimental challenges. This paper reports empirical proof that a neural network model is applicable to the prediction of foreign exchange rates. The exchange rates between Indian Rupee and four other major currencies, Pound Sterling, US Dollar, Euro and Japanese Yen are forecast by the trained neural networks. The neural network was trained by three different learning algorithms using historical data to find the suitable algorithm for prediction. The forecasting performance of the proposed system is evaluated using three statistical metrics and compared. The results presented here demonstrate that significantly close prediction can be made without extensive knowledge of market data.
Optimization Design based on BP Neural Network and GA Method
Directory of Open Access Journals (Sweden)
Bing Wang
2013-12-01
Full Text Available This study puts forward one kind optimization controlling solution method on complicated system. At first modeling using neural network then adopt the real data to structure the neural network model of pertinence, make the parameter to seek to the neural network model excellently by mixing GA finally, thus got intelligence to the complicated system to optimize and control. The method can identify network configuration and network training methods. By adopting the number coding and effectively reducing the network size and the network convergence time, increase the network training speed. The study provides this and optimizes relevant MATLAB procedure which controls the method, so long as adjust a little to the concrete problem, can believe this procedure well the optimization of the complicated system controls the problem in the reality of solving.
Study on optimization control method based on artificial neural network
Institute of Scientific and Technical Information of China (English)
FU Hua; SUN Shao-guang; XU Zhen-Iiang
2005-01-01
In the goal optimization and control optimization process the problems with common artificial neural network algorithm are unsure convergence, insufficient post-training network precision, and slow training speed, in which partial minimum value question tends to occur. This paper conducted an in-depth study on the causes of the limitations of the algorithm, presented a rapid artificial neural network algorithm, which is characterized by integrating multiple algorithms and by using their complementary advantages. The salient feature of the method is self-organization, which can effectively prevent the optimized results from tending to be partial minimum values. Overall optimization can be achieved with this method, goal function can be searched for in overall scope. With optimization control of coal mine ventilator as a practical application, the paper proves that by integrating multiple artificial neural network algorithms, best control optimization and goal optimized can be achieved.
Musical expertise affects neural bases of letter recognition.
Proverbio, Alice Mado; Manfredi, Mirella; Zani, Alberto; Adorni, Roberta
2013-02-01
It is known that early music learning (playing of an instrument) modifies functional brain structure (both white and gray matter) and connectivity, especially callosal transfer, motor control/coordination and auditory processing. We compared visual processing of notes and words in 15 professional musicians and 15 controls by recording their synchronized bioelectrical activity (ERPs) in response to words and notes. We found that musical training in childhood (from age ~8 years) modifies neural mechanisms of word reading, whatever the genetic predisposition, which was unknown. While letter processing was strongly left-lateralized in controls, the fusiform (BA37) and inferior occipital gyri (BA18) were activated in both hemispheres in musicians for both word and music processing. The evidence that the neural mechanism of letter processing differed in musicians and controls (being absolutely bilateral in musicians) suggests that musical expertise modifies the neural mechanisms of letter reading.
High Speed PAM -8 Optical Interconnects with Digital Equalization based on Neural Network
DEFF Research Database (Denmark)
Gaiarin, Simone; Pang, Xiaodan; Ozolins, Oskars
2016-01-01
We experimentally evaluate a high-speed optical interconnection link with neural network equalization. Enhanced equalization performances are shown comparing to standard linear FFE for an EML-based 32 GBd PAM-8 signal after 4-km SMF transmission.......We experimentally evaluate a high-speed optical interconnection link with neural network equalization. Enhanced equalization performances are shown comparing to standard linear FFE for an EML-based 32 GBd PAM-8 signal after 4-km SMF transmission....
Prediction Model of Soil Nutrients Loss Based on Artificial Neural Network
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Malian － River basin. The results by calculating show that the solution based on BP algorithms are consis tent with those based multiple－variables linear regression model. They also indicate that BP model in this paper is reasonable and BP algorithms are feasible.
A Sliding Mode Control-based on a RBF Neural Network for Deburring Industry Robotic Systems
Yong Tao; Jiaqi Zheng; Yuanchang Lin
2016-01-01
A sliding mode control method based on radial basis function (RBF) neural network is proposed for the deburring of industry robotic systems. First, a dynamic model for deburring the robot system is established. Then, a conventional SMC scheme is introduced for the joint position tracking of robot manipulators. The RBF neural network based sliding mode control (RBFNN-SMC) has the ability to learn uncertain control actions. In the RBFNN-SMC scheme, the adaptive tuning algorithms for network par...
An attractor-based complexity measurement for Boolean recurrent neural networks.
Directory of Open Access Journals (Sweden)
Jérémie Cabessa
Full Text Available We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics. This complexity measurement is achieved by first proving a computational equivalence between Boolean recurrent neural networks and some specific class of ω-automata, and then translating the most refined classification of ω-automata to the Boolean neural network context. As a result, a hierarchical classification of Boolean neural networks based on their attractive dynamics is obtained, thus providing a novel refined attractor-based complexity measurement for Boolean recurrent neural networks. These results provide new theoretical insights to the computational and dynamical capabilities of neural networks according to their attractive potentialities. An application of our findings is illustrated by the analysis of the dynamics of a simplified model of the basal ganglia-thalamocortical network simulated by a Boolean recurrent neural network. This example shows the significance of measuring network complexity, and how our results bear new founding elements for the understanding of the complexity of real brain circuits.
An attractor-based complexity measurement for Boolean recurrent neural networks.
Cabessa, Jérémie; Villa, Alessandro E P
2014-01-01
We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics. This complexity measurement is achieved by first proving a computational equivalence between Boolean recurrent neural networks and some specific class of ω-automata, and then translating the most refined classification of ω-automata to the Boolean neural network context. As a result, a hierarchical classification of Boolean neural networks based on their attractive dynamics is obtained, thus providing a novel refined attractor-based complexity measurement for Boolean recurrent neural networks. These results provide new theoretical insights to the computational and dynamical capabilities of neural networks according to their attractive potentialities. An application of our findings is illustrated by the analysis of the dynamics of a simplified model of the basal ganglia-thalamocortical network simulated by a Boolean recurrent neural network. This example shows the significance of measuring network complexity, and how our results bear new founding elements for the understanding of the complexity of real brain circuits.
The Chebyshev-polynomials-based unified model neural networks for function approximation.
Lee, T T; Jeng, J T
1998-01-01
In this paper, we propose the approximate transformable technique, which includes the direct transformation and indirect transformation, to obtain a Chebyshev-Polynomials-Based (CPB) unified model neural networks for feedforward/recurrent neural networks via Chebyshev polynomials approximation. Based on this approximate transformable technique, we have derived the relationship between the single-layer neural networks and multilayer perceptron neural networks. It is shown that the CPB unified model neural networks can be represented as a functional link networks that are based on Chebyshev polynomials, and those networks use the recursive least square method with forgetting factor as learning algorithm. It turns out that the CPB unified model neural networks not only has the same capability of universal approximator, but also has faster learning speed than conventional feedforward/recurrent neural networks. Furthermore, we have also derived the condition such that the unified model generating by Chebyshev polynomials is optimal in the sense of error least square approximation in the single variable ease. Computer simulations show that the proposed method does have the capability of universal approximator in some functional approximation with considerable reduction in learning time.
Enhanced Neural Cell Adhesion and Neurite Outgrowth on Graphene-Based Biomimetic Substrates
Directory of Open Access Journals (Sweden)
Suck Won Hong
2014-01-01
Full Text Available Neural cell adhesion and neurite outgrowth were examined on graphene-based biomimetic substrates. The biocompatibility of carbon nanomaterials such as graphene and carbon nanotubes (CNTs, that is, single-walled and multiwalled CNTs, against pheochromocytoma-derived PC-12 neural cells was also evaluated by quantifying metabolic activity (with WST-8 assay, intracellular oxidative stress (with ROS assay, and membrane integrity (with LDH assay. Graphene films were grown by using chemical vapor deposition and were then coated onto glass coverslips by using the scooping method. Graphene sheets were patterned on SiO2/Si substrates by using photolithography and were then covered with serum for a neural cell culture. Both types of CNTs induced significant dose-dependent decreases in the viability of PC-12 cells, whereas graphene exerted adverse effects on the neural cells just at over 62.5 ppm. This result implies that graphene and CNTs, even though they were the same carbon-based nanomaterials, show differential influences on neural cells. Furthermore, graphene-coated or graphene-patterned substrates were shown to substantially enhance the adhesion and neurite outgrowth of PC-12 cells. These results suggest that graphene-based substrates as biomimetic cues have good biocompatibility as well as a unique surface property that can enhance the neural cells, which would open up enormous opportunities in neural regeneration and nanomedicine.
Enhanced neural cell adhesion and neurite outgrowth on graphene-based biomimetic substrates.
Hong, Suck Won; Lee, Jong Ho; Kang, Seok Hee; Hwang, Eun Young; Hwang, Yu-Shik; Lee, Mi Hee; Han, Dong-Wook; Park, Jong-Chul
2014-01-01
Neural cell adhesion and neurite outgrowth were examined on graphene-based biomimetic substrates. The biocompatibility of carbon nanomaterials such as graphene and carbon nanotubes (CNTs), that is, single-walled and multiwalled CNTs, against pheochromocytoma-derived PC-12 neural cells was also evaluated by quantifying metabolic activity (with WST-8 assay), intracellular oxidative stress (with ROS assay), and membrane integrity (with LDH assay). Graphene films were grown by using chemical vapor deposition and were then coated onto glass coverslips by using the scooping method. Graphene sheets were patterned on SiO2/Si substrates by using photolithography and were then covered with serum for a neural cell culture. Both types of CNTs induced significant dose-dependent decreases in the viability of PC-12 cells, whereas graphene exerted adverse effects on the neural cells just at over 62.5 ppm. This result implies that graphene and CNTs, even though they were the same carbon-based nanomaterials, show differential influences on neural cells. Furthermore, graphene-coated or graphene-patterned substrates were shown to substantially enhance the adhesion and neurite outgrowth of PC-12 cells. These results suggest that graphene-based substrates as biomimetic cues have good biocompatibility as well as a unique surface property that can enhance the neural cells, which would open up enormous opportunities in neural regeneration and nanomedicine.
Solving Nonlinear Euler Equations with Arbitrary Accuracy
Dyson, Rodger W.
2005-01-01
A computer program that efficiently solves the time-dependent, nonlinear Euler equations in two dimensions to an arbitrarily high order of accuracy has been developed. The program implements a modified form of a prior arbitrary- accuracy simulation algorithm that is a member of the class of algorithms known in the art as modified expansion solution approximation (MESA) schemes. Whereas millions of lines of code were needed to implement the prior MESA algorithm, it is possible to implement the present MESA algorithm by use of one or a few pages of Fortran code, the exact amount depending on the specific application. The ability to solve the Euler equations to arbitrarily high accuracy is especially beneficial in simulations of aeroacoustic effects in settings in which fully nonlinear behavior is expected - for example, at stagnation points of fan blades, where linearizing assumptions break down. At these locations, it is necessary to solve the full nonlinear Euler equations, and inasmuch as the acoustical energy is of the order of 4 to 5 orders of magnitude below that of the mean flow, it is necessary to achieve an overall fractional error of less than 10-6 in order to faithfully simulate entropy, vortical, and acoustical waves.
Ozasa, Kazunari; Aono, Masashi; Maeda, Mizuo; Hara, Masahiko
In order to develop an adaptive computing system, we investigate microscopic optical feedback to a group of microbes (Euglena gracilis in this study) with a neural network algorithm, expecting that the unique characteristics of microbes, especially their strategies to survive/adapt against unfavorable environmental stimuli, will explicitly determine the temporal evolution of the microbe-based feedback system. The photophobic reactions of Euglena are extracted from experiments, and built in the Monte-Carlo simulation of a microbe-based neurocomputing. The simulation revealed a good performance of Euglena-based neurocomputing. Dynamic transition among the solutions is discussed from the viewpoint of feedback instability.
Continuous speech recognition based on convolutional neural network
Zhang, Qing-qing; Liu, Yong; Pan, Jie-lin; Yan, Yong-hong
2015-07-01
Convolutional Neural Networks (CNNs), which showed success in achieving translation invariance for many image processing tasks, are investigated for continuous speech recognitions in the paper. Compared to Deep Neural Networks (DNNs), which have been proven to be successful in many speech recognition tasks nowadays, CNNs can reduce the NN model sizes significantly, and at the same time achieve even better recognition accuracies. Experiments on standard speech corpus TIMIT showed that CNNs outperformed DNNs in the term of the accuracy when CNNs had even smaller model size.
Model predictive combustion control based on neural nets
Energy Technology Data Exchange (ETDEWEB)
Schmidt, D. [Powitec Intelligent Technologies GmbH, Essen (Germany); Kampschreuer, T. [AVR Afvalverwerking B.V., Duiven/Arnheim (Netherlands)
2008-07-01
The first closed-loop Neural Net combustion controller in the Netherlands has been installed at the HVC plant in Alkmaar. During the summer 2006 the first of the 'old' three lines was equipped with an individually controllable primary air distribution. As 'fire controller' the combustion optimiser from Powitec, the PiT Navigator, was selected, a system using digital image processing and neural nets. This paper shows the results from operating the plant with and without the NMPC optimiser and from the performance tests. (orig.)
Application of functional-link neural network in evaluation of sublayer suspension based on FWD test
Institute of Scientific and Technical Information of China (English)
陈瑜; 张起森
2004-01-01
Several methods for evaluating the sublayer suspension beneath old pavement with falling weight deflectormeter(FWD), were summarized and the respective advantages and disadvantages were analyzed. Based on these methods, the evaluation principles were improved and a new type of the neural network, functional-link neural network was proposed to evaluate the sublayer suspension with FWD test results. The concept of function link, learning method of functional-link neural network and the establishment process of neural network model were studied in detail. Based on the old pavement over-repairing engineering of Kaiping section, Guangdong Province in G325 National Highway, the application of functional-link neural network in evaluation of sublayer suspension beneath old pavement based on FWD test data on the spot was investigated. When learning rate is 0.1 and training cycles are 405, the functional-link network error is less than 0.0001, while the optimum chosen 4-8-1 BP needs over 10000 training cycles to reach the same accuracy with less precise evaluation results. Therefore, in contrast to common BP neural network,the functional-link neural network adopts single layer structure to learn and calculate, which simplifies the network, accelerates the convergence speed and improves the accuracy. Moreover the trained functional-link neural network can be adopted to directly evaluate the sublayer suspension based on FWD test data on the site. Engineering practice indicates that the functional-link neural model gains very excellent results and effectively guides the pavement over-repairing construction.
Decentralized neural identifier and control for nonlinear systems based on extended Kalman filter.
Castañeda, Carlos E; Esquivel, P
2012-07-01
A time-varying learning algorithm for recurrent high order neural network in order to identify and control nonlinear systems which integrates the use of a statistical framework is proposed. The learning algorithm is based in the extended Kalman filter, where the associated state and measurement noises covariance matrices are composed by the coupled variance between the plant states. The formulation allows identification of interactions associate between plant state and the neural convergence. Furthermore, a sliding window-based method for dynamical modeling of nonstationary systems is presented to improve the neural identification in the proposed methodology. The efficiency and accuracy of the proposed method is assessed to a five degree of freedom (DOF) robot manipulator where based on the time-varying neural identifier model, the decentralized discrete-time block control and sliding mode techniques are used to design independent controllers and develop the trajectory tracking for each DOF.
Global exponential almost periodicity of a delayed memristor-based neural networks.
Chen, Jiejie; Zeng, Zhigang; Jiang, Ping
2014-12-01
In this paper, the existence, uniqueness and stability of almost periodic solution for a class of delayed memristor-based neural networks are studied. By using a new Lyapunov function method, the neural network that has a unique almost periodic solution, which is globally exponentially stable is proved. Moreover, the obtained conclusion on the almost periodic solution is applied to prove the existence and stability of periodic solution (or equilibrium point) for delayed memristor-based neural networks with periodic coefficients (or constant coefficients). The obtained results are helpful to design the global exponential stability of almost periodic oscillatory memristor-based neural networks. Three numerical examples and simulations are also given to show the feasibility of our results.
Battery Performance Modelling ad Simulation: a Neural Network Based Approach
Ottavianelli, Giuseppe; Donati, Alessandro
2002-01-01
This project has developed on the background of ongoing researches within the Control Technology Unit (TOS-OSC) of the Special Projects Division at the European Space Operations Centre (ESOC) of the European Space Agency. The purpose of this research is to develop and validate an Artificial Neural Network tool (ANN) able to model, simulate and predict the Cluster II battery system's performance degradation. (Cluster II mission is made of four spacecraft flying in tetrahedral formation and aimed to observe and study the interaction between sun and earth by passing in and out of our planet's magnetic field). This prototype tool, named BAPER and developed with a commercial neural network toolbox, could be used to support short and medium term mission planning in order to improve and maximise the batteries lifetime, determining which are the future best charge/discharge cycles for the batteries given their present states, in view of a Cluster II mission extension. This study focuses on the five Silver-Cadmium batteries onboard of Tango, the fourth Cluster II satellite, but time restrains have allowed so far to perform an assessment only on the first battery. In their most basic form, ANNs are hyper-dimensional curve fits for non-linear data. With their remarkable ability to derive meaning from complicated or imprecise history data, ANN can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. ANNs learn by example, and this is why they can be described as an inductive, or data-based models for the simulation of input/target mappings. A trained ANN can be thought of as an "expert" in the category of information it has been given to analyse, and this expert can then be used, as in this project, to provide projections given new situations of interest and answer "what if" questions. The most appropriate algorithm, in terms of training speed and memory storage requirements, is clearly the Levenberg
Active Control of Sound based on Diagonal Recurrent Neural Network
Jayawardhana, Bayu; Xie, Lihua; Yuan, Shuqing
2002-01-01
Recurrent neural network has been known for its dynamic mapping and better suited for nonlinear dynamical system. Nonlinear controller may be needed in cases where the actuators exhibit the nonlinear characteristics, or in cases when the structure to be controlled exhibits nonlinear behavior. The fe
Neural network based satellite tracking for deep space applications
Amoozegar, F.; Ruggier, C.
2003-01-01
The objective of this paper is to provide a survey of neural network trends as applied to the tracking of spacecrafts in deep space at Ka-band under various weather conditions and examine the trade-off between tracing accuracy and communication link performance.
A neural network based seafloor classification using acoustic backscatter
Digital Repository Service at National Institute of Oceanography (India)
Chakraborty, B.
This paper presents a study results of the Artificial Neural Network (ANN) architectures [Self-Organizing Map (SOM) and Multi-Layer Perceptron (MLP)] using single beam echosounding data. The single beam echosounder, operable at 12 kHz, has been used...
Neural network-based control using Lyapunov functions
Luxemburg, Leon A.
1993-01-01
We have successfully demonstrated how the problem of stabilization of plants can be reduced to a problem of approximation of functions. Neural networks have been shown to have approximating and interpolating properties. This approach is good for linear and nonlinear plants. Software has been generated to demonstrate this approach.
Gore, Russell K.; Choi, Yoonsu; Bellamkonda, Ravi; English, Arthur
2015-02-01
group of awake and behaving animals. These unique findings provide preliminary evidence that efferent, volitional motor potentials can be recorded from the microchannel-based peripheral neural interface; a critical requirement for any neural interface intended to facilitate direct neural control of external technologies.
Ramamoorthy, P. A.; Huang, Song; Govind, Girish
1991-01-01
In fault diagnosis, control and real-time monitoring, both timing and accuracy are critical for operators or machines to reach proper solutions or appropriate actions. Expert systems are becoming more popular in the manufacturing community for dealing with such problems. In recent years, neural networks have revived and their applications have spread to many areas of science and engineering. A method of using neural networks to implement rule-based expert systems for time-critical applications is discussed here. This method can convert a given rule-based system into a neural network with fixed weights and thresholds. The rules governing the translation are presented along with some examples. We also present the results of automated machine implementation of such networks from the given rule-base. This significantly simplifies the translation process to neural network expert systems from conventional rule-based systems. Results comparing the performance of the proposed approach based on neural networks vs. the classical approach are given. The possibility of very large scale integration (VLSI) realization of such neural network expert systems is also discussed.
Directory of Open Access Journals (Sweden)
Bahita Mohamed
2011-01-01
Full Text Available In this work, we introduce an adaptive neural network controller for a class of nonlinear systems. The approach uses two Radial Basis Functions, RBF networks. The first RBF network is used to approximate the ideal control law which cannot be implemented since the dynamics of the system are unknown. The second RBF network is used for on-line estimating the control gain which is a nonlinear and unknown function of the states. The updating laws for the combined estimator and controller are derived through Lyapunov analysis. Asymptotic stability is established with the tracking errors converging to a neighborhood of the origin. Finally, the proposed method is applied to control and stabilize the inverted pendulum system.
