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definitions and neural mechanisms

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Báo cáo hóa học: " Computer simulations of neural mechanisms explaining upper and lower limb excitatory neural coupling Huang and Ferris" docx

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... insight into the neural mechanisms involved in excitatory interlimb coupling and could help design future experiments to better understand the neural mechanisms of excitatory neural coupling Acknowledgements ... Cite this article as: Huang and Ferris: Computer simulations of neural mechanisms explaining upper and lower limb excitatory neural coupling Journal of NeuroEngineering and Rehabilitation 2010 7:59 ... simulations of neural mechanisms explaining upper and lower limb excitatory neural coupling Helen J Huang1*, Daniel P Ferris1,2,3 Abstract Background: When humans perform rhythmic upper and lower...
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Báo cáo hóa học:

Báo cáo hóa học: " Research Article Neural Mechanisms of Motion Detection, Integration, and Segregation: From Biology to Artificial Image Processing Systems" docx

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... the membrane constant, gex and gin denote time-varying and input dependent membrane conductances (separate for excitatory and inhibitory synapses, resp.), and Eex and Ein denote saturation points ... linear property and saturates for increased steady input 2.2 Cascade Architecture and Description of Generic Cortical Processing Stages Our modelling of neural mechanisms (functionality) and their ... frame, processing two random dot kinematograms (the sequence shows 60 moving dots and consists of 60 frames with 40 × 40 px/frame) Random dots are initialized at random positions and a horizontal velocity...
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Employee Turnover: Definitions and Calculations

Employee Turnover: Definitions and Calculations

Kỹ năng quản lý

... range of alternative definitions of employee turnover can prove problematic for those with responsibility in this area Appreciating the subtle differences between similar sounding definitions helps ... at the beginning of a period, and remain with the company at the end of the period This figure can be useful but it hides the departures of employees that joined and subsequently left during the ... improvement over the total turnover definition; retirees and employees dismissed or made redundant no longer included This definition is more precise and more relevant to internal decision-making If...
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Automatic text extraction using DWT and Neural Network

Automatic text extraction using DWT and Neural Network

Kỹ thuật lập trình

... sequences using DWT and neural network DWT decomposes one original image into four sub-bands The transformed image includes one average component sub-band and three detail component sub-bands Each detail ... sub-bands in Figure In next subsection, a neural network is employed to learn the features of candidate text regions obtained from those detail component sub-bands Finally, the well trained neural ... operation and the final resulted 2-D Haar DWT is shown in Figure 3(c) 2-D Haar DWT decomposes a gray-level image into one average component sub-band and three detail component sub-bands From...
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Tài liệu Kalman Filtering and Neural Networks - Chapter 1: KALMAN FILTERS doc

Tài liệu Kalman Filtering and Neural Networks - Chapter 1: KALMAN FILTERS doc

Hóa học - Dầu khí

... Wiley, 1986 [3] M.S Grewal and A.P Andrews, Kalman Filtering: Theory and Practice Englewood Cliffs, NJ: Prentice-Hall, 1993 [4] H.L Van Trees, Detection, Estimation, and Modulation Theory, Part ... where yk is the observable at time k and Hk is the measurement matrix The measurement noise vk is assumed to be additive, white, and Gaussian, with zero mean and with covariance matrix defined by ... scalar random variables; generalization of the theory to vector random variables is a straightforward matter Suppose we are given the observable yk ¼ xk þ vk ; where xk is an unknown signal and vk...
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Tài liệu Kalman Filtering and Neural Networks P2 doc

Tài liệu Kalman Filtering and Neural Networks P2 doc

Điện - Điện tử

... speed, mapping accuracy, generalization, and overall performance relative to standard backpropagation and related methods Amongst the most promising and enduring of enhanced training methods ... is also maintained and evolved The global EKF (GEKF) training algorithm was introduced by Singhal and Wu [2] in the late 1980s, and has served as the basis for the development and enhancement of ... computationally effective neural network training methods that has enabled the application of feedforward and recurrent neural networks to problems in control, signal processing, and pattern recognition...
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Tài liệu Kalman Filtering and Neural Networks P3 doc

Tài liệu Kalman Filtering and Neural Networks P3 doc

Điện - Điện tử

...  circle moving right and up; square moving right and down; triangle moving right and up; circle moving right and down; square moving right and up; triangle moving right and down Training was ... Cortex, 1, 1–47 (1991) [2] J.S Lund, Q Wu and J.B Levitt, ‘‘Visual cortex cell types and connections’’, in M.A Arbib, Ed., Handbook of Brain Theory and Neural Networks, Cambridge, MA: MIT Press, ... Rao and Ballard [10] have proposed an alternative neural network implementation of the EKF that employs topdown feedback between layers, and have applied their model to both static images and...
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Tài liệu Kalman Filtering and Neural Networks P4 doc

Tài liệu Kalman Filtering and Neural Networks P4 doc

Điện - Điện tử

... in D.A Rand and L.S Young, Eds Dynamical Systems and Turbulence, Warwick 1980, Lecture Notes in Mathematics Vol 898 1981, p 230 Berlin: Springer-Verlag [6] A.M Fraser, ‘‘Information and entropy ... x2 ðk þ 1Þ ¼ 1:0 þ mfx1 ðkÞ þ x2 ðkÞ cos½mðkފg; ð4:6Þ where x1 and x2 are the real and imaginary components, respectively, of x and the parameter m is carefully chosen to be 0.7 so that the produced ... Note that A ¼ initialization and B ¼ one-step phase evaluation, the correlation dimension, Lyapunov exponents and Kolmogorov entropy of both the actual Ikeda series and the autonomously generated...
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Tài liệu Kalman Filtering and Neural Networks P5 pdf