Symmetries of the Euler compressible flow equations for general equation of state
Energy Technology Data Exchange (ETDEWEB)
Boyd, Zachary M. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Ramsey, Scott D. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Baty, Roy S. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2015-10-15
The Euler compressible flow equations exhibit different Lie symmetries depending on the equation of state (EOS) of the medium in which the flow occurs. This means that, in general, different types of similarity solution will be available in different flow media. We present a comprehensive classification of all EOS’s to which the Euler equations apply, based on the Lie symmetries admitted by the corresponding flow equations, restricting to the case of 1-D planar, cylindrical, or spherical geometry. The results are conveniently summarized in tables. This analysis also clarifies past work by Axford and Ovsiannikov on symmetry classification.
Eigenmode Analysis of Boundary Conditions for One-Dimensional Preconditioned Euler Equations
Darmofal, David L.
1998-01-01
An analysis of the effect of local preconditioning on boundary conditions for the subsonic, one-dimensional Euler equations is presented. Decay rates for the eigenmodes of the initial boundary value problem are determined for different boundary conditions. Riemann invariant boundary conditions based on the unpreconditioned Euler equations are shown to be reflective with preconditioning, and, at low Mach numbers, disturbances do not decay. Other boundary conditions are investigated which are non-reflective with preconditioning and numerical results are presented confirming the analysis.
Asymptotic solution for high vorticity regions in incompressible 3D Euler equations
Agafontsev, D S; Mailybaev, A A
2016-01-01
Incompressible 3D Euler equations develop high vorticity in very thin pancake-like regions from generic large-scale initial conditions. In this work we propose an exact solution of the Euler equations for the asymptotic pancake evolution. This solution combines a shear flow aligned with an asymmetric straining flow, and is characterized by a single asymmetry parameter and an arbitrary transversal vorticity profile. The analysis is based on detailed comparison with numerical simulations performed using a pseudo-spectral method in anisotropic grids of up to 972 x 2048 x 4096.
Chao, Tien-Hsin; Stoner, William W.
1993-01-01
An optical neural network based on the neocognitron paradigm is introduced. A novel aspect of the architecture design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by feeding back the ouput of the feature correlator interatively to the input spatial light modulator and by updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intraclass fault tolerance and interclass discrimination is achieved. A detailed system description is provided. Experimental demonstrations of a two-layer neural network for space-object discrimination is also presented.
Research on the controller of an arc welding process based on a PID neural network
Institute of Scientific and Technical Information of China (English)
Kuanfang HE; Shisheng HUANG
2008-01-01
A controller based on a PID neural network(PIDNN)is proposed for an arc welding power source whose output characteristic in responding to a given value is quickly and intelligently controlled in the welding process.The new method syncretizes the PID control strategy and neural network to control the welding process intelligently,so it has the merit of PID control rules and the trait of better information disposal ability of the neural network.The results of simulation show that the controller has the properties of quick response,low overshoot quick convergence and good stable accuracy,which meet the requirements for control of the welding process.
Synchronization control of memristor-based recurrent neural networks with perturbations.
Wang, Weiping; Li, Lixiang; Peng, Haipeng; Xiao, Jinghua; Yang, Yixian
2014-05-01
In this paper, the synchronization control of memristor-based recurrent neural networks with impulsive perturbations or boundary perturbations is studied. We find that the memristive connection weights have a certain relationship with the stability of the system. Some criteria are obtained to guarantee that memristive neural networks have strong noise tolerance capability. Two kinds of controllers are designed so that the memristive neural networks with perturbations can converge to the equilibrium points, which evoke human's memory patterns. The analysis in this paper employs the differential inclusions theory and the Lyapunov functional method. Numerical examples are given to show the effectiveness of our results.
STUDY ON INJECTION AND IGNITION CONTROL OF GASOLINE ENGINE BASED ON BP NEURAL NETWORK
Institute of Scientific and Technical Information of China (English)
Zhang Cuiping; Yang Qingfo
2003-01-01
According to advantages of neural network and characteristics of operating procedures of engine, a new strategy is represented on the control of fuel injection and ignition timing of gasoline engine based on improved BP network algorithm. The optimum ignition advance angle and fuel injection pulse band of engine under different speed and load are tested for the samples training network, focusing on the study of the design method and procedure of BP neural network in engine injection and ignition control. The results show that artificial neural network technique can meet the requirement of engine injection and ignition control. The method is feasible for improving power performance, economy and emission performances of gasoline engine.
Neural Network Based on Rough Sets and Its Application to Remote Sensing Image Classification
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
This paper presents a new kind of back propagation neural network (BPNN) based on rough sets,called rough back propagation neural network (RBPNN).The architecture and training method of RBPNN are presented and the survey and analysis of RBPNN for the classification of remote sensing multi-spectral image is discussed.The successful application of RBPNN to a land cover classification illustrates the simple computation and high accuracy of the new neural network and the flexibility and practicality of this new approach.
Layer Winner-Take-All neural networks based on existing competitive structures.
Chen, C M; Yang, J F
2000-01-01
In this paper, we propose generalized layer winner-take-all (WTA) neural networks based on the suggested full WTA networks, which can be extended from any existing WTA structure with a simple weighted-and-sum neuron. With modular regularity and local connection, the layer WTA network in either hierarchical or recursive structure is suitable for a large number of competitors. The complexity and convergence performances of layer and direct WTA neural networks are analyzed. Simulation results and theoretical analyzes verify that the layer WTA neural networks with extendibility outperform their original direct WTA structures in aspects of low complexity and fast convergence.
Neural network modeling for dynamic pulsed GTAW process with wire filler based on MATLAB
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Double-sided weld pool shapes were determined by multiple welding parameters and wire feed parameters during pulsed GTAW with wire filler. Aiming at such a system with multiple inputs and outputs, an effective modeling method, consisting of the impulse signal design, model structure and parameter identification and verification, was developed based on MATLAB software. Then, dynamic neural network models, TDNNM (Topside dynamic neural network model) and BHDNNM (Backside width and topside height dynamic neural network model), were established to predict double-sided shape parameters of the weld pool. The characteristic relationship of the welding process was simulated and analyzed with the models.
Institute of Scientific and Technical Information of China (English)
JI Guangrong; SUN Jie; ZHAO Wencang; ZHANG Hande
2006-01-01
This paper proposes a red tide monitoring method based on clustering and modular neural networks. To obtain the features of red tide from a mass of aerial remote sensing hyperspectral data, first the Log Residual Correction (LRC) is used to normalize the data, and then clustering analysis is adopted to select and form the training samples for the neural networks. For rapid monitoring, the discriminator is composed of modular neural networks, whose structure and learning parameters are determined by an Adaptive Genetic Algorithm (AGA). The experiments showed that this method can monitor red tide rapidly and effectively.
Xu, Changjin; Li, Peiluan; Pang, Yicheng
2016-12-01
In this letter, we deal with a class of memristor-based neural networks with distributed leakage delays. By applying a new Lyapunov function method, we obtain some sufficient conditions that ensure the existence, uniqueness, and global exponential stability of almost periodic solutions of neural networks. We apply the results of this solution to prove the existence and stability of periodic solutions for this delayed neural network with periodic coefficients. We then provide an example to illustrate the effectiveness of the theoretical results. Our results are completely new and complement the previous studies Chen, Zeng, and Jiang ( 2014 ) and Jiang, Zeng, and Chen ( 2015 ).
Building a Tax Predictive Model Based on the Cloud Neural Network
Institute of Scientific and Technical Information of China (English)
田永青; 李志; 朱仲英
2003-01-01
Tax is very important to the whole country, so a scientific tax predictive model is needed. This paper introduces the theory of the cloud model. On this basis, it presents a cloud neural network, and analyzes the main factors which influence the tax revenue. Then if proposes a tax predictive model based on the cloud neural network. The model combines the strongpoints of the cloud model and the neural network. The experiment and simulation results show the effectiveness of the algorithm in this paper.
Finite-time synchronization control of a class of memristor-based recurrent neural networks.
Jiang, Minghui; Wang, Shuangtao; Mei, Jun; Shen, Yanjun
2015-03-01
This paper presents a global and local finite-time synchronization control law for memristor neural networks. By utilizing the drive-response concept, differential inclusions theory, and Lyapunov functional method, we establish several sufficient conditions for finite-time synchronization between the master and corresponding slave memristor-based neural network with the designed controller. In comparison with the existing results, the proposed stability conditions are new, and the obtained results extend some previous works on conventional recurrent neural networks. Two numerical examples are provided to illustrate the effective of the design method.
Passivity of memristor-based BAM neural networks with different memductance and uncertain delays.
Anbuvithya, R; Mathiyalagan, K; Sakthivel, R; Prakash, P
2016-08-01
This paper addresses the passivity problem for a class of memristor-based bidirectional associate memory (BAM) neural networks with uncertain time-varying delays. In particular, the proposed memristive BAM neural networks is formulated with two different types of memductance functions. By constructing proper Lyapunov-Krasovskii functional and using differential inclusions theory, a new set of sufficient condition is obtained in terms of linear matrix inequalities which guarantee the passivity criteria for the considered neural networks. Finally, two numerical examples are given to illustrate the effectiveness of the proposed theoretical results.
Photovoltaic Power Prediction Based on Scene Simulation Knowledge Mining and Adaptive Neural Network
Directory of Open Access Journals (Sweden)
Dongxiao Niu
2013-01-01
Full Text Available Influenced by light, temperature, atmospheric pressure, and some other random factors, photovoltaic power has characteristics of volatility and intermittent. Accurately forecasting photovoltaic power can effectively improve security and stability of power grid system. The paper comprehensively analyzes influence of light intensity, day type, temperature, and season on photovoltaic power. According to the proposed scene simulation knowledge mining (SSKM technique, the influencing factors are clustered and fused into prediction model. Combining adaptive algorithm with neural network, adaptive neural network prediction model is established. Actual numerical example verifies the effectiveness and applicability of the proposed photovoltaic power prediction model based on scene simulation knowledge mining and adaptive neural network.
Correlation methods of base-level cycle based on wavelet neural network
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
The authors discussed the method of wavelet neural network (WNN) for correlation of base-level cycle. A new vectored method of well log data was proposed. Through the training with the known data set, the WNN can remenber the cycle pattern characteristic of the well log curves. By the trained WNN to identify the cycle pattern in the vectored log data, the ocrrelation process among the well cycles was completed. The application indicates that it is highly efficient and reliable in base-level cycle correlation.
Convergence of Compressible Euler-Maxwell Equations to Compressible Euler-Poisson Equations
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
In this paper, the convergence of time-dependent Euler-Maxwell equations to compressible Euler-Poisson equations in a torus via the non-relativistic limit is studied.The local existence of smooth solutions to both systems is proved by using energy estimates for first order symmetrizable hyperbolic systems. For well prepared initial data the convergence of solutions is rigorously justified by an analysis of asymptotic expansions up to any order. The authors perform also an initial layer analysis for general initial data and prove the convergence of asymptotic expansions up to first order.
Neural Network-Based Resistance Spot Welding Control and Quality Prediction
Energy Technology Data Exchange (ETDEWEB)
Allen, J.D., Jr.; Ivezic, N.D.; Zacharia, T.
1999-07-10
This paper describes the development and evaluation of neural network-based systems for industrial resistance spot welding process control and weld quality assessment. The developed systems utilize recurrent neural networks for process control and both recurrent networks and static networks for quality prediction. The first section describes a system capable of both welding process control and real-time weld quality assessment, The second describes the development and evaluation of a static neural network-based weld quality assessment system that relied on experimental design to limit the influence of environmental variability. Relevant data analysis methods are also discussed. The weld classifier resulting from the analysis successfldly balances predictive power and simplicity of interpretation. The results presented for both systems demonstrate clearly that neural networks can be employed to address two significant problems common to the resistance spot welding industry, control of the process itself, and non-destructive determination of resulting weld quality.
Instantaneous Gradient Based Dual Mode Feed-Forward Neural Network Blind Equalization Algorithm
Directory of Open Access Journals (Sweden)
Ying Xiao
2013-01-01
Full Text Available To further improve the performance of feed-forward neural network blind equalization based on Constant Modulus Algorithm (CMA cost function, an instantaneous gradient based dual mode between Modified Constant Modulus Algorithm (MCMA and Decision Directed (DD algorithm was proposed. The neural network weights change quantity of the adjacent iterative process is defined as instantaneous gradient. After the network converges, the weights of neural network to achieve a stable energy state and the instantaneous gradient would be zero. Therefore dual mode algorithm can be realized by criterion which set according to the instantaneous gradient. Computer simulation results show that the dual mode feed-forward neural network blind equalization algorithm proposed in this study improves the convergence rate and convergence precision effectively, at the same time, has good restart and tracking ability under channel burst interference condition.
Cheng, Longlong; Zhang, Guangju; Wan, Baikun; Hao, Linlin; Qi, Hongzhi; Ming, Dong
2009-01-01
Functional electrical stimulation (FES) has been widely used in the area of neural engineering. It utilizes electrical current to activate nerves innervating extremities affected by paralysis. An effective combination of a traditional PID controller and a neural network, being capable of nonlinear expression and adaptive learning property, supply a more reliable approach to construct FES controller that help the paraplegia complete the action they want. A FES system tuned by Radial Basis Function (RBF) Neural Network-based Proportional-Integral-Derivative (PID) model was designed to control the knee joint according to the desired trajectory through stimulation of lower limbs muscles in this paper. Experiment result shows that the FES system with RBF Neural Network-based PID model get a better performance when tracking the preset trajectory of knee angle comparing with the system adjusted by Ziegler- Nichols tuning PID model.
The optimum design of the pressure control spring of the relief valve based on neural networks
Institute of Scientific and Technical Information of China (English)
FU Xiao-jin
2006-01-01
Based on the traditional optimization methods about the pressure control spring of the relief valve and combined with the advantages of neural network, this paper put forward the optimization method with many parameters and a lot of constraints based on neural network. The object function of optimization is transformed into the energy function of the neural network and the mathematical model of neural network optimization about the pressure control spring of the relief valve is set up in this method which also puts forward its own algorithm. An example of application shows that network convergence gets stable state of minimization object function E, and object function converges to the utmost minimum point with steady function, then best solution is gained, which makes the design plan better. The algorithm of solution for the problem is effective about the optimum design of the pressure control spring and improves the performance target.
Design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization
Castillo, Oscar; Kacprzyk, Janusz
2015-01-01
This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in eight main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on fuzzy systems. The second part contains papers with the main theme of neural networks theory, which are basically papers dealing with new concepts and algorithms in neural networks. The third part contains papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The fourth part contains papers describing new nature-inspired optimization algorithms. The fifth part presents div...
Hambli, Ridha
2011-01-01
The aim of this paper is to develop a multiscale hierarchical hybrid model based on finite element analysis and neural network computation to link mesoscopic scale (trabecular network level) and macroscopic (whole bone level) to simulate bone remodelling process. Because whole bone simulation considering the 3D trabecular level is time consuming, the finite element calculation is performed at macroscopic level and a trained neural network are employed as numerical devices for substituting the finite element code needed for the mesoscale prediction. The bone mechanical properties are updated at macroscopic scale depending on the morphological organization at the mesoscopic computed by the trained neural network. The digital image-based modeling technique using m-CT and voxel finite element mesh is used to capture 2 mm3 Representative Volume Elements at mesoscale level in a femur head. The input data for the artificial neural network are a set of bone material parameters, boundary conditions and the applied str...
FAULT DIAGNOSIS OF ROTATING MACHINERY USING KNOWLEDGE-BASED FUZZY NEURAL NETWORK
Institute of Scientific and Technical Information of China (English)
LI Ru-qiang; CHEN Jin; WU Xing
2006-01-01
A novel knowledge-based fuzzy neural network (KBFNN) for fault diagnosis is presented. Crude rules were extracted and the corresponding dependent factors and antecedent coverage factors were calculated firstly from the diagnostic sample based on rough sets theory. Then the number of rules was used to construct partially the structure of a fuzzy neural network and those factors were implemented as initial weights, with fuzzy output parameters being optimized by genetic algorithm. Such fuzzy neural network was called KBFNN. This KBFNN was utilized to identify typical faults of rotating machinery.Diagnostic results show that it has those merits of shorter training time and higher right diagnostic level compared to general fuzzy neural networks.
Technique of information hiding based on neural network
Xu, Li; Tao, Gu
2007-04-01
A neural network algorithm is proposed which can conceal different files effectively such as *.exe, *.com, *.doc, *.txt and self-defined file formats. First, the important contents of the file are coded into a binary array. The total number of 0s and 1s is N. Second, to make the neural network learn the sample space, N pixel values and their closely relevant pixel values are randomly chosen from a color BMP format image and used to train the neural network, thus we get a group of network parameters and its outputs Y1. Each element of Y1 is increased by 0 or 1 according to the zeros and ones from the array, the increased Y1is called Y2. Third, using the transmitted parameters, a receiver can restore the neural network. Network outputs Y3(Y1) can also be obtained by simulating the restored neural network with the seed pixel values. Finally, the encrypted information can be decoded by Y2 and Y3. The acquisition of parameters and Y1 is different when the neural network is trained each time, so the algorithm has the characteristic of a one-time pad, which can enhance the correspondence security. Because the network colligates the chosen pixel values and their closely relevant pixel values, a cryptanalyst can not restore the network parameters and Y3 easily. Without the seed picture and the password, he does not know the encrypted data even if he knows the network parameters and Y2. If he only has the seed picture, he does not know the encrypted contents either, because there is no other information in the picture, which just is used to train the network. Using the same algorithm, the fragile watermark can be embedded into Y1 simultaneously. Besides telling you whether Y2 or network parameters have been tampered with, the fragile watermark could as well, reflecting the distortion status in the spatial domain of the tampered image. Therefore, the proposed method is of significance in practice.
Dynamic Stability of Euler Beams under Axial Unsteady Wind Force
Directory of Open Access Journals (Sweden)
You-Qin Huang
2014-01-01
Full Text Available Dynamic instability of beams in complex structures caused by unsteady wind load has occurred more frequently. However, studies on the parametric resonance of beams are generally limited to harmonic loads, while arbitrary dynamic load is rarely involved. The critical frequency equation for simply supported Euler beams with uniform section under arbitrary axial dynamic forces is firstly derived in this paper based on the Mathieu-Hill equation. Dynamic instability regions with high precision are then calculated by a presented eigenvalue method. Further, the dynamically unstable state of beams under the wind force with any mean or fluctuating component is determined by load normalization, and the wind-induced parametric resonant response is computed by the Runge-Kutta approach. Finally, a measured wind load time-history is input into the dynamic system to indicate that the proposed methods are effective. This study presents a new method to determine the wind-induced dynamic stability of Euler beams. The beam would become dynamically unstable provided that the parametric point, denoting the relation between load properties and structural frequency, is located in the instability region, no matter whether the wind load component is large or not.
Directory of Open Access Journals (Sweden)
Necla ÖZKAYA
2007-01-01
Full Text Available Automatic fingerprint recognition systems are utilised for personal identification with the use of comparisons of local ridge characteristics and their relationships. Critical stages in personal identification are to extract features automatically, fast and reliably from the input fingerprint images. In this study, a new approach based on artificial neural networks to extract minutiae from fingerprint images is developed and introduced. The results have shown that artificial neural networks achieve the minutiae extraction from fingerprint images with high accuracy.
Artificial neural network based pulse shape analysis in cryogenic detectors for rare event searches
Energy Technology Data Exchange (ETDEWEB)
Zoeller, Andreas [Physik Department E15, Technische Universitaet Muenchen, 85748 Garching (Germany); Collaboration: CRESST-Collaboration
2015-07-01
We present a method based on an Artificial Neural Network for a pulse shape analysis in cryogenic detectors. To train the neural network a huge amount of pulses with known properties are necessary. Therefore, a data-driven simulation used to generate these sets is explained. Furthermore, these simulations allow detailed studies, especially of the cut efficiency and the signal purity of the developed cut. First results are presented and compared with the performance of alternative algorithms.
Breakout Prediction Based on BP Neural Network in Continuous Casting Process
Directory of Open Access Journals (Sweden)
Zhang Ben-guo
2016-01-01
Full Text Available An improved BP neural network model was presented by modifying the learning algorithm of the traditional BP neural network, based on the Levenberg-Marquardt algorithm, and was applied to the breakout prediction system in the continuous casting process. The results showed that the accuracy rate of the model for the temperature pattern of sticking breakout was 96.43%, and the quote rate was 100%, that verified the feasibility of the model.
Neural network predicts sequence of TP53 gene based on DNA chip
DEFF Research Database (Denmark)
Spicker, J.S.; Wikman, F.; Lu, M.L.;
2002-01-01
We have trained an artificial neural network to predict the sequence of the human TP53 tumor suppressor gene based on a p53 GeneChip. The trained neural network uses as input the fluorescence intensities of DNA hybridized to oligonucleotides on the surface of the chip and makes between zero...... and four errors in the predicted 1300 bp sequence when tested on wild-type TP53 sequence....
Adaptive control of chaotic systems based on a single layer neural network
Energy Technology Data Exchange (ETDEWEB)
Shen Liqun [Space Control and Inertia Technology Research Center, Harbin Institute of Technology, Harbin 150001 (China)], E-mail: liqunshen@gmail.com; Wang Mao [Space Control and Inertia Technology Research Center, Harbin Institute of Technology, Harbin 150001 (China)
2007-08-27
This Letter presents an adaptive neural network control method for the chaos control problem. Based on a single layer neural network, the dynamic about the unstable fixed period point of the chaotic system can be adaptively identified without detailed information about the chaotic system. And the controlled chaotic system can be stabilized on the unstable fixed period orbit. Simulation results of Henon map and Lorenz system verify the effectiveness of the proposed control method.
Projective synchronization of fractional-order memristor-based neural networks.
Bao, Hai-Bo; Cao, Jin-De
2015-03-01
This paper investigates the projective synchronization of fractional-order memristor-based neural networks. Sufficient conditions are derived in the sense of Caputo's fractional derivation and by combining a fractional-order differential inequality. Two numerical examples are given to show the effectiveness of the main results. The results in this paper extend and improve some previous works on the synchronization of fractional-order neural networks.
Directory of Open Access Journals (Sweden)
2009-03-01
Full Text Available An electrocardiogram (ECG beat classification scheme based on multiple signal classification (MUSIC algorithm, morphological descriptors, and neural networks is proposed for discriminating nine ECG beat types. These are normal, fusion of ventricular and normal, fusion of paced and normal, left bundle branch block, right bundle branch block, premature ventricular concentration, atrial premature contraction, paced beat, and ventricular flutter. ECG signal samples from MIT-BIH arrhythmia database are used to evaluate the scheme. MUSIC algorithm is used to calculate pseudospectrum of ECG signals. The low-frequency samples are picked to have the most valuable heartbeat information. These samples along with two morphological descriptors, which deliver the characteristics and features of all parts of the heart, form an input feature vector. This vector is used for the initial training of a classifier neural network. The neural network is designed to have nine sample outputs which constitute the nine beat types. Two neural network schemes, namely multilayered perceptron (MLP neural network and a probabilistic neural network (PNN, are employed. The experimental results achieved a promising accuracy of 99.03% for classifying the beat types using MLP neural network. In addition, our scheme recognizes NORMAL class with 100% accuracy and never misclassifies any other classes as NORMAL.
Naghsh-Nilchi, Ahmad R.; Kadkhodamohammadi, A. Rahim
2009-12-01
An electrocardiogram (ECG) beat classification scheme based on multiple signal classification (MUSIC) algorithm, morphological descriptors, and neural networks is proposed for discriminating nine ECG beat types. These are normal, fusion of ventricular and normal, fusion of paced and normal, left bundle branch block, right bundle branch block, premature ventricular concentration, atrial premature contraction, paced beat, and ventricular flutter. ECG signal samples from MIT-BIH arrhythmia database are used to evaluate the scheme. MUSIC algorithm is used to calculate pseudospectrum of ECG signals. The low-frequency samples are picked to have the most valuable heartbeat information. These samples along with two morphological descriptors, which deliver the characteristics and features of all parts of the heart, form an input feature vector. This vector is used for the initial training of a classifier neural network. The neural network is designed to have nine sample outputs which constitute the nine beat types. Two neural network schemes, namely multilayered perceptron (MLP) neural network and a probabilistic neural network (PNN), are employed. The experimental results achieved a promising accuracy of 99.03% for classifying the beat types using MLP neural network. In addition, our scheme recognizes NORMAL class with 100% accuracy and never misclassifies any other classes as NORMAL.
NOISE IDENTIFICATION FOR HYDRAULIC AXIAL PISTON PUMP BASED ON ARTIFICIAL NEURAL NETWORKS
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
The noise identification model of the neural networks is established for the 63SCY14-1B hydraulic axial piston pump. Taking four kinds of different port plates as instances, the noise identification is successfully carried out for hydraulic axial piston pump based on experiments with the MATLAB and the toolbox of neural networks. The operating pressure, the flow rate of hydraulic axial piston pump, the temperature of hydraulic oil, and bulk modulus of hydraulic oil are the main parameters having influences on the noise of hydraulic axial piston pump. These four parameters are used as inputs of neural networks, and experimental data of the noise are used as outputs of neural networks. Error of noise identification is less than 1% after the neural networks have been trained. The results show that the noise identification of hydraulic axial piston pump is feasible and reliable by using artificial neural networks. The method of noise identification with neural networks is also creative one of noise theoretical research for hydraulic axial piston pump.
A case for spiking neural network simulation based on configurable multiple-FPGA systems.
Yang, Shufan; Wu, Qiang; Li, Renfa
2011-09-01
Recent neuropsychological research has begun to reveal that neurons encode information in the timing of spikes. Spiking neural network simulations are a flexible and powerful method for investigating the behaviour of neuronal systems. Simulation of the spiking neural networks in software is unable to rapidly generate output spikes in large-scale of neural network. An alternative approach, hardware implementation of such system, provides the possibility to generate independent spikes precisely and simultaneously output spike waves in real time, under the premise that spiking neural network can take full advantage of hardware inherent parallelism. We introduce a configurable FPGA-oriented hardware platform for spiking neural network simulation in this work. We aim to use this platform to combine the speed of dedicated hardware with the programmability of software so that it might allow neuroscientists to put together sophisticated computation experiments of their own model. A feed-forward hierarchy network is developed as a case study to describe the operation of biological neural systems (such as orientation selectivity of visual cortex) and computational models of such systems. This model demonstrates how a feed-forward neural network constructs the circuitry required for orientation selectivity and provides platform for reaching a deeper understanding of the primate visual system. In the future, larger scale models based on this framework can be used to replicate the actual architecture in visual cortex, leading to more detailed predictions and insights into visual perception phenomenon.
Improving the Precision and Speed of Euler Angles Computation from Low-Cost Rotation Sensor Data
Directory of Open Access Journals (Sweden)
Aleš Janota
2015-03-01
Full Text Available This article compares three different algorithms used to compute Euler angles from data obtained by the angular rate sensor (e.g., MEMS gyroscope—the algorithms based on a rotational matrix, on transforming angular velocity to time derivations of the Euler angles and on unit quaternion expressing rotation. Algorithms are compared by their computational efficiency and accuracy of Euler angles estimation. If attitude of the object is computed only from data obtained by the gyroscope, the quaternion-based algorithm seems to be most suitable (having similar accuracy as the matrix-based algorithm, but taking approx. 30% less clock cycles on the 8-bit microcomputer. Integration of the Euler angles’ time derivations has a singularity, therefore is not accurate at full range of object’s attitude. Since the error in every real gyroscope system tends to increase with time due to its offset and thermal drift, we also propose some measures based on compensation by additional sensors (a magnetic compass and accelerometer. Vector data of mentioned secondary sensors has to be transformed into the inertial frame of reference. While transformation of the vector by the matrix is slightly faster than doing the same by quaternion, the compensated sensor system utilizing a matrix-based algorithm can be approximately 10% faster than the system utilizing quaternions (depending on implementation and hardware.
Improving the precision and speed of Euler angles computation from low-cost rotation sensor data.
Janota, Aleš; Šimák, Vojtech; Nemec, Dušan; Hrbček, Jozef
2015-03-23
This article compares three different algorithms used to compute Euler angles from data obtained by the angular rate sensor (e.g., MEMS gyroscope)-the algorithms based on a rotational matrix, on transforming angular velocity to time derivations of the Euler angles and on unit quaternion expressing rotation. Algorithms are compared by their computational efficiency and accuracy of Euler angles estimation. If attitude of the object is computed only from data obtained by the gyroscope, the quaternion-based algorithm seems to be most suitable (having similar accuracy as the matrix-based algorithm, but taking approx. 30% less clock cycles on the 8-bit microcomputer). Integration of the Euler angles' time derivations has a singularity, therefore is not accurate at full range of object's attitude. Since the error in every real gyroscope system tends to increase with time due to its offset and thermal drift, we also propose some measures based on compensation by additional sensors (a magnetic compass and accelerometer). Vector data of mentioned secondary sensors has to be transformed into the inertial frame of reference. While transformation of the vector by the matrix is slightly faster than doing the same by quaternion, the compensated sensor system utilizing a matrix-based algorithm can be approximately 10% faster than the system utilizing quaternions (depending on implementation and hardware).
Euler European Libraries and Electronic Resources in Mathematical Sciences
The Euler Project. Karlsruhe
The European Libraries and Electronic Resources (EULER) Project in Mathematical Sciences provides the EulerService site for searching out "mathematical resources such as books, pre-prints, web-pages, abstracts, proceedings, serials, technical reports preprints) and NetLab (for Internet resources), this outstanding engine is capable of simple, full, and refined searches. It also offers a browse option, which responds to entries in the author, keyword, and title fields. Further information about the Project is provided at the EULER homepage.
Self-Constrained Euler Deconvolution Using Potential Field Data of Different Altitudes
Zhou, Wenna; Nan, Zeyu; Li, Jiyan
2016-06-01
Euler deconvolution has been developed as almost the most common tool in potential field data semi-automatic interpretation. The structural index (SI) is a main determining factor of the quality of depth estimation. In this paper, we first present an improved Euler deconvolution method to eliminate the influence of SI using potential field data of different altitudes. The different altitudes data can be obtained by the upward continuation or can be directly obtained by the airborne measurement realization. Euler deconvolution at different altitudes of a certain range has very similar calculation equation. Therefore, the ratio of Euler equations of two different altitudes can be calculated to discard the SI. Thus, the depth and location of geologic source can be directly calculated using the improved Euler deconvolution without any prior information. Particularly, the noise influence can be decreased using the upward continuation of different altitudes. The new method is called self-constrained Euler deconvolution (SED). Subsequently, based on the SED algorithm, we deduce the full tensor gradient (FTG) calculation form of the new improved method. As we all know, using multi-components data of FTG have added advantages in data interpretation. The FTG form is composed by x-, y- and z-directional components. Due to the using more components, the FTG form can get more accurate results and more information in detail. The proposed modification method is tested using different synthetic models, and the satisfactory results are obtained. Finally, we applied the new approach to Bishop model magnetic data and real gravity data. All the results demonstrate that the new approach is utility tool to interpret the potential field and full tensor gradient data.
Developing neural stem cell-based treatments for neurodegenerative diseases.
Byrne, James A
2014-05-30
Owing to the aging of the population, our society now faces an impending wave of age-related neurodegenerative pathologies, the most significant of which is Alzheimer's disease. Currently, no effective therapies for Alzheimer's disease have been developed. However, recent advances in the fields of neural stem cells and human induced pluripotent stem cells now provide us with the first real hope for a cure. The recent discovery by Blurton-Jones and colleagues that neural stem cells can effectively deliver disease-modifying therapeutic proteins throughout the brains of our best rodent models of Alzheimer's disease, combined with recent advances in human nuclear reprogramming, stem cell research, and highly customized genetic engineering, may represent a potentially revolutionary personalized cellular therapeutic approach capable of effectively curing, ameliorating, and/or slowing the progression of Alzheimer's disease.
Vocal learning in elephants: neural bases and adaptive context.
Stoeger, Angela S; Manger, Paul
2014-10-01
In the last decade clear evidence has accumulated that elephants are capable of vocal production learning. Examples of vocal imitation are documented in African (Loxodonta africana) and Asian (Elephas maximus) elephants, but little is known about the function of vocal learning within the natural communication systems of either species. We are also just starting to identify the neural basis of elephant vocalizations. The African elephant diencephalon and brainstem possess specializations related to aspects of neural information processing in the motor system (affecting the timing and learning of trunk movements) and the auditory and vocalization system. Comparative interdisciplinary (from behavioral to neuroanatomical) studies are strongly warranted to increase our understanding of both vocal learning and vocal behavior in elephants.
Risk Assessment Algorithms Based On Recursive Neural Networks
De Lara, Alejandro Chinea Manrique
2007-01-01
The assessment of highly-risky situations at road intersections have been recently revealed as an important research topic within the context of the automotive industry. In this paper we shall introduce a novel approach to compute risk functions by using a combination of a highly non-linear processing model in conjunction with a powerful information encoding procedure. Specifically, the elements of information either static or dynamic that appear in a road intersection scene are encoded by using directed positional acyclic labeled graphs. The risk assessment problem is then reformulated in terms of an inductive learning task carried out by a recursive neural network. Recursive neural networks are connectionist models capable of solving supervised and non-supervised learning problems represented by directed ordered acyclic graphs. The potential of this novel approach is demonstrated through well predefined scenarios. The major difference of our approach compared to others is expressed by the fact of learning t...
Glaucoma detection based on deep convolutional neural network.
Xiangyu Chen; Yanwu Xu; Damon Wing Kee Wong; Tien Yin Wong; Jiang Liu
2015-08-01
Glaucoma is a chronic and irreversible eye disease, which leads to deterioration in vision and quality of life. In this paper, we develop a deep learning (DL) architecture with convolutional neural network for automated glaucoma diagnosis. Deep learning systems, such as convolutional neural networks (CNNs), can infer a hierarchical representation of images to discriminate between glaucoma and non-glaucoma patterns for diagnostic decisions. The proposed DL architecture contains six learned layers: four convolutional layers and two fully-connected layers. Dropout and data augmentation strategies are adopted to further boost the performance of glaucoma diagnosis. Extensive experiments are performed on the ORIGA and SCES datasets. The results show area under curve (AUC) of the receiver operating characteristic curve in glaucoma detection at 0.831 and 0.887 in the two databases, much better than state-of-the-art algorithms. The method could be used for glaucoma detection.
Memristor-based neural networks: Synaptic versus neuronal stochasticity
Naous, Rawan
2016-11-02
In neuromorphic circuits, stochasticity in the cortex can be mapped into the synaptic or neuronal components. The hardware emulation of these stochastic neural networks are currently being extensively studied using resistive memories or memristors. The ionic process involved in the underlying switching behavior of the memristive elements is considered as the main source of stochasticity of its operation. Building on its inherent variability, the memristor is incorporated into abstract models of stochastic neurons and synapses. Two approaches of stochastic neural networks are investigated. Aside from the size and area perspective, the impact on the system performance, in terms of accuracy, recognition rates, and learning, among these two approaches and where the memristor would fall into place are the main comparison points to be considered.
Neural network based PWM AC chopper fed induction motor drive
Directory of Open Access Journals (Sweden)
Venkatesan Jamuna
2009-01-01
Full Text Available In this paper, a new Simulink model for a neural network controlled PWM AC chopper fed single phase induction motor is proposed. Closed loop speed control is achieved using a neural network controller. To maintain a constant fluid flow with a variation in pressure head, drives like fan and pump are operated with closed loop speed control. The need to improve the quality and reliability of the drive circuit has increased because of the growing demand for improving the performance of motor drives. With the increased availability of MOSFET's and IGBT's, PWM converters can be used efficiently in low and medium power applications. From the simulation studies, it is seen that the PWM AC chopper has a better harmonic spectrum and lesser copper loss than the Phase controlled AC chopper. It is observed that the drive system with the proposed model produces better dynamic performance, reduced overshoot and fast transient response. .
A NOVEL INTRUSION DETECTION MODE BASED ON UNDERSTANDABLE NEURAL NETWORK TREES
Institute of Scientific and Technical Information of China (English)
Xu Qinzhen; Yang Luxi; Zhao Qiangfu; He Zhenya
2006-01-01
Several data mining techniques such as Hidden Markov Model (HMM), artificial neural network,statistical techniques and expert systems are used to model network packets in the field of intrusion detection.In this paper a novel intrusion detection mode based on understandable Neural Network Tree (NNTree) is presented. NNTree is a modular neural network with the overall structure being a Decision Tree (DT), and each non-terminal node being an Expert Neural Network (ENN). One crucial advantage of using NNTrees is that they keep the non-symbolic model ENN's capability of learning in changing environments. Another potential advantage of using NNTrees is that they are actually "gray boxes" as they can be interpreted easily ifthe number of inputs for each ENN is limited. We showed through experiments that the trained NNTree achieved a simple ENN at each non-terminal node as well as a satisfying recognition rate of the network packets dataset.We also compared the performance with that of a three-layer backpropagation neural network. Experimental results indicated that the NNTree based intrusion detection model achieved better performance than the neural network based intrusion detection model.
AN INTELLIGENT CONTROL SYSTEM BASED ON RECURRENT NEURAL FUZZY NETWORK AND ITS APPLICATION TO CSTR
Institute of Scientific and Technical Information of China (English)
JIA Li; YU Jinshou
2005-01-01
In this paper, an intelligent control system based on recurrent neural fuzzy network is presented for complex, uncertain and nonlinear processes, in which a recurrent neural fuzzy network is used as controller (RNFNC) to control a process adaptively and a recurrent neural network based on recursive predictive error algorithm (RNNM) is utilized to estimate the gradient information (ey)/(e)u for optimizing the parameters of controller.Compared with many neural fuzzy control systems, it uses recurrent neural network to realize the fuzzy controller. Moreover, recursive predictive error algorithm (RPE) is implemented to construct RNNM on line. Lastly, in order to evaluate the performance of theproposed control system, the presented control system is applied to continuously stirred tank reactor (CSTR). Simulation comparisons, based on control effect and output error,with general fuzzy controller and feed-forward neural fuzzy network controller (FNFNC),are conducted. In addition, the rates of convergence of RNNM respectively using RPE algorithm and gradient learning algorithm are also compared. The results show that the proposed control system is better for controlling uncertain and nonlinear processes.
Neuronal spike sorting based on radial basis function neural networks
Directory of Open Access Journals (Sweden)
Taghavi Kani M
2011-02-01
Full Text Available "nBackground: Studying the behavior of a society of neurons, extracting the communication mechanisms of brain with other tissues, finding treatment for some nervous system diseases and designing neuroprosthetic devices, require an algorithm to sort neuralspikes automatically. However, sorting neural spikes is a challenging task because of the low signal to noise ratio (SNR of the spikes. The main purpose of this study was to design an automatic algorithm for classifying neuronal spikes that are emitted from a specific region of the nervous system."n "nMethods: The spike sorting process usually consists of three stages: detection, feature extraction and sorting. We initially used signal statistics to detect neural spikes. Then, we chose a limited number of typical spikes as features and finally used them to train a radial basis function (RBF neural network to sort the spikes. In most spike sorting devices, these signals are not linearly discriminative. In order to solve this problem, the aforesaid RBF neural network was used."n "nResults: After the learning process, our proposed algorithm classified any arbitrary spike. The obtained results showed that even though the proposed Radial Basis Spike Sorter (RBSS reached to the same error as the previous methods, however, the computational costs were much lower compared to other algorithms. Moreover, the competitive points of the proposed algorithm were its good speed and low computational complexity."n "nConclusion: Regarding the results of this study, the proposed algorithm seems to serve the purpose of procedures that require real-time processing and spike sorting.
Activated sludge process based on artificial neural network
Institute of Scientific and Technical Information of China (English)
张文艺; 蔡建安
2002-01-01
Considering the difficulty of creating water quality model for activated sludge system, a typical BP artificial neural network model has been established to simulate the operation of a waste water treatment facilities. The comparison of prediction results with the on-spot measurements shows the model, the model is accurate and this model can also be used to realize intelligentized on-line control of the wastewater processing process.
License Plate Recognition Based on Transform Coding and Neural Network
Institute of Scientific and Technical Information of China (English)
李小平; 胡海生; 宋瀚涛; 朱建学; 丁俨
2003-01-01
A method of vehicle license plate recognition utilizing Karhunen-Loeve(K-L) transform is provided. The transform is used to extract features from a mass of image templates, to describe high-dimensional images with low-dimensional ones, and moreover, to implement data compression and play down complexity of the neural network. With the character to reduce eigenspace dimensionality of K-L transform and the ability to map data of BP network, the method does effectively in recognizing license plates.
Spaceflight Effects on Neurocognitive Performance: Extent, Longevity and Neural Bases
Seidler, R. D.; Mulavara, A. P.; Koppelmans, V.; Kofman, I. S.; Cassady, K.; Yuan, P.; De Dios, Y. E.; Gadd, N.; Riascos, R. F.; Wood, S. J.; Bloomberg, J. J.
2017-01-01
We are conducting ongoing experiments in which we are performing structural and functional magnetic resonance brain imaging to identify the relationships between changes in neurocognitive function and neural structural alterations following a six month International Space Station mission. Our central hypothesis is that measures of brain structure, function, and network integrity will change from pre to post spaceflight. Moreover, we predict that these changes will correlate with indices of cognitive, sensory, and motor function in a neuroanatomically selective fashion. Our interdisciplinary approach utilizes cutting edge neuroimaging techniques and a broad ranging battery of sensory, motor, and cognitive assessments that are conducted pre flight, during flight, and post flight to investigate potential neuroplastic and maladaptive brain changes in crewmembers following long-duration spaceflight. Success in this endeavor would 1) result in identification of the underlying neural mechanisms and operational risks of spaceflight-induced changes in behavior, and 2) identify whether a return to normative behavioral function following re-adaptation to Earth's gravitational environment is associated with a restitution of brain structure and function or instead is supported by substitution with compensatory brain processes. We have collected data on several crewmembers and preliminary findings will be presented. Eventual comparison to results from our parallel bed rest study will enable us to parse out the multiple mechanisms contributing to any spaceflight-induced neural structural and behavioral changes that we observe.
Expert music performance: cognitive, neural, and developmental bases.
Brown, Rachel M; Zatorre, Robert J; Penhune, Virginia B
2015-01-01
In this chapter, we explore what happens in the brain of an expert musician during performance. Understanding expert music performance is interesting to cognitive neuroscientists not only because it tests the limits of human memory and movement, but also because studying expert musicianship can help us understand skilled human behavior in general. In this chapter, we outline important facets of our current understanding of the cognitive and neural basis for music performance, and developmental factors that may underlie musical ability. We address three main questions. (1) What is expert performance? (2) How do musicians achieve expert-level performance? (3) How does expert performance come about? We address the first question by describing musicians' ability to remember, plan, execute, and monitor their performances in order to perform music accurately and expressively. We address the second question by reviewing evidence for possible cognitive and neural mechanisms that may underlie or contribute to expert music performance, including the integration of sound and movement, feedforward and feedback motor control processes, expectancy, and imagery. We further discuss how neural circuits in auditory, motor, parietal, subcortical, and frontal cortex all contribute to different facets of musical expertise. Finally, we address the third question by reviewing evidence for the heritability of musical expertise and for how expertise develops through training and practice. We end by discussing outlooks for future work.
Neural Network Based Lna Design for Mobile Satellite Receiver
Directory of Open Access Journals (Sweden)
Abhijeet Upadhya
2014-08-01
Full Text Available Paper presents a Neural Network Modelling approach to microwave LNA design. To acknowledge the specifications of the amplifier, Mobile Satellite Systems are analyzed. Scattering parameters of the LNA in the frequency range 0.5 to 18 GHz are calculated using a Multilayer Perceptron Artificial Neural Network model and corresponding smith charts and polar charts are plotted as output to the model. From these plots, the microwave scattering parameter description of the LNA are obtained. Model is efficiently trained using Agilent ATF 331M4 InGaAs/InP Low Noise pHEMT amplifier datasheet and the neural model’s output seem to follow the various device characteristic curves with high regression. Next, Maximum Allowable Gain and Noise figure of the device are modelled and plotted for the same frequency range. Finally, the optimized model is utilized as an interpolator and the resolution of the amplifying capability with noise characteristics are obtained for the L Band of MSS operation.
A meshless front tracking method for the Euler equations of fluid dynamics
Witteveen, J.A.S.
2009-01-01
A second order front tracking method is developed for solving the Euler equations of inviscid fluid dynamics numerically. Front tracking methods are usually limited to first order accuracy, since they are based on a piecewise constant approximation of the solution. Here the second order convergence
Euler-Lagrange Equations for the Gribov Reggeon Calculus in QCD and in Gravity
Lipatov, L. N.
The theory of the high energy scattering in QCD and gravity is based on the reggeization of gluons and gravitons, respectively. We discuss the corresponding effective actions for reggeized particle interactions. The Euler-Lagrange equations in these theories are constructed with a variational approach for the effective actions and by using their invariance under the gauge and general coordinate transformations.
Entropy Analysis of Kinetic Flux Vector Splitting Schemes for the Compressible Euler Equations
Shiuhong, Lui; Xu, Jun
1999-01-01
Flux Vector Splitting (FVS) scheme is one group of approximate Riemann solvers for the compressible Euler equations. In this paper, the discretized entropy condition of the Kinetic Flux Vector Splitting (KFVS) scheme based on the gas-kinetic theory is proved. The proof of the entropy condition involves the entropy definition difference between the distinguishable and indistinguishable particles.
A Note on Residue Formulas for the Euler Class of Sphere Fibrations
Institute of Scientific and Technical Information of China (English)
Francisco Gómez RUIZ
2011-01-01
This paper presents a definition of residue formulas for the Euler class of cohomology-oriented sphere fibrations §. If the base of § is a topological manifold, a Hopf index theorem can be obtained and, for the smooth category, a generalization of a residue formula is derived for real vector bundles given in [2].
Directory of Open Access Journals (Sweden)
Neera Saxena
2011-07-01
Full Text Available This paper presents an ensemble of neo-cognitron neural network base classifiers to enhance the accuracy of the system, along the experimental results. The method offers lesser computational preprocessing in comparison to other ensemble techniques as it ex-preempts feature extraction process before feeding the data into base classifiers. This is achieved by the basic nature of neo-cognitron, it is a multilayer feed-forward neural network. Ensemble of such base classifiers gives class labels for each pattern that in turn is combined to give the final class label for that pattern. The purpose of this paper is not only to exemplify learning behaviour of neo-cognitron as base classifiers, but also to purport better fashion to combine neural network based ensemble classifiers.
Knowledge base and neural network approach for protein secondary structure prediction.
Patel, Maulika S; Mazumdar, Himanshu S
2014-11-21
Protein structure prediction is of great relevance given the abundant genomic and proteomic data generated by the genome sequencing projects. Protein secondary structure prediction is addressed as a sub task in determining the protein tertiary structure and function. In this paper, a novel algorithm, KB-PROSSP-NN, which is a combination of knowledge base and modeling of the exceptions in the knowledge base using neural networks for protein secondary structure prediction (PSSP), is proposed. The knowledge base is derived from a proteomic sequence-structure database and consists of the statistics of association between the 5-residue words and corresponding secondary structure. The predicted results obtained using knowledge base are refined with a Backpropogation neural network algorithm. Neural net models the exceptions of the knowledge base. The Q3 accuracy of 90% and 82% is achieved on the RS126 and CB396 test sets respectively which suggest improvement over existing state of art methods.
Euler and the Ordinary Differential Equations
Taborda, Jonathan
2010-01-01
The following notes are intended to make a small digression on the topics mentioned in the title of the same, since these were not addressed in the past tribute by the Institute of Physics of the UdeA. We believe more than platitude try to justify the importance and effectiveness in the development of mathematics and physics during the eighteenth century and present such issues, therefore a brief description of the methods and problems attacked by Euler and his contemporaries using the heuristics. Note in advance that they constitute a strong impoverished attempt to honor the memory of who is considered the Shakespeare of Mathematics: Universal, rich in detail and inexhaustible.
Multiblock, Multigrid Solution Of Euler Equations
Melson, N. Duane; Cannizzaro, Frank E.; Von Lavante, E.
1994-01-01
Method of numerical solution of Euler equations of three-dimensional flow of compressible fluid involves combination of multiblock and multigrid strategies. In multiblock strategy, flow field divided, into multiple smaller, more computationally-convenient zones and computational grid fitted to applicable flow boundaries generated in each block. In multigrid strategy used here, different quantities computed, variously, on finer or coarser grids. Minimizing cost of computation by using fewest grid points yielding acceptably accurate values of affected variable. Multigrid strategy found effective in accelerating convergence to steady state, while multiblock strategy provides geometric flexibility.
Stochastic Euler-Poincaré reduction
Energy Technology Data Exchange (ETDEWEB)
Arnaudon, Marc, E-mail: marc.arnaudon@math.u-bordeaux1.fr [Institut de Mathématiques de Bordeaux (UMR 5251) Université Bordeaux 1 351, Cours de la Libération F33405 TALENCE Cedex (France); Chen, Xin, E-mail: chenxin-217@hotmail.com [Grupo de Física-Matemática Univ. Lisboa, Av.Prof. Gama Pinto 2 1649-003 Lisboa (Portugal); Cruzeiro, Ana Bela, E-mail: abcruz@math.ist.utl.pt [GFMUL and Dep. de Matemática Instituto Superior Técnico (UL), Av. Rovisco Pais 1049-001 Lisboa (Portugal)
2014-08-15
We prove a Euler-Poincaré reduction theorem for stochastic processes taking values on a Lie group, which is a generalization of the reduction argument for the deterministic case [J. E. Marsden and T. S. Ratiu, Introduction to Mechanics and Symmetry: A Basic Exposition of Classical Mechanical Systems, Texts in Applied Mathematics (Springer, 2003)]. We also show examples of its application to SO(3) and to the group of diffeomorphisms, which includes the Navier-Stokes equation on a bounded domain and the Camassa-Holm equation.
Institute of Scientific and Technical Information of China (English)
Qi Zhidong; Zhu Xinjian; Cao Guangyi
2006-01-01
Aiming at on-line controlling of Direct Methanol Fuel Cell (DMFC) stack, an adaptive neural fuzzy inference technology is adopted in the modeling and control of DMFC temperature system. In the modeling process, an Adaptive Neural Fuzzy Inference System (ANFIS) identification model of DMFC stack temperature is developed based on the input-output sampled data, which can avoid the internal complexity of DMFC stack. In the controlling process, with the network model trained well as the reference model of the DMFC control system, a novel fuzzy genetic algorithm is used to regulate the parameters and fuzzy rules of a neural fuzzy controller. In the simulation, compared with the nonlinear Proportional Integral Derivative (PID) and traditional fuzzy algorithm, the improved neural fuzzy controller designed in this paper gets better performance, as demonstrated by the simulation results.
Neural-net based real-time economic dispatch for thermal power plants
Energy Technology Data Exchange (ETDEWEB)
Djukanovic, M.; Milosevic, B. [Inst. Nikola Tesla, Belgrade (Yugoslavia). Dept. of Power Systems; Calovic, M. [Univ. of Belgrade (Yugoslavia). Dept. of Electrical Engineering; Sobajic, D.J. [Electric Power Research Inst., Palo Alto, CA (United States)
1996-12-01
This paper proposes the application of artificial neural networks to real-time optimal generation dispatch of thermal units. The approach can take into account the operational requirements and network losses. The proposed economic dispatch uses an artificial neural network (ANN) for generation of penalty factors, depending on the input generator powers and identified system load change. Then, a few additional iterations are performed within an iterative computation procedure for the solution of coordination equations, by using reference-bus penalty-factors derived from the Newton-Raphson load flow. A coordination technique for environmental and economic dispatch of pure thermal systems, based on the neural-net theory for simplified solution algorithms and improved man-machine interface is introduced. Numerical results on two test examples show that the proposed algorithm can efficiently and accurately develop optimal and feasible generator output trajectories, by applying neural-net forecasts of system load patterns.
A Predictive Neural Network-Based Cascade Control for pH Reactors
Directory of Open Access Journals (Sweden)
Mujahed AlDhaifallah
2016-01-01
Full Text Available This paper is concerned with the development of predictive neural network-based cascade control for pH reactors. The cascade structure consists of a master control loop (fuzzy proportional-integral and a slave one (predictive neural network. The master loop is chosen to be more accurate but slower than the slave one. The strong features found in cascade structure have been added to the inherent features in model predictive neural network. The neural network is used to alleviate modeling difficulties found with pH reactor and to predict its behavior. The parameters of predictive algorithm are determined using an optimization algorithm. The effectiveness and feasibility of the proposed design have been demonstrated using MatLab.
MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning.
Liu, Yang; Yang, Jie; Huang, Yuan; Xu, Lixiong; Li, Siguang; Qi, Man
2015-01-01
Artificial neural networks (ANNs) have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation.
Peng, Zhouhua; Wang, Dan; Wang, Wei; Liu, Lu
2015-11-01
This paper investigates the containment control problem of networked autonomous underwater vehicles in the presence of model uncertainty and unknown ocean disturbances. A predictor-based neural dynamic surface control design method is presented to develop the distributed adaptive containment controllers, under which the trajectories of follower vehicles nearly converge to the dynamic convex hull spanned by multiple reference trajectories over a directed network. Prediction errors, rather than tracking errors, are used to update the neural adaptation laws, which are independent of the tracking error dynamics, resulting in two time-scales to govern the entire system. The stability property of the closed-loop network is established via Lyapunov analysis, and transient property is quantified in terms of L2 norms of the derivatives of neural weights, which are shown to be smaller than the classical neural dynamic surface control approach. Comparative studies are given to show the substantial improvements of the proposed new method.
Short-Term Wind Speed Forecast Based on B-Spline Neural Network Optimized by PSO
Directory of Open Access Journals (Sweden)
Zhongqiang Wu
2015-01-01
Full Text Available Considering the randomness and volatility of wind, a method based on B-spline neural network optimized by particle swarm optimization is proposed to predict the short-term wind speed. The B-spline neural network can change the division of input space and the definition of basis function flexibly. For any input, only a few outputs of hidden layers are nonzero, the outputs are simple, and the convergence speed is fast, but it is easy to fall into local minimum. The traditional method to divide the input space is thoughtless and it will influence the final prediction accuracy. Particle swarm optimization is adopted to solve the problem by optimizing the nodes. Simulated results show that it has higher prediction accuracy than traditional B-spline neural network and BP neural network.
Prediction Model of Weekly Retail Price for Eggs Based on Chaotic Neural Network
Institute of Scientific and Technical Information of China (English)
LI Zhe-min; CUI Li-guo; XU Shi-wei; WENG Ling-yun; DONG Xiao-xia; LI Gan-qiong; YU Hai-peng
2013-01-01
This paper establishes a short-term prediction model of weekly retail prices for eggs based on chaotic neural network with the weekly retail prices of eggs from January 2008 to December 2012 in China. In the process of determining the structure of the chaotic neural network, the number of input layer nodes of the network is calculated by reconstructing phase space and computing its saturated embedding dimension, and then the number of hidden layer nodes is estimated by trial and error. Finally, this model is applied to predict the retail prices of eggs and compared with ARIMA. The result shows that the chaotic neural network has better nonlinear iftting ability and higher precision in the prediction of weekly retail price of eggs. The empirical result also shows that the chaotic neural network can be widely used in the ifeld of short-term prediction of agricultural prices.
A New Modeling Method Based on Genetic Neural Network for Numeral Eddy Current Sensor
Institute of Scientific and Technical Information of China (English)
Along Yu; Zheng Li
2006-01-01
In this paper, we present a method used to the numeral eddy current sensor modeling based on genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced. In this method,the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network (GNN) according to measurement data. So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network. The nonlinear model has the advantages of strong robustness, on-line scaling and high precision. The maximum nonlinearity error can be reduced to 0.037% using GNN. However, the maximum nonlinearity error is 0.075% using least square method (LMS).
A study of interceptor attitude control based on adaptive wavelet neural networks
Li, Da; Wang, Qing-chao
2005-12-01
This paper engages to study the 3-DOF attitude control problem of the kinetic interceptor. When the kinetic interceptor enters into terminal guidance it has to maneuver with large angles. The characteristic of interceptor attitude system is nonlinearity, strong-coupling and MIMO. A kind of inverse control approach based on adaptive wavelet neural networks was proposed in this paper. Instead of using one complex neural network as the controller, the nonlinear dynamics of the interceptor can be approximated by three independent subsystems applying exact feedback-linearization firstly, and then controllers for each subsystem are designed using adaptive wavelet neural networks respectively. This method avoids computing a large amount of the weights and bias in one massive neural network and the control parameters can be adaptive changed online. Simulation results betray that the proposed controller performs remarkably well.
Dissolved oxygen prediction using a possibility theory based fuzzy neural network
Khan, Usman T.; Valeo, Caterina
2016-06-01
A new fuzzy neural network method to predict minimum dissolved oxygen (DO) concentration in a highly urbanised riverine environment (in Calgary, Canada) is proposed. The method uses abiotic factors (non-living, physical and chemical attributes) as inputs to the model, since the physical mechanisms governing DO in the river are largely unknown. A new two-step method to construct fuzzy numbers using observations is proposed. Then an existing fuzzy neural network is modified to account for fuzzy number inputs and also uses possibility theory based intervals to train the network. Results demonstrate that the method is particularly well suited to predicting low DO events in the Bow River. Model performance is compared with a fuzzy neural network with crisp inputs, as well as with a traditional neural network. Model output and a defuzzification technique are used to estimate the risk of low DO so that water resource managers can implement strategies to prevent the occurrence of low DO.
Enhanced Password Based Security System Based on User Behavior using Neural Networks
Directory of Open Access Journals (Sweden)
Gour Sundar Mitra Thakur
2012-04-01
Full Text Available There are multiple numbers of security systems are available to protect your computer/resources. Among them, password based systems are the most commonly used system due to its simplicity, applicability and cost effectiveness But these types of systems have higher sensitivity to cyber-attack. Most of the advanced methods for authentication based on password security encrypt the contents of password before storing or transmitting in the physical domain. But all conventional encryption methods are having its own limitations, generally either in terms of complexity or in terms of efficiency.In this paper an enhanced password based security system has been proposed based on user typing behavior, which will attempt to identify authenticity of any user failing to login in first few attempts by analyzing the basic user behaviors/activities and finally training them through neural network and classifying them as genuine or intruder
Standard Cell-Based Implementation of a Digital Optoelectronic Neural-Network Hardware
Maier, Klaus D.; Beckstein, Clemens; Blickhan, Reinhard; Erhard, Werner
2001-03-01
A standard cell-based implementation of a digital optoelectronic neural-network architecture is presented. The overall structure of the multilayer perceptron network that was used, the optoelectronic interconnection system between the layers, and all components required in each layer are defined. The design process from VHDL-based modeling from synthesis and partly automatic placing and routing to the final editing of one layer of the circuit of the multilayer perceptrons are described. A suitable approach for the standard cell-based design of optoelectronic systems is presented, and shortcomings of the design tool that was used are pointed out. The layout for the microelectronic circuit of one layer in a multilayer perceptron neural network with a performance potential 1 magnitude higher than neural networks that are purely electronic based has been successfully designed.
Experimental method to predict avalanches based on neural networks
Directory of Open Access Journals (Sweden)
V. V. Zhdanov
2016-01-01
Full Text Available The article presents results of experimental use of currently available statistical methods to classify the avalanche‑dangerous precipitations and snowfalls in the Kishi Almaty river basin. The avalanche service of Kazakhstan uses graphical methods for prediction of avalanches developed by I.V. Kondrashov and E.I. Kolesnikov. The main objective of this work was to develop a modern model that could be used directly at the avalanche stations. Classification of winter precipitations into dangerous snowfalls and non‑dangerous ones was performed by two following ways: the linear discriminant function (canonical analysis and artificial neural networks. Observational data on weather and avalanches in the gorge Kishi Almaty in the gorge Kishi Almaty were used as a training sample. Coefficients for the canonical variables were calculated by the software «Statistica» (Russian version 6.0, and then the necessary formula had been constructed. The accuracy of the above classification was 96%. Simulator by the authors L.N. Yasnitsky and F.М. Cherepanov was used to learn the neural networks. The trained neural network demonstrated 98% accuracy of the classification. Prepared statistical models are recommended to be tested at the snow‑avalanche stations. Results of the tests will be used for estimation of the model quality and its readiness for the operational work. In future, we plan to apply these models for classification of the avalanche danger by the five‑point international scale.
The neural bases underlying social risk perception in purchase decisions.
Yokoyama, Ryoichi; Nozawa, Takayuki; Sugiura, Motoaki; Yomogida, Yukihito; Takeuchi, Hikaru; Akimoto, Yoritaka; Shibuya, Satoru; Kawashima, Ryuta
2014-05-01
Social considerations significantly influence daily purchase decisions, and the perception of social risk (i.e., the anticipated disapproval of others) is crucial in dissuading consumers from making purchases. However, the neural basis for consumers' perception of social risk remains undiscovered, and this novel study clarifies the relevant neural processes. A total of 26 volunteers were scanned while they evaluated purchase intention of products (purchase intention task) and their anticipation of others' disapproval for possessing a product (social risk task), using functional magnetic resonance imaging (fMRI). The fMRI data from the purchase intention task was used to identify the brain region associated with perception of social risk during purchase decision making by using subjective social risk ratings for a parametric modulation analysis. Furthermore, we aimed to explore if there was a difference between participants' purchase decisions and their explicit evaluations of social risk, with reference to the neural activity associated with social risk perception. For this, subjective social risk ratings were used for a parametric modulation analysis on fMRI data from the social risk task. Analysis of the purchase intention task revealed a significant positive correlation between ratings of social risk and activity in the anterior insula, an area of the brain that is known as part of the emotion-related network. Analysis of the social risk task revealed a significant positive correlation between ratings of social risk and activity in the temporal parietal junction and the medial prefrontal cortex, which are known as theory-of-mind regions. Our results suggest that the anterior insula processes consumers' social risk implicitly to prompt consumers not to buy socially unacceptable products, whereas ToM-related regions process such risk explicitly in considering the anticipated disapproval of others. These findings may prove helpful in understanding the mental
Monte Carlo Euler approximations of HJM term structure financial models
Björk, Tomas
2012-11-22
We present Monte Carlo-Euler methods for a weak approximation problem related to the Heath-Jarrow-Morton (HJM) term structure model, based on Itô stochastic differential equations in infinite dimensional spaces, and prove strong and weak error convergence estimates. The weak error estimates are based on stochastic flows and discrete dual backward problems, and they can be used to identify different error contributions arising from time and maturity discretization as well as the classical statistical error due to finite sampling. Explicit formulas for efficient computation of sharp error approximation are included. Due to the structure of the HJM models considered here, the computational effort devoted to the error estimates is low compared to the work to compute Monte Carlo solutions to the HJM model. Numerical examples with known exact solution are included in order to show the behavior of the estimates. © 2012 Springer Science+Business Media Dordrecht.
Experimental evaluation of neural, statistical, and model-based approaches to FLIR ATR
Li, Baoxin; Zheng, Qinfen; Der, Sandor Z.; Chellappa, Rama; Nasrabadi, Nasser M.; Chan, Lipchen A.; Wang, LinCheng
1998-09-01
This paper presents an empirical evaluation of a number of recently developed Automatic Target Recognition algorithms for Forward-Looking InfraRed (FLIR) imagery using a large database of real second-generation FLIR images. The algorithms evaluated are based on convolution neural networks (CNN), principal component analysis (PCA), linear discriminant analysis (LDA), learning vector quantization (LVQ), and modular neural networks (MNN). Two model-based algorithms, using Hausdorff metric based matching and geometric hashing, are also evaluated. A hierarchial pose estimation system using CNN plus either PCA or LDA, developed by the authors, is also evaluated using the same data set.
Modulation of grasping force in prosthetic hands using neural network-based predictive control.
Pasluosta, Cristian F; Chiu, Alan W L
2015-01-01
This chapter describes the implementation of a neural network-based predictive control system for driving a prosthetic hand. Nonlinearities associated with the electromechanical aspects of prosthetic devices present great challenges for precise control of this type of device. Model-based controllers may overcome this issue. Moreover, given the complexity of these kinds of electromechanical systems, neural network-based modeling arises as a good fit for modeling the fingers' dynamics. The results of simulations mimicking potential situations encountered during activities of daily living demonstrate the feasibility of this technique.
A DATA MINING METHOD BASED ON CONSTRUCTIVE NEURAL NETWORKS
Institute of Scientific and Technical Information of China (English)
Wang Lunwen; Zhang Ling
2007-01-01
In this letter, Constructive Neural Networks (CNN) is used in large-scale data mining. By introducing the principle and characteristics of CNN and pointing out its deficiencies, fuzzy theory is adopted to improve the covering algorithms. The threshold of covering algorithms is redefined. "Extended area" for test samples is built. The inference of the outlier is eliminated. Furthermore, "Sphere Neighborhood (SN)" are constructed. The membership functions of test samples are given and all of the test samples are determined accordingly. The method is used to mine large wireless monitor data (about 3 × 107 data points), and knowledge is found effectively.
Hardware Prototyping of Neural Network based Fetal Electrocardiogram Extraction
Hasan, M. A.; Reaz, M. B. I.
2012-01-01
The aim of this paper is to model the algorithm for Fetal ECG (FECG) extraction from composite abdominal ECG (AECG) using VHDL (Very High Speed Integrated Circuit Hardware Description Language) for FPGA (Field Programmable Gate Array) implementation. Artificial Neural Network that provides efficient and effective ways of separating FECG signal from composite AECG signal has been designed. The proposed method gives an accuracy of 93.7% for R-peak detection in FHR monitoring. The designed VHDL model is synthesized and fitted into Altera's Stratix II EP2S15F484C3 using the Quartus II version 8.0 Web Edition for FPGA implementation.
Wavelet Neural Network Based Traffic Prediction for Next Generation Network
Institute of Scientific and Technical Information of China (English)
Zhao Qigang; Li Qunzhan; He Zhengyou
2005-01-01
By using netflow traffic collecting technology, some traffic data for analysis are collected from a next generation network (NGN) operator. To build a wavelet basis neural network (NN), the Sigmoid function is replaced with the wavelet in NN. Then the wavelet multiresolution analysis method is used to decompose the traffic signal, and the decomposed component sequences are employed to train the NN. By using the methods, an NGN traffic prediction model is built to predict one day's traffic. The experimental results show that the traffic prediction method of wavelet NN is more accurate than that without using wavelet in the NGN traffic forecasting.
Human -Computer Interface using Gestures based on Neural Network
Directory of Open Access Journals (Sweden)
Aarti Malik
2014-10-01
Full Text Available - Gestures are powerful tools for non-verbal communication. Human computer interface (HCI is a growing field which reduces the complexity of interaction between human and machine in which gestures are used for conveying information or controlling the machine. In the present paper, static hand gestures are utilized for this purpose. The paper presents a novel technique of recognizing hand gestures i.e. A-Z alphabets, 0-9 numbers and 6 additional control signals (for keyboard and mouse control by extracting various features of hand ,creating a feature vector table and training a neural network. The proposed work has a recognition rate of 99%. .
Product Assembly Cost Estimation Based on Artificial Neural Networks
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
This paper proposes a method for assembly cost estimation in actual manufacture during the design phase using artificial neural networks (ANN). It can support the de signers in cost effectiveness, then help to control the total cost. The method was used in the assembly cost estimation of the crucial parts of some railway stock products. As a compari son, we use the linear regression (LR) model in the same field. The result shows that ANN model performs better than the LR model in assembly cost estimation.
Risk assessment of logistics outsourcing based on BP neural network
Liu, Xiaofeng; Tian, Zi-you
The purpose of this article is to evaluate the risk of the enterprises logistics outsourcing. To get this goal, the paper first analysed he main risks existing in the logistics outsourcing, and then set up a risk evaluation index system of the logistics outsourcing; second applied BP neural network into the logistics outsourcing risk evaluation and used MATLAB to the simulation. It proved that the network error is small and has strong practicability. And this method can be used by enterprises to evaluate the risks of logistics outsourcing.
A dynamic multiblock approach to solving the unsteady Euler equations about complex configurations
Arabshahi, Abdollah
The objective is the development of a numerical method which can accurately and economically solve the unsteady Euler equations for three-dimensional flow fields around complex configurations, particularly a generic aircraft with a store in the captive and vertical launch position. A cell centered finite volume spatial discretization is applied to the three-dimensional, time-dependent, Euler equations written in general time-dependent curvilinear coordinates. Two algorithms are presented for solving the system of Euler equations. The first algorithm is based on flux-vector splitting while the second algorithm is based on flux-difference splitting using Roe averaged variables. For both algorithms, an implicit upwind biased approach is employed to integrate the spatially discretized equations in time. The multiblock technique utilizes the concept of decomposing the flow field between the surfaces of the configuration and some outer far field boundary into a set of blocks. Calculated results compared with experimental data indicate that the present Euler solver can calculate transonic flow fields efficiently and accurately over complex geometries. Furthermore, the results demonstrate how computational fluid dynamics (CFD) can be used to accurately simulate steady and, for the first time, unsteady fluid flow over a complete wing-pylon-store configuration with the store in the captive and vertical launch positions.
Stereo Matching Based on Immune Neural Network in Abdomen Reconstruction
Directory of Open Access Journals (Sweden)
Huan Liu
2015-01-01
Full Text Available Stereo feature matching is a technique that finds an optimal match in two images from the same entity in the three-dimensional world. The stereo correspondence problem is formulated as an optimization task where an energy function, which represents the constraints on the solution, is to be minimized. A novel intelligent biological network (Bio-Net, which involves the human B-T cells immune system into neural network, is proposed in this study in order to learn the robust relationship between the input feature points and the output matched points. A model from input-output data (left reference point-right target point is established. In the experiments, the abdomen reconstructions for different-shape mannequins are then performed by means of the proposed method. The final results are compared and analyzed, which demonstrate that the proposed approach greatly outperforms the single neural network and the conventional matching algorithm in precise. Particularly, as far as time cost and efficiency, the proposed method exhibits its significant promising and potential for improvement. Hence, it is entirely considered as an effective and feasible alternative option for stereo matching.
Neural network based daily precipitation generator (NNGEN-P)
Energy Technology Data Exchange (ETDEWEB)
Boulanger, Jean-Philippe [LODYC, UMR CNRS/IRD/UPMC, Paris (France); University of Buenos Aires, Departamento de Ciencias de la Atmosfera y los Oceanos, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina); Martinez, Fernando; Segura, Enrique C. [University of Buenos Aires, Departamento de Computacion, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina); Penalba, Olga [University of Buenos Aires, Departamento de Ciencias de la Atmosfera y los Oceanos, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina)
2007-02-15
Daily weather generators are used in many applications and risk analyses. The present paper explores the potential of neural network architectures to design daily weather generator models. Focusing this first paper on precipitation, we design a collection of neural networks (multi-layer perceptrons in the present case), which are trained so as to approximate the empirical cumulative distribution (CDF) function for the occurrence of wet and dry spells and for the precipitation amounts. This approach contributes to correct some of the biases of the usual two-step weather generator models. As compared to a rainfall occurrence Markov model, NNGEN-P represents fairly well the mean and standard deviation of the number of wet days per month, and it significantly improves the simulation of the longest dry and wet periods. Then, we compared NNGEN-P to three parametric distribution functions usually applied to fit rainfall cumulative distribution functions (Gamma, Weibull and double-exponential). A data set of 19 Argentine stations was used. Also, data corresponding to stations in the United States, in Europe and in the Tropics were included to confirm the results. One of the advantages of NNGEN-P is that it is non-parametric. Unlike other parametric function, which adapt to certain types of climate regimes, NNGEN-P is fully adaptive to the observed cumulative distribution functions, which, on some occasions, may present complex shapes. On-going works will soon produce an extended version of NNGEN to temperature and radiation. (orig.)
Using Artificial Neural Networks for Energy Regulation Based Variable-speed Electrohydraulic Drive
Institute of Scientific and Technical Information of China (English)
XU Ming; JIN Bo; YU Yaxin; SHEN Haikuo; LI Wei
2010-01-01
In the energy regulation based varibable-speed electrohydraulic drive system, the supply energy and the demanded energy, which will affect the control performance greatly, are crucial. However, they are hard to be obtained via conventional methods for some reasons. This paper tries to a new route: the definitive numerical values of the supply energy and the demanded energy are not required, except for their relationship which is called energy state. A three-layer back propagation(BP) neural network was built up to act as an energy analysis unit to deduce the energy state. The neural network has three inputs: the reference displacement, the actual displacement of cylinder rod and the system flowrate supply. The output of the neural network is energy state. A Chebyshev type II filter was designed to calculate the cylinder speed for the estimation of system flowrate supply. The training and testing samples of neural network were collected by the system accurate simulation model. After off-line training, the neural network was tested by the testing data. And the testing result demonstrates that the designed neural network was successful. Then, the neural network acts as the energy analysis unit in real-time experiments of cylinder position control, where it works efficiently under square-wave and sine-wave reference displacement. The experimental results validate its feasibility and adaptability. Only a position sensor and some pressure sensors, which are cheap and have quick dynamic response, are necessary for the system control. And the neural network plays the role of identifying the energy state.
CONTROL OF NONLINEAR PROCESS USING NEURAL NETWORK BASED MODEL PREDICTIVE CONTROL
Directory of Open Access Journals (Sweden)
Dr.A.TRIVEDI
2011-04-01
Full Text Available This paper presents a Neural Network based Model Predictive Control (NNMPC strategy to control nonlinear process. Multilayer Perceptron Neural Network (MLP is chosen to represent a Nonlinear Auto Regressive with eXogenous signal (NARX model of a nonlinear system. NARX dynamic model is based on feed-forward architecture and offers good approximation capabilities along with robustness and accuracy. Based on the identified neural model, a generalized predictive control (GPC algorithm is implemented to control the composition in acontinuous stirred tank reactor (CSTR, whose parameters are optimally determined by solving quadratic performance index using well known Levenberg-Marquardt and Quasi-Newton algorithm. NNMPC is tuned by selecting few horizon parameters and weighting factor. The tracking performance of the NNMPC is tested using different amplitude function as a reference signal on CSTR application. Also the robustness and performance is tested in the presence of disturbance on random reference signal.
RBF neural network and active circles based algorithm for contours extraction
Institute of Scientific and Technical Information of China (English)
Zhou Zhiheng; Zeng Delu; Xie Shengli
2007-01-01
For the contours extraction from the images, active contour model and self-organizing map based approach are popular nowadays. But they are still confronted with the problems that the optimization of energy function will trap in local minimums and the contour evolutions greatly depend on the initial contour selection. Addressing to these problems, a contours extraction algorithm based on RBF neural network is proposed here. A series of circles with adaptive radius and center is firstly used to search image feature points that are scattered enough. After the feature points are clustered, a group of radial basis functions are constructed. Using the pixels' intensities and gradients as the input vector, the final object contour can be obtained by the predicting ability of the neural network. The RBF neural network based algorithm is tested on three kinds of images, such as changing topology, complicated background, and blurring or noisy boundary. Simulation results show that the proposed algorithm performs contours extraction greatly.
Elevator Group-Control Policy Based on Neural Network Optimized by Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
SHEN Hong; WAN Jianru; ZHANG Zhichao; LIU Yingpei; LI Guangye
2009-01-01
Aiming at the diversity and nonlinearity of the elevator system control target, an effective group method based on a hybrid algorithm of genetic algorithm and neural network is presented in this paper. The genetic algo-rithm is used to search the weight of the neural network. At the same time, the multi-objective-based evaluation function is adopted, in which there are three main indicators including the passenger waiting time, car passengers number and the number of stops. Different weights are given to meet the actual needs. The optimal values of the evaluation function are obtained, and the optimal dispatch control of the elevator group control system based on neural network is realized. By analyzing the running of the elevator group control system, all the processes and steps are presented. The validity of the hybrid algorithm is verified by the dynamic imitation performance.
COMPARISON OF STABILITY BETWEEN NAVIER-STOKES AND EULER EQUATIONS
Institute of Scientific and Technical Information of China (English)
SHI Wei-hui; WANG Yue-peng; SHEN Chun
2006-01-01
The stability about Navier-Stokes equation and Euler equation was brought into comparison. And by taking their typical initial value problem for example, the reason of leading to the difference in stability between Navier-Stokes equation and Euler equation was also analyzed.
Applications of an Explicit Formula for the Generalized Euler Numbers
Institute of Scientific and Technical Information of China (English)
Guo Dong LIU; Wen Peng ZHANG
2008-01-01
The authors establish an explicit formula for the generalized Euler Numbers E(x)2n ,and obtain some identities and congruences involving the higher-order Euler numbers, Stirling numbers,the central factorial numbers and the values of the Riemann zeta-function.
On Euler's Theorem for Homogeneous Functions and Proofs Thereof.
Tykodi, R. J.
1982-01-01
Euler's theorem for homogenous functions is useful when developing thermodynamic distinction between extensive and intensive variables of state and when deriving the Gibbs-Duhem relation. Discusses Euler's theorem and thermodynamic applications. Includes six-step instructional strategy for introducing the material to students. (Author/JN)
Euler-Lagrange Forms and Cohomology Groups on Jet Bundles
Institute of Scientific and Technical Information of China (English)
CHEN Jing-Bo
2005-01-01
@@ Using the language of jet bundles, we generalize the definitions of Euler-Lagrange one-form and the associated cohomology which were introduced by Guo et al. [Commun. Theor. Phys. 37(2002)1]. Continuous and discreteLagrange mechanics and field theory are presented. Higher order Euler-Lagrange cohomology groups are also introduced.
Leonhard Euler and his contributions to fluid mechanics
Salas, M. D.
1988-01-01
The career of Leonhard Euler, one of the world's most gifted scientists, is reviewed. The paper focuses on Euler's contributions to fluid mechanics and gives a perspective of how this science was born. A bibliography is included to provide the history enthusiast with a starting point for further study.
Reinforcement-Based Fuzzy Neural Network ontrol with Automatic Rule Generation
Institute of Scientific and Technical Information of China (English)
无
1999-01-01
A reinforcemen-based fuzzy neural network control with automatic rule generation RBFNNC) is pro-posed. A set of optimized fuzzy control rules can be automatically generated through reinforcement learning based onthe state variables of object system. RBFNNC was applied to a cart-pole balancing system and simulation resultshows significant improvements on the rule generation.
Directory of Open Access Journals (Sweden)
Jing Lu
2014-11-01
Full Text Available We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM, and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: the first part is the “fuzzy rule-based neural network”, which simulates sequential relations among fuzzy sets using artificial neural network; and the second part is the “neural fuzzy inference system”, which is based on the first part, but could learn new fuzzy rules from the previous ones according to the algorithm we proposed. NFIS-WPM (High Pro and NFIS-WPM (Ave are improved versions of this model. It is well known that the need for accurate weather prediction is apparent when considering the benefits. However, the excessive pursuit of accuracy in weather prediction makes some of the “accurate” prediction results meaningless and the numerical prediction model is often complex and time-consuming. By adapting this novel model to a precipitation prediction problem, we make the predicted outcomes of precipitation more accurate and the prediction methods simpler than by using the complex numerical forecasting model that would occupy large computation resources, be time-consuming and which has a low predictive accuracy rate. Accordingly, we achieve more accurate predictive precipitation results than by using traditional artificial neural networks that have low predictive accuracy.
Recognition algorithm of seabed pipeline defect inspection based on dynamic WBF neural networks
Institute of Scientific and Technical Information of China (English)
Jin Tao; Que Peiwen; Tao Zhengshu
2005-01-01
This paper describes a magnetic flux leak (MFL) model of pipeline defect inspection, and presents a recognition algorithm based on dynamic wavelet basis function (WBF) neural network. The dynamic network utilizes multiscale and multiresolution orthogonal wavelet, through signals backwards propagation, has more significant advantages than BP or other neural networks used in MFL inspection. It also can control the accuracy of the predicted defect profiles, high-speed convergence possessing and well approaching feature. The performance applying the algorithm based on the network to predict defect profile from experimental MFL signals is presented.
Zero phase error control based on neural compensation for flight simulator servo system
Institute of Scientific and Technical Information of China (English)
Liu Jinkun; He Peng; Er Lianjie
2006-01-01
Using the future desired input value, zero phase error controller enables the overall system's frequency response exhibit zero phase shift for all frequencies and a small gain error at low frequency range, and based on this, a new algorithm is presented to design the feedforward controller. However, zero phase error controller is only suitable for certain linear system. To reduce the tracking error and improve robustness, the design of the proposed feedforward controller uses a neural compensation based on diagonal recurrent neural network. Simulation and real-time control results for flight simulator servo system show the effectiveness of the proposed approach.
Grantham, Katie
2003-01-01
Reusable Launch Vehicles (RLVs) have different mission requirements than the Space Shuttle, which is used for benchmark guidance design. Therefore, alternative Terminal Area Energy Management (TAEM) and Approach and Landing (A/L) Guidance schemes can be examined in the interest of cost reduction. A neural network based solution for a finite horizon trajectory optimization problem is presented in this paper. In this approach the optimal trajectory of the vehicle is produced by adaptive critic based neural networks, which were trained off-line to maintain a gradual glideslope.
Fuzzy Control Based on Neural Networks for Armored Vehicle Electric Drive System
Institute of Scientific and Technical Information of China (English)
MA Xiao-jun; LI Hua; ZHANG Jian; ZHANG Yu-nan
2006-01-01
In order to meet rigorous demands of control of electric motors in armored vehicle electric drive system and make the system of strong robustness and antijamming capability, a fuzzy control method based on neural networks is put forward. The simulation model of the armored vehicle electric drive system is built up to test the validity of the control. Simulation experiments show that when load is increased or decreased suddenly, the system adopting fuzzy control based on neural networks is insensitive to parameter change and has little overshooting and oscillation compared with PID control.
Nonlinear model predictive control with guaraneed stability based on pesudolinear neural networks
Institute of Scientific and Technical Information of China (English)
WANG Yongji; WANG Hong
2004-01-01
A nonlinear model predictive control problem based on pseudo-linear neural network (PNN) is discussed, in which the second order on-line optimization method is adopted. The recursive computation of Jacobian matrix is investigated. The stability of the closed loop model predictive control system is analyzed based on Lyapunov theory to obtain the sufficient condition for the asymptotical stability of the neural predictive control system. A simulation was carried out for an exothermic first-order reaction in a continuous stirred tank reactor. It is demonstrated that the proposed control strategy is applicable to some of nonlinear systems.
Nuclide identification algorithm based on K-L transform and neural networks
Energy Technology Data Exchange (ETDEWEB)
Chen Liang [Key Laboratory of Particle and Radiation Imaging (Tsinghua University), Department of Engineering Physics, Tsinghua University, Ministry of Education (China)], E-mail: cliang00@mails.tsinghua.edu.cn; Wei Yixiang [Key Laboratory of Particle and Radiation Imaging (Tsinghua University), Department of Engineering Physics, Tsinghua University, Ministry of Education (China)
2009-01-11
Traditional spectrum analysis algorithm based on peak search is hard to deal with complex overlapped peaks, especially in bad resolution and high background conditions. This paper described a new nuclide identification method based on the Karhunen-Loeve transform (K-L transform) and artificial neural networks. By the K-L transform and feature extraction, the nuclide gamma spectrum was compacted. The K-L transform coefficients were used as the neural network's input. The linear associative memory and ADALINE were discussed. Lots of experiments and tests showed that the method was credible and practical, especially suitable for fast nuclide identification.
Nuclide identification algorithm based on K-L transform and neural networks
Chen, Liang; Wei, Yi-Xiang
2009-01-01
Traditional spectrum analysis algorithm based on peak search is hard to deal with complex overlapped peaks, especially in bad resolution and high background conditions. This paper described a new nuclide identification method based on the Karhunen-Loeve transform (K-L transform) and artificial neural networks. By the K-L transform and feature extraction, the nuclide gamma spectrum was compacted. The K-L transform coefficients were used as the neural network's input. The linear associative memory and ADALINE were discussed. Lots of experiments and tests showed that the method was credible and practical, especially suitable for fast nuclide identification.
Development of Novel Gas Brand Anti-Piracy System based on BP Neural Networks
Energy Technology Data Exchange (ETDEWEB)
Wang, L [School of Aeronautics and Astronautics, Tongji University, Shanghai (China); Zhang, Y Y [Chinese-German School of Postgraduate Studies, Tongji University (China); Ding, L [Chinese-German School of Postgraduate Studies, Tongji University (China)
2006-10-15
The Wireless-net Close-loop gas brand anti-piracy system introduced in this paper is a new type of brand piracy technical product based on BP neural network. It is composed by gas brand piracy label possessing gas exhalation resource, ARM embedded gas-detector, GPRS wireless module and data base of merchandise information. First, the system obtains the information on the special label through gas sensor array ,then the attained signals are transferred into ARM Embedded board and identified by artificial neural network, and finally turns back the outcome of data collection and identification to the manufactures with the help of GPRS module.
Institute of Scientific and Technical Information of China (English)
LI Wei; WANG Wei; MA Yi-mei; WANG Jin-hai
2008-01-01
Presented is a global dynamic reconfiguration design of an artificial neural network based on field programmable gate array(FPGA). Discussed are the dynamic reconfiguration principles and methods. Proposed is a global dynamic reconfiguration scheme using Xilinx FPGA and platform flash. Using the revision capabilities of Xilinx XCF32P platform flash, an artificial neural network based on Xilinx XC2V30P Virtex-Ⅱ can be reconfigured dynamically from back propagation(BP) learning algorithms to BP network testing algorithms. The experimental results indicate that the scheme is feasible, and that, using dynamic reconfiguration technology, FPGA resource utilization can be reduced remarkably.
Fuzzy System for Prognosis of Tank Failure Based on Neural Network
Institute of Scientific and Technical Information of China (English)
Li Guan
2005-01-01
A system for prognosis of tank failures was set up based on the results of analysis on fault phenomena. An algorithm incorporating fuzzy mathematics with the BP neural network was used to solve this prognosis model, and the availability of this model was also analyzed. This neural network-based fuzzy system for prognosis of tank failures has been put into operation at Huangdao oil terminal. The application results have shown that this system is effective for real-time prognosis of various potential tank failures and timely adoption of mitigative measures to avoid major tank accidents, which would have great significance for safeguarding the safe operation of the oil terminal.
Artificial neural network based inverse design method for circular sliding slopes
Institute of Scientific and Technical Information of China (English)
丁德馨; 张志军
2004-01-01
Current design method for circular sliding slopes is not so reasonable that it often results in slope sliding. As a result, artificial neural network (ANN) is used to establish an artificial neural network based inverse design method for circular sliding slopes. A sample set containing 21 successful circular sliding slopes excavated in the past is used to train the network. A test sample of 3 successful circular sliding slopes excavated in the past is used to test the trained network. The test results show that the ANN based inverse design method is valid and can be applied to the design of circular sliding slopes.
基于格式塔心理学和Euler spiral的轮廓修复算法%Gestalt psychology and Euler spiral for contour completion
Institute of Scientific and Technical Information of China (English)
张桂梅; 刘丕玉
2013-01-01
现有的轮廓修复方法假设已知遮挡发生的位置,且未考虑人类认知特性.针对该问题,提出一种结构较完整的轮廓修复方法.算法首先检测T型节点确定遮挡发生的位置；根据格式塔心理学的完形法则提出组合约束条件,据此将T型节点进行组合.对平滑轮廓遮挡,基于Euler spiral,并以曲率变化最小作为能量最小化准则,修复局部缺失的轮廓；对角点轮廓遮挡,利用过角点的两条曲线的可见部分构造两条Euler spiral,根据Euler spiral的扩展性对构造的两条Euler spiral进行延拓并求交,从而修复角点遮挡轮廓.方法能自动确定遮挡发生的位置,能够对T型节点进行正确组合,使轮廓修复更符合人类视觉感知；对角点轮廓遮挡采用Euler spiral的扩展性进行修复,能克服角点轮廓修复仅适合于修复轮廓为直线或圆弧的规则物体.应用模拟图像和真实图像验证了方法的有效性.%The existing contour completion methods assume known the positions of occlusion,and do not consider the human cognitive characteristics.To solve these problems,this paper proposed a more complete contour completion method.The algorithm firstly detected T-junctions to locate the occlusion; then proposed the combined constraint conditions based on laws of organization in Gestalt psychology,combined T-junctions using the above conditions.Finally,it investigated the contour completion in two kinds.For smooth contour,it completed by Euler spiral.It considered curvature variation minimum as energy minimization criterion.For occluded comer contour,it constructed two Euler spiral using the visible part of the two curves which composed the comer point.The constructed two Euler spirals extended and intersected with each other according to the extensible of Euler spiral.This method can automatically locate the occlusion by detecting T-junctions; the contour completion can more correspond with human visual perception
Recurrent neural networks-based multivariable system PID predictive control
Institute of Scientific and Technical Information of China (English)
ZHANG Yan; WANG Fanzhen; SONG Ying; CHEN Zengqiang; YUAN Zhuzhi
2007-01-01
A nonlinear proportion integration differentiation (PID) controller is proposed on the basis of recurrent neural networks,due to the difficulty of tuning the parameters of conventional PID controller.In the control process of nonlinear multivariable system,a decoupling controller was constructed,which took advantage of multi-nonlinear PID controllers in parallel.With the idea of predictive control,two multivariable predictive control strategies were established.One strategy involved the use of the general minimum variance control function on the basis of recursive multi-step predictive method.The other involved the adoption of multistep predictive cost energy to train the weights of the decoupling controller.Simulation studies have shown the efficiency of these strategies.
Electronic Nose Based on an Optimized Competition Neural Network
Directory of Open Access Journals (Sweden)
Haiping Zhang
2011-05-01
Full Text Available In view of the fact that there are disadvantages in that the class number must be determined in advance, the value of learning rates are hard to fix, etc., when using traditional competitive neural networks (CNNs in electronic noses (E-noses, an optimized CNN method was presented. The optimized CNN was established on the basis of the optimum class number of samples according to the changes of the Davies and Bouldin (DB value and it could increase, divide, or delete neurons in order to adjust the number of neurons automatically. Moreover, the learning rate changes according to the variety of training times of each sample. The traditional CNN and the optimized CNN were applied to five kinds of sorted vinegars with an E-nose. The results showed that optimized network structures could adjust the number of clusters dynamically and resulted in good classifications.
Medical image segmentation based on cellular neural network
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
The application of cellular neural network (CNN) has made great progress in image processing. When the selected objects extraction (SOE) CNN is applied to gray scale images, its effects depend on the choice of initial points. In this paper, we take medical images as an example to analyze this limitation. Then an improved algorithm is proposed in which we can segment any gray level objects regardless of the limitation stated above. We also use the gradient information and contour detection CNN to determine the contour and ensure the veracity of segmentation effectively. Finally, we apply the improved algorithm to tumor segmentation of the human brain MR image. The experimental results show that the algorithm is practical and effective.
Culture and social support: neural bases and biological impact.
Sherman, David K; Kim, Heejung S; Taylor, Shelley E
2009-01-01
Social support is an effective means by which people cope with stressful events, and consequently, it beneficially affects health and well-being. Yet there are profound cultural differences in the effectiveness of different types of support and how people use their support networks. In this paper, we examine research on the impact of culture on social support, the neural underpinnings of social support, and how cultural differences in social support seeking are manifested biologically. We focus on cultural factors that may affect individuals' decisions to seek or not to seek social support and how culture moderates the impact of support seeking on biological and psychological health outcomes. We also examine recent research on the interaction between genes and culture in social support use. Discussion centers on the importance of developing an overarching framework of social support that integrates health psychology, cultural psychology, social neuroscience, and genetics.
The Neural Bases of Framing Effects in Social Dilemmas
DEFF Research Database (Denmark)
Macoveanu, Julian; Ramsøy, Thomas; Skov, Martin
2015-01-01
Human behavior in social dilemmas is strongly framed by the social context, but the mechanisms underlying this framing effect remains poorly understood. To identify the behavioral and neural responses mediating framing of social interactions, subjects underwent functional Magnetic Resonance Imaging...... while playing a Prisoners Dilemma game. In separate neuroimaging sessions, the game was either framed as a cooperation game or a competition game. Social decisions where subjects were affected by the frame engaged the hippocampal formation, precuneus, dorsomedial prefrontal cortex and lateral temporal...... gyrus. Among these regions, the engagement of the left hippocampus was further modulated by individual differences in empathy. Social decisions not adhering to the frame were associated with stronger engagement of the angular gyrus and trend increases in lateral orbitofrontal cortex, posterior...
Stability Analysis of Neural Networks-Based System Identification
Directory of Open Access Journals (Sweden)
Talel Korkobi
2008-01-01
Full Text Available This paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomena during the learning process are avoided. A Lyapunov analysis leads to the computation of the expression of a convenient adaptive learning rate verifying the convergence stability criteria. Finally, the elaborated training algorithm is applied in several simulations. The results confirm the effectiveness of the CSBP algorithm.
Strawberry Maturity Neural Network Detectng System Based on Genetic Algorithm
Xu, Liming
The quick and non-detective detection of agriculture product is one of the measures to increase the precision and productivity of harvesting and grading. Having analyzed H frequency of different maturities in different light intensities, the results show that H frequency for the same maturity has little influence in different light intensities; Under the same light intensity, three strawberry maturities are changing in order. After having confirmed the H frequency section to distinguish the different strawberry maturity, the triplelayer feed-forward neural network system to detect strawberry maturity was designed by using genetic algorithm. The test results show that the detecting precision ratio is 91.7%, it takes 160ms to distinguish one strawberry. Therefore, the online non-detective detecting the strawberry maturity could be realized.
The neural bases of framing effects in social dilemmas
DEFF Research Database (Denmark)
Macoveanu, Julian; Ramsøy, Thomas; Skov, Martin
2016-01-01
Human behavior in social dilemmas is strongly framed by the social context, but the mechanisms underlying this framing effect remain poorly understood. To identify the behavioral and neural responses mediating framing of social interactions, participants underwent functional MRI while playing...... findings provide the first insight into the mechanisms underlying framing of behavior in social dilemmas, indicating increased engagement of the hippocampus and neocortical areas involved in memory, social reasoning, and mentalizing when participants make decisions that conform to the imposed social frame....... a prisoner’s dilemma game. In separate neuroimaging sessions, the game was either framed as a cooperation game or a competition game. The framing of social decisions engaged the hippocampal formation, precuneus, dorsomedial prefrontal cortex, and lateral temporal gyrus. Among these regions, the engagement...
The neural bases of framing effects in social dilemmas
DEFF Research Database (Denmark)
Macoveanu, Julian; Ramsøy, Thomas; Skov, Martin
Human behavior in social dilemmas is strongly framed by the social context, but the mechanisms underlying this framing effect remains poorly understood. To identify the behavioral and neural responses mediating framing of social interactions, subjects underwent functional Magnetic Resonance Imaging...... intraparietal cortex, and temporopolar cortex. Our findings provide the first insight into the mechanisms underlying framing of behavior in social dilemmas, indicating increased engagement of the hippocampus and neocortical areas involved in memory, social reasoning and mentalizing when subjects make decisions...... while playing a Prisoners Dilemma game. In separate neuroimaging sessions, the game was either framed as a cooperation game or a competition game. Social decisions where subjects were affected by the frame engaged the hippocampal formation, precuneus, dorsomedial prefrontal cortex and lateral temporal...
Neural Network-Based Multimode Fiber-Optic Information Transmission
Marusarz, Ronald K.; Sayeh, Mohammad R.
2001-01-01
A new technique for transmitting information through multimode fiber-optic cables is presented. This technique sends parallel channels through the fiber-optic cable, thereby greatly improving the data transmission rate compared with that of the current technology, which uses serial data transmission through single-mode fiber. An artificial neural network is employed to decipher the transmitted information from the received speckle pattern. Several different preprocessing algorithms are developed, tested, and evaluated. These algorithms employ average region intensity, distributed individual pixel intensity, and maximum mean-square-difference optimal group selection methods. The effect of modal dispersion on the data rate is analyzed. An increased data transmission rate by a factor of 37 over that of single-mode fibers is realized. When implementing our technique, we can increase the channel capacity of a typical multimode fiber by a factor of 6.
High power fuel cell simulator based on artificial neural network
Energy Technology Data Exchange (ETDEWEB)
Chavez-Ramirez, Abraham U.; Munoz-Guerrero, Roberto [Departamento de Ingenieria Electrica, CINVESTAV-IPN. Av. Instituto Politecnico Nacional No. 2508, D.F. CP 07360 (Mexico); Duron-Torres, S.M. [Unidad Academica de Ciencias Quimicas, Universidad Autonoma de Zacatecas, Campus Siglo XXI, Edif. 6 (Mexico); Ferraro, M.; Brunaccini, G.; Sergi, F.; Antonucci, V. [CNR-ITAE, Via Salita S. Lucia sopra Contesse 5-98126 Messina (Italy); Arriaga, L.G. [Centro de Investigacion y Desarrollo Tecnologico en Electroquimica S.C., Parque Tecnologico Queretaro, Sanfandila, Pedro Escobedo, Queretaro (Mexico)
2010-11-15
Artificial Neural Network (ANN) has become a powerful modeling tool for predicting the performance of complex systems with no well-known variable relationships due to the inherent properties. A commercial Polymeric Electrolyte Membrane fuel cell (PEMFC) stack (5 kW) was modeled successfully using this tool, increasing the number of test into the 7 inputs - 2 outputs-dimensional spaces in the shortest time, acquiring only a small amount of experimental data. Some parameters could not be measured easily on the real system in experimental tests; however, by receiving the data from PEMFC, the ANN could be trained to learn the internal relationships that govern this system, and predict its behavior without any physical equations. Confident accuracy was achieved in this work making possible to import this tool to complex systems and applications. (author)
Ran, Changyan; Cheng, Xianghong
2016-09-02
This paper presents a direct and non-singular approach based on an unscented Kalman filter (UKF) for the integration of strapdown inertial navigation systems (SINSs) with the aid of velocity. The state vector includes velocity and Euler angles, and the system model contains Euler angle kinematics equations. The measured velocity in the body frame is used as the filter measurement. The quaternion nonlinear equality constraint is eliminated, and the cross-noise problem is overcome. The filter model is simple and easy to apply without linearization. Data fusion is performed by an UKF, which directly estimates and outputs the navigation information. There is no need to process navigation computation and error correction separately because the navigation computation is completed synchronously during the filter time updating. In addition, the singularities are avoided with the help of the dual-Euler method. The performance of the proposed approach is verified by road test data from a land vehicle equipped with an odometer aided SINS, and a singularity turntable test is conducted using three-axis turntable test data. The results show that the proposed approach can achieve higher navigation accuracy than the commonly-used indirect approach, and the singularities can be efficiently removed as the result of dual-Euler method.
Ravat, Dhananjay
1996-01-01
The applicability of the Euler method of source location determination was investigated on several model situations pertinent to satellite-data scale situations as well as Magsat data of Europe. Our investigations enabled us to understand the end-member cases for which the Euler method will work with the present satellite magnetic data and also the cases for which the assumptions implicit in the Euler method will not be met by the present satellite magnetic data. These results have been presented in one invited lecture at the Indo-US workshop on Geomagnetism in Studies of the Earth's Interior in August 1994 in Pune, India, and at one presentation at the 21st General Assembly of the IUGG in July 1995 in Boulder, CO. A new method, called Anomaly Attenuation Rate (AAR) Method (based on the Euler method), was developed during this study. This method is scale-independent and is appropriate to locate centroids of semi-compact three dimensional sources of gravity and magnetic anomalies. The method was presented during 1996 Spring AGU meeting and a manuscript describing this method is being prepared for its submission to a high-ranking journal. The grant has resulted in 3 papers and presentations at national and international meetings and one manuscript of a paper (to be submitted shortly to a reputable journal).
Ran, Changyan; Cheng, Xianghong
2016-01-01
This paper presents a direct and non-singular approach based on an unscented Kalman filter (UKF) for the integration of strapdown inertial navigation systems (SINSs) with the aid of velocity. The state vector includes velocity and Euler angles, and the system model contains Euler angle kinematics equations. The measured velocity in the body frame is used as the filter measurement. The quaternion nonlinear equality constraint is eliminated, and the cross-noise problem is overcome. The filter model is simple and easy to apply without linearization. Data fusion is performed by an UKF, which directly estimates and outputs the navigation information. There is no need to process navigation computation and error correction separately because the navigation computation is completed synchronously during the filter time updating. In addition, the singularities are avoided with the help of the dual-Euler method. The performance of the proposed approach is verified by road test data from a land vehicle equipped with an odometer aided SINS, and a singularity turntable test is conducted using three-axis turntable test data. The results show that the proposed approach can achieve higher navigation accuracy than the commonly-used indirect approach, and the singularities can be efficiently removed as the result of dual-Euler method. PMID:27598169
Prezioso, M.; Merrikh-Bayat, F.; Chakrabarti, B.; Strukov, D.
2016-02-01
Artificial neural networks have been receiving increasing attention due to their superior performance in many information processing tasks. Typically, scaling up the size of the network results in better performance and richer functionality. However, large neural networks are challenging to implement in software and customized hardware are generally required for their practical implementations. In this work, we will discuss our group's recent efforts on the development of such custom hardware circuits, based on hybrid CMOS/memristor circuits, in particular of CMOL variety. We will start by reviewing the basics of memristive devices and of CMOL circuits. We will then discuss our recent progress towards demonstration of hybrid circuits, focusing on the experimental and theoretical results for artificial neural networks based on crossbarintegrated metal oxide memristors. We will conclude presentation with the discussion of the remaining challenges and the most pressing research needs.
Macrobenthos habitat potential mapping using GIS-based artificial neural network models.
Lee, Saro; Park, Inhye; Koo, Bon Joo; Ryu, Joo-Hyung; Choi, Jong-Kuk; Woo, Han Jun
2013-02-15
This paper proposes and tests a method of producing macrobenthos habitat potential maps in Hwangdo tidal flat, Korea based on an artificial neural network. Samples of macrobenthos were collected during field work, and eight control factors were compiled as a spatial database from remotely sensed data and GIS analysis. The macrobenthos habitat potential maps were produced using an artificial neural network model. Macrobenthos habitat potential maps were made for Macrophthalmus dilatatus, Cerithideopsilla cingulata, and Armandia lanceolata. The maps were validated by compared with the surveyed habitat locations. A strong correlation between the potential maps and species locations was revealed. The validation result showed average accuracies of 74.9%, 78.32%, and 73.27% for M. dilatatus, C. cingulata, and A. lanceolata, respectively. A GIS-based artificial neural network model combined with remote sensing techniques is an effective tool for mapping the areas of macrobenthos habitat potential in tidal flats.
D-FNN Based Modeling and BP Neural Network Decoupling Control of PVC Stripping Process
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Shu-zhi Gao
2014-01-01
Full Text Available PVC stripping process is a kind of complicated industrial process with characteristics of highly nonlinear and time varying. Aiming at the problem of establishing the accurate mathematics model due to the multivariable coupling and big time delay, the dynamic fuzzy neural network (D-FNN is adopted to establish the PVC stripping process model based on the actual process operation datum. Then, the PVC stripping process is decoupled by the distributed neural network decoupling module to obtain two single-input-single-output (SISO subsystems (slurry flow to top tower temperature and steam flow to bottom tower temperature. Finally, the PID controller based on BP neural networks is used to control the decoupled PVC stripper system. Simulation results show the effectiveness of the proposed integrated intelligent control method.
Multi-AUV Hunting Algorithm Based on Bio-inspired Neural Network in Unknown Environments
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Daqi Zhu
2015-11-01
Full Text Available The multi-AUV hunting problem is one of the key issues in multi-robot system research. In order to hunt the target efficiently, a new hunting algorithm based on a bio-inspired neural network has been proposed in this paper. Firstly, the AUV’s working environment can be represented, based on the biological-inspired neural network model. There is one-to-one correspondence between each neuron in the neural network and the position of the grid map in the underwater environment. The activity values of biological neurons then guide the AUV’s sailing path and finally the target is surrounded by AUVs. In addition, a method called negotiation is used to solve the AUV’s allocation of hunting points. The simulation results show that the algorithm used in the paper can provide rapid and highly efficient path planning in the unknown environment with obstacles and non-obstacles.
Leong, Harrison Monfook
1988-01-01
General formulae for mapping optimization problems into systems of ordinary differential equations associated with artificial neural networks are presented. A comparison is made to optimization using gradient-search methods. The performance measure is the settling time from an initial state to a target state. A simple analytical example illustrates a situation where dynamical systems representing artificial neural network methods would settle faster than those representing gradient-search. Settling time was investigated for a more complicated optimization problem using computer simulations. The problem was a simplified version of a problem in medical imaging: determining loci of cerebral activity from electromagnetic measurements at the scalp. The simulations showed that gradient based systems typically settled 50 to 100 times faster than systems based on current neural network optimization methods.
Single-hidden-layer feed-forward quantum neural network based on Grover learning.
Liu, Cheng-Yi; Chen, Chein; Chang, Ching-Ter; Shih, Lun-Min
2013-09-01
In this paper, a novel single-hidden-layer feed-forward quantum neural network model is proposed based on some concepts and principles in the quantum theory. By combining the quantum mechanism with the feed-forward neural network, we defined quantum hidden neurons and connected quantum weights, and used them as the fundamental information processing unit in a single-hidden-layer feed-forward neural network. The quantum neurons make a wide range of nonlinear functions serve as the activation functions in the hidden layer of the network, and the Grover searching algorithm outstands the optimal parameter setting iteratively and thus makes very efficient neural network learning possible. The quantum neuron and weights, along with a Grover searching algorithm based learning, result in a novel and efficient neural network characteristic of reduced network, high efficient training and prospect application in future. Some simulations are taken to investigate the performance of the proposed quantum network and the result show that it can achieve accurate learning.
Directory of Open Access Journals (Sweden)
Nahid Ardalani
2011-07-01
Full Text Available This article describes linear and nonlinear Artificial Neural Network(ANN-based predictors as Autoregressive Moving Average models with Auxiliary input (ARMAX process for Signal to Interference plus Noise Ratio (SINR prediction in Direct Sequence Code Division Multiple Access (DS/CDMA systems. The Multi Layer Perceptron (MLP neural network with nonlinear function is used as nonlinear neural network and Adaptive Linear (Adaline predictor is used as linear predictor. The problem of complexity of the MLP and Adaline structures is solved by using the Minimum Mean Squared Error (MMSE principle to select the optimal numbers of input and hidden nodes by try and error role. Simulation results show that both of MLP and Adaline optimal neural networks can track the effect of deep fading due to using a 1.8 GHZ carrier frequency at the urban mobile speeds of 10 km/h, 50 km/h and 120 km/h with tolerable estimation errors. Therefore, the neural networkbased predictor is well suitable SINR-based predictor in closedloop power control to combat multi path fading in CDMA systems.
TECHNICAL NOTE: The development of a PZT-based microdrive for neural signal recording
Park, Sangkyu; Yoon, Euisung; Lee, Sukchan; Shin, Hee-sup; Park, Hyunjun; Kim, Byungkyu; Kim, Daesoo; Park, Jongoh; Park, Sukho
2008-04-01
A hand-controlled microdrive has been used to obtain neural signals from rodents such as rats and mice. However, it places severe physical stress on the rodents during its manipulation, and this stress leads to alertness in the mice and low efficiency in obtaining neural signals from the mice. To overcome this issue, we developed a novel microdrive, which allows one to adjust the electrodes by a piezoelectric device (PZT) with high precision. Its mass is light enough to install on the mouse's head. The proposed microdrive has three H-type PZT actuators and their guiding structure. The operation principle of the microdrive is based on the well known inchworm mechanism. When the three PZT actuators are synchronized, linear motion of the electrode is produced along the guiding structure. The electrodes used for the recording of the neural signals from neuron cells were fixed at one of the PZT actuators. Our proposed microdrive has an accuracy of about 400 nm and a long stroke of about 5 mm. In response to formalin-induced pain, single unit activities are robustly measured at the thalamus with electrodes whose vertical depth is adjusted by the microdrive under urethane anesthesia. In addition, the microdrive was efficient in detecting neural signals from mice that were moving freely. Thus, the present study suggests that the PZT-based microdrive could be an alternative for the efficient detection of neural signals from mice during behavioral states without any stress to the mice.
Central differences, Euler numbers and symbolic methods
Dowker, J S
2013-01-01
I relate some coefficients encountered when computing the functional determinants on spheres to the central differentials of nothing. In doing this I use some historic works, in particular transcribing the elegant symbolic formalism of Jeffery (1861) into central difference form which has computational advantages for Euler numbers, as discovered by Shovelton (1915). I derive sum rules for these, and for the central differentials, the proof of which involves an interesting expression for powers of sech x as multiple derivatives. I present a more general, symbolic treatment of central difference calculus which allows known, and unknown, things to be obtained in an elegant and compact fashion gaining, at no cost, the expansion of the powers of the inverse sinh, a basic central function. Systematic use is made of the operator 2 asinh(D/2). Umbral calculus is employed to compress the operator formalism. For example the orthogonality/completeness of the factorial numbers, of the first and second kinds, translates, ...
The Euler current and parity odd transport
Golkar, Siavash; Son, Dam T
2014-01-01
For a spacetime of odd dimensions endowed with a unit vector field, we introduce a new topological current that is identically conserved and whose charge is equal to the Euler character of the even dimensional spacelike foliations. The existence of this current allows us to introduce new Chern-Simons-type terms in the effective field theories describing relativistic quantum Hall states and (2+1) dimensional superfluids. Using effective field theory, we calculate various correlation functions and identify transport coefficients. In the quantum Hall case, this current provides the natural relativistic generalization of the Wen-Zee term, required to characterize the shift and Hall viscosity in quantum Hall systems. For the superfluid case this term is required to have nonzero Hall viscosity and to describe superfluids with non s-wave pairing.
Genetic Algorithm-Based Artificial Neural Network for Voltage Stability Assessment
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Garima Singh
2011-01-01
Full Text Available With the emerging trend of restructuring in the electric power industry, many transmission lines have been forced to operate at almost their full capacities worldwide. Due to this, more incidents of voltage instability and collapse are being observed throughout the world leading to major system breakdowns. To avoid these undesirable incidents, a fast and accurate estimation of voltage stability margin is required. In this paper, genetic algorithm based back propagation neural network (GABPNN has been proposed for voltage stability margin estimation which is an indication of the power system's proximity to voltage collapse. The proposed approach utilizes a hybrid algorithm that integrates genetic algorithm and the back propagation neural network. The proposed algorithm aims to combine the capacity of GAs in avoiding local minima and at the same time fast execution of the BP algorithm. Input features for GABPNN are selected on the basis of angular distance-based clustering technique. The performance of the proposed GABPNN approach has been compared with the most commonly used gradient based BP neural network by estimating the voltage stability margin at different loading conditions in 6-bus and IEEE 30-bus system. GA based neural network learns faster, at the same time it provides more accurate voltage stability margin estimation as compared to that based on BP algorithm. It is found to be suitable for online applications in energy management systems.
Automatic event detection based on artificial neural networks
Doubravová, Jana; Wiszniowski, Jan; Horálek, Josef
2015-04-01
The proposed algorithm was developed to be used for Webnet, a local seismic network in West Bohemia. The Webnet network was built to monitor West Bohemia/Vogtland swarm area. During the earthquake swarms there is a large number of events which must be evaluated automatically to get a quick estimate of the current earthquake activity. Our focus is to get good automatic results prior to precise manual processing. With automatic data processing we may also reach a lower completeness magnitude. The first step of automatic seismic data processing is the detection of events. To get a good detection performance we require low number of false detections as well as high number of correctly detected events. We used a single layer recurrent neural network (SLRNN) trained by manual detections from swarms in West Bohemia in the past years. As inputs of the SLRNN we use STA/LTA of half-octave filter bank fed by vertical and horizontal components of seismograms. All stations were trained together to obtain the same network with the same neuron weights. We tried several architectures - different number of neurons - and different starting points for training. Networks giving the best results for training set must not be the optimal ones for unknown waveforms. Therefore we test each network on test set from different swarm (but still with similar characteristics, i.e. location, focal mechanisms, magnitude range). We also apply a coincidence verification for each event. It means that we can lower the number of false detections by rejecting events on one station only and force to declare an event on all stations in the network by coincidence on two or more stations. In further work we would like to retrain the network for each station individually so each station will have its own coefficients (neural weights) set. We would also like to apply this method to data from Reykjanet network located in Reykjanes peninsula, Iceland. As soon as we have a reliable detection, we can proceed to
Nishizawa, K; Izawa, E-I; Watanabe, S
2011-12-01
Large-billed crows (Corvus macrorhynchos), highly social birds, form stable dominance relationships based on the memory of win/loss outcomes of first encounters and on individual discrimination. This socio-cognitive behaviour predicts the existence of neural mechanisms for integration of social behaviour control and individual discrimination. This study aimed to elucidate the neural substrates of memory-based dominance in crows. First, the formation of dominance relationships was confirmed between males in a dyadic encounter paradigm. Next, we examined whether neural activities in 22 focal nuclei of pallium and subpallium were correlated with social behaviour and stimulus familiarity after exposure to dominant/subordinate familiar individuals and unfamiliar conspecifics. Neural activity was determined by measuring expression level of the immediate-early-gene (IEG) protein Zenk. Crows displayed aggressive and/or submissive behaviour to opponents less frequently but more discriminatively in subsequent encounters, suggesting stable dominance based on memory, including win/loss outcomes of the first encounters and individual discrimination. Neural correlates of aggressive and submissive behaviour were found in limbic subpallium including septum, bed nucleus of the striae terminalis (BST), and nucleus taeniae of amygdala (TnA), but also those to familiarity factor in BST and TnA. Contrastingly, correlates of social behaviour were little in pallium and those of familiarity with exposed individuals were identified in hippocampus, medial meso-/nidopallium, and ventro-caudal nidopallium. Given the anatomical connection and neural response patterns of the focal nuclei, neural networks connecting pallium and limbic subpallium via hippocampus could be involved in the integration of individual discrimination and social behaviour control in memory-based dominance in the crow.
Maass, Wolfgang; Natschläger, Thomas; Markram, Henry
2002-11-01
A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real time. We propose a new computational model for real-time computing on time-varying input that provides an alternative to paradigms based on Turing machines or attractor neural networks. It does not require a task-dependent construction of neural circuits. Instead, it is based on principles of high-dimensional dynamical systems in combination with statistical learning theory and can be implemented on generic evolved or found recurrent circuitry. It is shown that the inherent transient dynamics of the high-dimensional dynamical system formed by a sufficiently large and heterogeneous neural circuit may serve as universal analog fading memory. Readout neurons can learn to extract in real time from the current state of such recurrent neural circuit information about current and past inputs that may be needed for diverse tasks. Stable internal states are not required for giving a stable output, since transient internal states can be transformed by readout neurons into stable target outputs due to the high dimensionality of the dynamical system. Our approach is based on a rigorous computational model, the liquid state machine, that, unlike Turing machines, does not require sequential transitions between well-defined discrete internal states. It is supported, as the Turing machine is, by rigorous mathematical results that predict universal computational power under idealized conditions, but for the biologically more realistic scenario of real-time processing of time-varying inputs. Our approach provides new perspectives for the interpretation of neural coding, the design of experiments and data analysis in neurophysiology, and the solution of problems in robotics and neurotechnology.
Nonlinear modeling of PEMFC based on neural networks identification
Institute of Scientific and Technical Information of China (English)
SUN Tao; CAO Guang-yi; ZHU Xin-jian
2005-01-01
The proton exchange membrane generation technology is highly efficient and clean, and is considered as the most hopeful "green" power technology. The operating principles of proton exchange membrane fuel cell (PEMFC) system involve thermodynamics, electrochemistry, hydrodynamics and mass transfer theory, which comprise a complex nonlinear system, for which it is difficult to establish a mathematical model. This paper first simply analyzes the necessity of the PEMFC generation technology, then introduces the generating principle from four aspects: electrode, single cell, stack, system; and then uses the approach and self-study ability of artificial neural network to build the model of nonlinear system, and adapts the Levenberg-Marquardt BP (LMBP) to build the electric characteristic model of PEMFC. The model uses experimental data as training specimens, on the condition the system is provided enough hydrogen. Considering the flow velocity of air (or oxygen) and the cell operational temperature as inputs, the cell voltage and current density as the outputs and establishing the electric characteristic model of PEMFC according to the different cell temperatures. The voltage-current output curves of model has some guidance effect for improving the cell performance, and provide basic data for optimizing cell performance that have practical significance.
Seabed Classification Using BP Neural Network Based on GA
Institute of Scientific and Technical Information of China (English)
Yang Fanlin; Liu Jingnan
2003-01-01
Side scan sonar imaging is one of the advanced methods for seabed study. In order to be utilized in other projects, such as ocean engineering, the image needs to be classified according to the distributions of different classes of seabed materials. In this paper, seabed image is classified according to BP neural network, and Genetic Algorithm is adopted in train network in this paper. The feature vectors are average intensity, six statistics of texture and two dimensions of fractal. It considers not only the spatial correlation between different pixels, but also the terrain coarseness. The texture is denoted by the statistics of the co-occurrence matrix. Double Blanket algorithm is used to calculate dimension. Because a uniform fractal may not be sufficient to describe a seafloor, two dimensions are calculated respectively by the upper blanket and the lower blanket. However, in sonar image, fractal has directivity, i. e.there are different dimensions in different direction. Dimensions are different in acrosstrack and alongtrack, so the average of four directions is used to solve this problem. Finally, the real data verify the algorithm. In this paper, one hidden layer including six nodes is adopted. The BP network is rapidly and accurately convergent through GA. Correct classification rate is 92.5% in the result.
Neural bases of recommendations differ according to social network structure.
Brook O'Donnell, Matthew; Bayer, Joseph B; Cascio, Christopher N; Falk, Emily B
2017-01-18
Ideas spread across social networks, but not everyone is equally positioned to be a successful recommender. Do individuals with more opportunities to connect otherwise unconnected others-high information brokers-use their brains differently than low information brokers when making recommendations? We test the hypothesis that those with more opportunities for information brokerage may use brain systems implicated in considering the thoughts, perspectives, and mental states of others (i.e., 'mentalizing') more when spreading ideas. We used social network analysis to quantify individuals' opportunities for information brokerage. This served as a predictor of activity within meta-analytically defined neural regions associated with mentalizing (DMPFC, TPJ, MPFC, PCC, MTG) as participants received feedback about peer opinions of mobile game apps. Higher information brokers exhibited more activity in this mentalizing network when receiving divergent peer feedback and updating their recommendation. These data support the idea that those in different network positions may use their brains differently to perform social tasks. Different social network positions might provide more opportunities to engage specific psychological processes. Or those who tend to engage such processes more may place themselves in systematically different network positions. These data highlight the value of integrating levels of analysis, from brain networks to social networks.
A neural network based reputation bootstrapping approach for service selection
Wu, Quanwang; Zhu, Qingsheng; Li, Peng
2015-10-01
With the concept of service-oriented computing becoming widely accepted in enterprise application integration, more and more computing resources are encapsulated as services and published online. Reputation mechanism has been studied to establish trust on prior unknown services. One of the limitations of current reputation mechanisms is that they cannot assess the reputation of newly deployed services as no record of their previous behaviours exists. Most of the current bootstrapping approaches merely assign default reputation values to newcomers. However, by this kind of methods, either newcomers or existing services will be favoured. In this paper, we present a novel reputation bootstrapping approach, where correlations between features and performance of existing services are learned through an artificial neural network (ANN) and they are then generalised to establish a tentative reputation when evaluating new and unknown services. Reputations of services published previously by the same provider are also incorporated for reputation bootstrapping if available. The proposed reputation bootstrapping approach is seamlessly embedded into an existing reputation model and implemented in the extended service-oriented architecture. Empirical studies of the proposed approach are shown at last.
Fuzzy Control System of Hydraulic Roll Bending Based on Genetic Neural Network
Institute of Scientific and Technical Information of China (English)
JIA Chun-yu; LIU Hong-min; ZHOU Hui-feng
2005-01-01
For nonlinear hydraulic roll bending control, a new fuzzy intelligent control method was proposed based on the genetic neural network. The method taking account of dynamic and static characteristics of control system has settled the problems of recognizing and controlling the unknown, uncertain and nonlinear system successfully,and has been applied to hydraulic roll bending control. The simulation results indicate that the system has good performance and strong robustness, and is better than traditional PID and neural-fuzzy control. The method is an effective tool to control roll bending force with increased dynamic response speed of control system and enhanced tracking accuracy.
Institute of Scientific and Technical Information of China (English)
鄢田云; 张翠芳; 靳蕃
2003-01-01
Identification simulation for dynamical system which is based on genetic algorithm (GA) and recurrent multilayer neural network (RMNN) is presented. In order to reduce the inputs of the model, RMNN which can remember and store some previous parameters is used for identifier. And for its high efficiency and optimization, genetic algorithm is introduced into training RMNN. Simulation results show the effectiveness of the proposed scheme. Under the same training algorithm, the identification performance of RMNN is superior to that of nonrecurrent multilayer neural network (NRMNN).
Audio Watermarking Based on HAS and Neural Networks in DCT Domain
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Cheng Ji-Shiung
2003-01-01
Full Text Available We propose a new intelligent audio watermarking method based on the characteristics of the HAS and the techniques of neural networks in the DCT domain. The method makes the watermark imperceptible by using the audio masking characteristics of the HAS. Moreover, the method exploits a neural network for memorizing the relationships between the original audio signals and the watermarked audio signals. Therefore, the method is capable of extracting watermarks without original audio signals. Finally, the experimental results are also included to illustrate that the method significantly possesses robustness to be immune against common attacks for the copyright protection of digital audio.
Effects of Interactive Function Forms in a Self-Organized Critical Model Based on Neural Networks
Institute of Scientific and Technical Information of China (English)
ZHAO Xiao-Wei; ZHOU Li-Ming; CHEN Tian-Lun
2003-01-01
Based on the standard self-organizing map neural network model and an integrate-and-fire mechanism, we introduce a kind of coupled map lattice system to investigate scale-invariance behavior in the activity of model neural populations. We let the parameter β, which together with α represents the interactive strength between neurons, have different function forms, and we find the function forms and their parameters are very important to our model's avalanche dynamical behaviors, especially to the emergence of different avalanche behaviors in different areas of our system.
Directory of Open Access Journals (Sweden)
Valentin Potapov
2016-12-01
Full Text Available Purpose: This work presents a method of diagnosing the technical condition of turbofan engines using hybrid neural network algorithm based on software developed for the analysis of data obtained in the aircraft life. Methods: allows the engine diagnostics with deep recognition to the structural assembly in the presence of single structural damage components of the engine running and the multifaceted damage. Results: of the optimization of neural network structure to solve the problems of evaluating technical state of the bypass turbofan engine, when used with genetic algorithms.
Different Avalanche Behaviors in Different Specific Areas of a System Based on Neural Networks
Institute of Scientific and Technical Information of China (English)
ZHAOXiao-Wei; CHENTian-Lun
2003-01-01
Based on the standard self-organizing map (SOM) neural network model and an integrate-and-fire mecha-nism, we introduce a kind of coupled map lattice system to investigate scale-invariance behavior in the activity of model neural populations. We find power-law distribution behavior of avalanche size in our model. But more importantly, we find there are different avalanche distribution behaviors in different specific areas of our system, which are formed by the topological learning process of the SOM net.
Effects of Interactive Function Forms in a Self-Organized Critical Model Based on Neural Networks
Institute of Scientific and Technical Information of China (English)
ZHAOXiao-Wei; ZHOULi-Ming; CHENTian-Lun
2003-01-01
Based on the standard self-organizing map neural network model and an integrate-and-fire mechanism, we introduce a kind of coupled map lattice system to investigate scale-invariance behavior in the activity of model neural populations. We let the parameter β, which together with α represents the interactive strength between neurons, have different function forms, and we find the function forms and their parameters are very important to our model''s avalanche dynamical behaviors, especially to the emergence of different avalanche behaviors in different areas of our system.
One-way hash function based on hyper-chaotic cellular neural network
Institute of Scientific and Technical Information of China (English)
Yang Qun-Ting; Gao Tie-Gang
2008-01-01
The design of an efficient one-way hash function with good performance is a hot spot in modern cryptography researches. In this paper, a hash function construction method based on cell neural network with hyper-chaos characteristics is proposed. First, the chaos sequence is gotten by iterating cellular neural network with Runge-Kutta algorithm, and then the chaos sequence is iterated with the message. The hash code is obtained through the corresponding transform of the latter chaos sequence. Simulation and analysis demonstrate that the new method has the merit of convenience, high sensitivity to initial values, good hash performance, especially the strong stability.
Neural Network Based Feedback Linearization Control of an Unmanned Aerial Vehicle
Institute of Scientific and Technical Information of China (English)
Dan Necsulescu; Yi-Wu Jiang; Bumsoo Kim
2007-01-01
This paper presents a flight control design for an unmanned aerial vehicle (UAV) using a nonlinear autoregressive moving average (NARMA-L2) neural network based feedback linearization and output redefinition technique. The UAV investigated is nonminimum phase. The output redefinition technique is used in such a way that the resulting system to be inverted is a minimum phase system. The NARMA-L2 neural network is trained off-line for forward dynamics of the UAV model with redefined output and is then inverted to force the real output to approximately track a command input. Simulation results show that the proposed approaches have good performance.
Short-term load forecasting study of wind power based on Elman neural network
Tian, Xinran; Yu, Jing; Long, Teng; Liu, Jicheng
2017-01-01
Since wind power has intermittent, irregular and volatility nature, improving load forecasting accuracy of wind power has significant influence on controlling wind system and guarantees stable operation of power grids. This paper constructed the wind farm loading forecasting in short-term based on Elman neural network, and made a numerical example analysis. . Examples show that, using input delayed of feedback Elman neural network, can reflect the inherent laws of wind load operation better, so as to present a new idea for short-term load forecasting of wind power.
Neural-network-based speed controller for induction motors using inverse dynamics model
Ahmed, Hassanein S.; Mohamed, Kamel
2016-08-01
Artificial Neural Networks (ANNs) are excellent tools for controller design. ANNs have many advantages compared to traditional control methods. These advantages include simple architecture, training and generalization and distortion insensitivity to nonlinear approximations and nonexact input data. Induction motors have many excellent features, such as simple and rugged construction, high reliability, high robustness, low cost, minimum maintenance, high efficiency, and good self-starting capabilities. In this paper, we propose a neural-network-based inverse model for speed controllers for induction motors. Simulation results show that the ANNs have a high tracing capability.
Multi-Objective Optimization and Analysis Model of Sintering Process Based on BP Neural Network
Institute of Scientific and Technical Information of China (English)
ZHANG Jun-hong; XIE An-guo; SHEN Feng-man
2007-01-01
A multi-objective optimization and analysis model of the sintering process based on BP neural network is presented. Genetic algorithms are combined to simplify the BP neural network, which can reduce the learning time and increase the forecasting accuracy of the network model. This model has been experimented in the sintering process, and the production cost, the energy consumption, the quality (revolving intensity), and the output are considered at the same time. Moreover, the relation between some factors and the multi-objectives has been analyzed, and the results are consistent with the process. Different objectives are emphasized at different practical periods, and this can provide a theoretical basis for the manager.
Fuzzy neural network control of underwater vehicles based on desired state programming
Institute of Scientific and Technical Information of China (English)
LIANG Xiao; LI Ye; XU Yu-ru; WAN Lei; QIN Zai-bai
2006-01-01
Due to the nonlinearity and uncertainty, the precise control of underwater vehicles in some intelligent operations hasn't been solved very well yet. A novel method of control based on desired state programming was presented, which used the technique of fuzzy neural network. The structure of fuzzy neural network was constructed according to the moving characters and the back propagation algorithm was deduced. Simulation experiments were conducted on general detection remotely operated vehicle.The results show that there is a great improvement in response and precision over traditional control, and good robustness to the model's uncertainty and external disturbance, which has theoretical and practical value.
Underground object characterization based on neural networks for ground penetrating radar data
Zhang, Yu; Huston, Dryver; Xia, Tian
2016-04-01
In this paper, an object characterization method based on neural networks is developed for GPR subsurface imaging. Currently, most existing studies demonstrate detecting and imaging objects of cylindrical shapes. While in this paper, no restriction is imposed on the object shape. Three neural network algorithms are exploited to characterize different types of object signatures, including object shape, object material, object size, object depth and subsurface medium's dielectric constant. Feature extraction is performed to characterize the instantaneous amplitude and time delay of the reflection signal from the object. The characterization method is evaluated utilizing the data synthesized with the finite-difference timedomain (FDTD) simulator.
A Study of Maneuvering Control for an Air Cushion Vehicle Based on Back Propagation Neural Network
Institute of Scientific and Technical Information of China (English)
LU Jun; HUANG Guo-liang; LI Shu-zhi
2009-01-01
A back propagation (BP) neural network mathematical model was established to investigate the maneuvering control of an air cushion vehicle (ACV). The calculation was based on four-freedom-degree model experiments of hydrodynamics and aerodynamics. It is necessary for the ACV to control the velocity and the yaw rate as well as the velocity angle at the same time. The yaw rate and the velocity angle must be controlled correspondingly because of the whipping, which is a special characteristic for the ACV. The calculation results show that it is an efficient way for the ACV's maneuvering control by using a BP neural network to adjust PID parameters online.
Discrimination of neutrons and {\\gamma}-rays in liquid scintillator based on Elman neural network
Zhang, Cai-Xun; Zhao, Jian-Ling; Wang, Li; Yu, Xun-Zhen; Zhu, Jing-Jun; Xing, Hao-Yang
2015-01-01
A new neutron and {\\gamma} (n/{\\gamma}) discrimination method based on Elman Neural Network (ENN) was put forward to improve the n/{\\gamma} discrimination performance of liquid scintillator (LS). In this study, neutron and {\\gamma} data acquired from EJ-335 which was exposed in Am-Be radiation field was discriminated using ENN. The difference of n/{\\gamma} discrimination performance between using ENN and Back Propagation Neural Network (BPNN) is that ENN gave a improvement over BPNN in n/{\\gamma} discrimination with the increasing increasing of the Figure of Merit (FOM) from 0.907 to 0.953.
Self-organized Criticality in a Model Based on Neural Network
Institute of Scientific and Technical Information of China (English)
ZHAO Xiao-Wei; CHEN Tian-Lun
2001-01-01
Based on the LISSOM neural network model, we introduce a model to investigate self-organized criticality in the activity of neural populations. The influence of connection (synapse) between neurons has been adequately considered in this model. It is found to exhibit self-organized criticality (SOC) behavior under appropriate conditions.``We also find that the learning process has promotive influence on emergence of SOC behavior. In addition, we analyze the influence of various factors of the model on the SOC behavior, which is characterized by the power-law behavior of the avalanche size distribution.``
Neural Network Based Algorithm and Simulation of Information Fusion in the Coal Mine
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
The concepts of information fusion and the basic principles of neural networks are introduced.Neural networks were introduced as a way of building an information fusion model in a coal mine monitoring system.This assures the accurate transmission of the multi-sensor information that comes from the coal mine monitoring systems.The information fusion mode was analyzed.An algorithm was designed based on this analysis and some simulation results were given.Finally, conclusions that could provide auxiliary decision making information to the coal mine dispatching officers were presented.
Neural network and genetic algorithm based global path planning in a static environment
Institute of Scientific and Technical Information of China (English)
DU Xin; CHEN Hua-hua; GU Wei-kang
2005-01-01
Mobile robot global path planning in a static environment is an important problem. The paper proposes a method of global path planning based on neural network and genetic algorithm. We constructed the neural network model of environmental information in the workspace for a robot and used this model to establish the relationship between a collision avoidance path and the output of the model. Then the two-dimensional coding for the path via-points was converted to one-dimensional one and the fitness of both the collision avoidance path and the shortest distance are integrated into a fitness function. The simulation results showed that the proposed method is correct and effective.
Abbaspour, Alireza; Aboutalebi, Payam; Yen, Kang K; Sargolzaei, Arman
2017-03-01
A new online detection strategy is developed to detect faults in sensors and actuators of unmanned aerial vehicle (UAV) systems. In this design, the weighting parameters of the Neural Network (NN) are updated by using the Extended Kalman Filter (EKF). Online adaptation of these weighting parameters helps to detect abrupt, intermittent, and incipient faults accurately. We apply the proposed fault detection system to a nonlinear dynamic model of the WVU YF-22 unmanned aircraft for its evaluation. The simulation results show that the new method has better performance in comparison with conventional recurrent neural network-based fault detection strategies.
Finite-time synchronization of fractional-order memristor-based neural networks with time delays.
Velmurugan, G; Rakkiyappan, R; Cao, Jinde
2016-01-01
In this paper, we consider the problem of finite-time synchronization of a class of fractional-order memristor-based neural networks (FMNNs) with time delays and investigated it potentially. By using Laplace transform, the generalized Gronwall's inequality, Mittag-Leffler functions and linear feedback control technique, some new sufficient conditions are derived to ensure the finite-time synchronization of addressing FMNNs with fractional order α:1neural networks. Finally, three numerical examples are presented to show the effectiveness of our proposed theoretical results.
Exploring the neural bases of goal-directed motor behavior using fully resolved simulations
Patel, Namu; Patankar, Neelesh A.
2016-11-01
Undulatory swimming is an ideal problem for understanding the neural architecture for motor control and movement; a vertebrate's robust morphology and adaptive locomotive gait allows the swimmer to navigate complex environments. Simple mathematical models for neurally activated muscle contractions have been incorporated into a swimmer immersed in fluid. Muscle contractions produce bending moments which determine the swimming kinematics. The neurobiology of goal-directed locomotion is explored using fast, efficient, and fully resolved constraint-based immersed boundary simulations. Hierarchical control systems tune the strength, frequency, and duty cycle for neural activation waves to produce multifarious swimming gaits or synergies. Simulation results are used to investigate why the basal ganglia and other control systems may command a particular neural pattern to accomplish a task. Using simple neural models, the effect of proprioceptive feedback on refining the body motion is demonstrated. Lastly, the ability for a learned swimmer to successfully navigate a complex environment is tested. This work is supported by NSF CBET 1066575 and NSF CMMI 0941674.
Parallel Neural Network-Based Motion Controller for Autonomous Underwater Vehicles
Institute of Scientific and Technical Information of China (English)
GAN Yong; WANG Li-rong; WAN Lei; XU Yu-ru
2005-01-01
A parallel neural network-based controller (PNNC) is presented for the motion control of underwater vehicles in this paper. It consists of a real-time part, a self-learning part and a desired-state programmer, and it is different from normal adaptive neural network controller in structure. Owing to the introduction of the self-learning part, on-line learning can be performed without sample data in several sample periods, resulting in high learning speed of the controller and good control performance. The desired-state programmer is utilized to obtain better learning samples of the neural network to keep the stability of the controller. The developed controller is applied to the 4-degree of freedom control of the AUV "IUV-IV" and is successful on the simulation platform. The control performance is also compared with that of neural network controller with different structures such as normal adaptive neural network and different learning methods. Current effects and surge velocity control are also included to demonstrate the controller's performance. It is shown that the PNNC has a great possibility to solve the problems in the control system design of underwater vehicles.
A Universal Formula for Extracting the Euler Angles
Shuster, Malcolm D.; Markley, F. Landis
2004-01-01
Recently, the authors completed a study of the Davenport angles, which are a generalization of the Euler angles for which the initial and final Euler axes need not be either mutually parallel or mutually perpendicular or even along the coordinate axes. During the conduct of that study, those authors discovered a relationship which can be used to compute straightforwardly the Euler angles characterizing a proper-orthogonal direction-cosine matrix for an arbitrary Euler-axis set satisfying n(sub 1) x n(sub 2) = 0 and n(sub 3) x n(sub 1) = 0, which is also satisfied by the more usual Euler angles we encounter commonly in the practice of Astronautics. Rather than leave that relationship hidden in an article with very different focus from the present Engineering note, we present it and the universal algorithm derived from it for extracting the Euler angles from the direction-cosine matrix here. We also offer literal "code" for performing the operations, numerical examples, and general considerations about the extraction of Euler angles which are not universally known, particularly, the treatment of statistical error.
Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network.
Han, Hong-Gui; Zhang, Lu; Hou, Ying; Qiao, Jun-Fei
2016-02-01
A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure and parameters are adjusted concurrently in the training process. The proposed SR-RBF neural network is represented in a general nonlinear form for predicting the future dynamic behaviors of nonlinear systems. To improve the modeling accuracy, a spiking-based growing and pruning algorithm and an adaptive learning algorithm are developed to tune the structure and parameters of the SR-RBF neural network, respectively. Meanwhile, for the control problem, an improved gradient method is utilized for the solution of the optimization problem in NMPC. The stability of the resulting control system is proved based on the Lyapunov stability theory. Finally, the proposed SR-RBF neural network-based NMPC (SR-RBF-NMPC) is used to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). Comparisons with other existing methods demonstrate that the SR-RBF-NMPC can achieve a considerably better model fitting for WWTP and a better control performance for DO concentration.
Congestion Control for ATM Networks Based on Diagonal Recurrent Neural Networks
Institute of Scientific and Technical Information of China (English)
HuangYunxian; YanWei
1997-01-01
An adaptive control model and its algorithms based on simple diagonal recurrent neural networks are presented for the dynamic congestion control in broadband ATM networks.Two simple dynamic queuing models of real networks are used to test the performance of the suggested control scheme.
Identification of children's activity type with accelerometer-based neural networks
Vries, S.I. de; Engels, M.; Garre, F.G.
2011-01-01
Purpose: The study's purpose was to identify children's physical activity type using artificial neural network (ANN) models based on uniaxial or triaxial accelerometer data from the hip or the ankle. Methods: Fifty-eight children (31 boys and 27 girls, age range = 9-12 yr) performed the following ac
Petkov, Nikolay
1995-01-01
A preprocessor based on a computational model of simple cells in the mammalian primary visual cortex is combined with a self-organising artificial neural network classifier. After learning with a sequence of input images, the output units of the system turn out to correspond to classes of input imag
A New Method for Studying the Periodic System Based on a Kohonen Neural Network
Chen, David Zhekai
2010-01-01
A new method for studying the periodic system is described based on the combination of a Kohonen neural network and a set of chemical and physical properties. The classification results are directly shown in a two-dimensional map and easy to interpret. This is one of the major advantages of this approach over other methods reported in the…
Institute of Scientific and Technical Information of China (English)
曾宪钊; 成冀; 安欣; 方礼明
2002-01-01
This paper introduces a new Air Combat Intelligence Simulation System (ACISS) in a 32 versus 32 air combat, describes three methods: Genetic Algorithms (GA) in the multi-targeting decision and Evading Missile Rule Base learning, Neural Networks (NN) in the maneuvering decision, and Time Effectiveness Algorithm (TEA) in the adjudicating an air combat and the evaluating evading missile effectiveness.
Parks, Lauren K.; Hill, Dina E.; Thoma, Robert J.; Euler, Matthew J.; Lewine, Jeffrey D.; Yeo, Ronald A.
2009-01-01
Although many studies have compared the brains of normal controls and individuals with autism, especially older, higher-functioning individuals with autism, little is known of the neural correlates of the vast clinical heterogeneity characteristic of the disorder. In this study, we used voxel-based morphometry (VBM) to examine gray matter…
Kwon, Chung-Jin; Kim, Sung-Joong; Han, Woo-Young; Min, Won-Kyoung
2005-12-01
The rotor position and speed estimation of permanent-magnet synchronous motor(PMSM) was dealt with. By measuring the phase voltages and currents of the PMSM drive, two diagonally recurrent neural network(DRNN) based observers, a neural current observer and a neural velocity observer were developed. DRNN which has self-feedback of the hidden neurons ensures that the outputs of DRNN contain the whole past information of the system even if the inputs of DRNN are only the present states and inputs of the system. Thus the structure of DRNN may be simpler than that of feedforward and fully recurrent neural networks. If the backpropagation method was used for the training of the DRNN the problem of slow convergence arise. In order to reduce this problem, recursive prediction error(RPE) based learning method for the DRNN was presented. The simulation results show that the proposed approach gives a good estimation of rotor speed and position, and RPE based training has requires a shorter computation time compared to backpropagation based training.
Direction-of-change forecasting using a volatility-based recurrent neural network
Bekiros, S.D.; Georgoutsos, D.A.
2008-01-01
This paper investigates the profitability of a trading strategy, based on recurrent neural networks, that attempts to predict the direction-of-change of the market in the case of the NASDAQ composite index. The sample extends over the period 8 February 1971 to 7 April 1998, while the sub-period 8 Ap
Energy Technology Data Exchange (ETDEWEB)
Djukanovic, M. (Institut Nikola Tesla, Belgrade (Yugoslavia)); Sobajic, D.J.; Yohhan Pao (Case Western Reserve Univ., Cleveland, OH (United States))
1991-10-01
The identification of the mode of instability plays an essential role in generating principal energy boundary hypersurfaces. We present a new method for unstable machine identification based on the use of supervised learning neural-net technology, and the adaptive pattern recognition concept. It is shown that using individual energy functions as pattern features, appropriately trained neural-nets can retrieve the reliable characterization of the transient process including critical clearing time parameter, mode of instability and energy margins. Generalization capabilities of the neural-net processing allow for these assessments to be made independently of load levels. The results obtained from computer simulations are presented using the New England power system, as an example. (author).
Peng, Zhouhua; Wang, Dan; Wang, Jun
2016-06-22
This paper presents a predictor-based neural dynamic surface control (PNDSC) design method for a class of uncertain nonlinear systems in a strict-feedback form. In contrast to existing NDSC approaches where the tracking errors are commonly used to update neural network weights, a predictor is proposed for every subsystem, and the prediction errors are employed to update the neural adaptation laws. The proposed scheme enables smooth and fast identification of system dynamics without incurring high-frequency oscillations, which are unavoidable using classical NDSC methods. Furthermore, the result is extended to the PNDSC with observer feedback, and its robustness against measurement noise is analyzed. Numerical and experimental results are given to demonstrate the efficacy of the proposed PNDSC architecture.
Intelligent predicting approach of peritoneal fluid absorption rate based-on neural network
Institute of Scientific and Technical Information of China (English)
Mei ZHANG; Yueming HU; Tao WANG
2003-01-01
This paper addresses the important intelligent predicting problem of peritoneal absorption rate in the peritoneal dialysis treament process of renal failure. As the index of dialysis adequacy, KT/V and Ccr are widely used and accepted. However,growing evidence suggests that the fluid balance may play a critical role in dialysis adequacy and patient outcome. Peritoneal fluid absorption decreases the peritoneal fluid removal. Understanding the peritoneal fluid absorption rate will help clinicians to opthnize the dialysis dwell time. The neural network approach is applied to the prediction of peritoneal absorption rate. Compared with multivariable regression method, the experimental results showed that neural network method has an advantage over multivariable regression. The application of this predicting method based-on neural network in clinic is instructive.
RBF neural network based $\\mathcal{H}_{\\infty}$ synchronization for unknown chaotic systems
Indian Academy of Sciences (India)
Choon Ki Ahn
2010-08-01
In this paper, we propose a new $\\mathcal{H}_{\\infty}$ synchronization strategy, called a Radial Basis Function Neural Network $\\mathcal{H}_{\\infty}$ synchronization (RBFNNHS) strategy, for unknown chaotic systems in the presence of external disturbance. In the proposed framework, a radial basis function neural network (RBFNN) is constructed as an alternative to approximate the unknown nonlinear function of the chaotic system. Based on this neural network and linear matrix inequality (LMI) formulation, the RBFNNHS controller and the learning laws are presented to reduce the effect of disturbance to an $\\mathcal{H}_{\\infty}$ norm constraint. It is shown that ﬁnding the RBFNNHS controller and the learning laws can be transformed into the LMI problem and solved using the convex optimization method. A numerical example is presented to demonstrate the validity of the proposed RBFNNHS scheme.
Fabric Defect Detection Technique Based on Two-double Neural Network
Institute of Scientific and Technical Information of China (English)
XIE Chun-ping; XU Bo-jun; CHEN Jun-jie
2008-01-01
This paper introduces the identification of the defects on the fabric by using two-double neural network and wavelet analysis. The purpose is to fit for the automatic cloth inspection system and to avoid the disadvantages of traditional human inspection. Firstly, training the normal fabric to acquire its characteristics and then using the BP neural network to tell the normal fabric apart from the one with defects. Secondly, doing the two-dimensional discrete wavelet transformation based on the image of the defects, then wiping off the proper characteristics of the fabric, and identifying the defects utilizing the trained BP neural network. It is proved that this method is of high speed and accuracy. It comes up to the requirement of automatic cloth inspection.