Tài liệu Kalman Filtering and Neural Networks P5 pdf

Điện - Điện tử

... @x xk ^ D ð5:9Þ and where Rv and Rn are the covariances of vk and nk , respectively 5.2.2 EKF–Weight Estimation As proposed initially in [30], and further developed in [31] and [32], the EKF ... ð5:63Þ ^ where xkjN and pkjN are defined as the conditional mean and variance of xk ^ ^ kjN given w and all the data, fyk gN The terms xÀ and pÀ are the conditional kjN mean and variance of xÀ ... Atlas, ‘‘Recurrent neural networks and robust time series prediction,’’ IEEE Transactions on Neural Networks, 5(2), 240–254 (1994) [15] S.C Stubberud and M Owen, ‘‘Artificial neural network feedback...
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Tài liệu Kalman Filtering and Neural Networks P6 pdf

Tài liệu Kalman Filtering and Neural Networks P6 pdf

Điện - Điện tử

... of f and g and the noise covariances Given observations of the (no longer hidden) states and outputs, f and g can be obtained as the solution to a possibly nonlinear regression problem, and the ... matrices A and B multiplying inputs x and u, respectively; and an output bias vector b, and the noise covariance Q Each RBF is assumed to be a Gaussian in x space, with center ci and width given ... admit exact and efficient inference (Here, and in what follows, we call a system linear if both the state evolution function and the state-to-output observation function are linear, and nonlinear...
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Tài liệu Kalman Filtering and Neural Networks P7 pptx

Tài liệu Kalman Filtering and Neural Networks P7 pptx

Điện - Điện tử

... learning the parameters The use of the EKF for training neural networks has been developed by Singhal and Wu [8] and Puskorious and Feldkamp [9], and is covered in Chapter of this book The use of the ... ¼ Ck ¼  @x  @n D ^ xk ð7:29Þ  n and where Rv and Rn are the covariances of vk and nk , respectively The noise means are denoted by n ¼ E½nŠ and v ¼ E½vŠ, and are usually assumed to equal zero ... filtering (CDF) techniques developed separately by Ito and Xiong [12] and Nørgaard, Poulsen, and Ravn [13] In [7] van der Merwe and Wan show how the UKF and CDF can be unified in a general family of derivativefree...
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Tài liệu Kalman Filtering and Neural Networks - Chapter 4: CHAOTIC DYNAMICS pdf

Tài liệu Kalman Filtering and Neural Networks - Chapter 4: CHAOTIC DYNAMICS pdf

Hóa học - Dầu khí

... in D.A Rand and L.S Young, Eds Dynamical Systems and Turbulence, Warwick 1980, Lecture Notes in Mathematics Vol 898 1981, p 230 Berlin: Springer-Verlag [6] A.M Fraser, ‘‘Information and entropy ... x2 ðk þ 1Þ ¼ 1:0 þ mfx1 ðkÞ þ x2 ðkÞ cos½mðkފg; ð4:6Þ where x1 and x2 are the real and imaginary components, respectively, of x and the parameter m is carefully chosen to be 0.7 so that the produced ... Note that A ¼ initialization and B ¼ one-step phase evaluation, the correlation dimension, Lyapunov exponents and Kolmogorov entropy of both the actual Ikeda series and the autonomously generated...
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Tài liệu Kalman Filtering and Neural Networks - Chapter 5: DUAL EXTENDED KALMAN FILTER METHODS docx

Tài liệu Kalman Filtering and Neural Networks - Chapter 5: DUAL EXTENDED KALMAN FILTER METHODS docx

Hóa học - Dầu khí

... @x xk ^ D ð5:9Þ and where Rv and Rn are the covariances of vk and nk , respectively 5.2.2 EKF–Weight Estimation As proposed initially in [30], and further developed in [31] and [32], the EKF ... ð5:63Þ ^ where xkjN and pkjN are defined as the conditional mean and variance of xk ^ ^ kjN given w and all the data, fyk gN The terms xÀ and pÀ are the conditional kjN mean and variance of xÀ ... Atlas, ‘‘Recurrent neural networks and robust time series prediction,’’ IEEE Transactions on Neural Networks, 5(2), 240–254 (1994) [15] S.C Stubberud and M Owen, ‘‘Artificial neural network feedback...
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Tài liệu Kalman Filtering and Neural Networks - Chapter 6: LEARNING NONLINEAR DYNAMICAL SYSTEMS USING THE EXPECTATION– MAXIMIZATION ALGORITHM doc

Tài liệu Kalman Filtering and Neural Networks - Chapter 6: LEARNING NONLINEAR DYNAMICAL SYSTEMS USING THE EXPECTATION– MAXIMIZATION ALGORITHM doc

Hóa học - Dầu khí

... of f and g and the noise covariances Given observations of the (no longer hidden) states and outputs, f and g can be obtained as the solution to a possibly nonlinear regression problem, and the ... matrices A and B multiplying inputs x and u, respectively; and an output bias vector b, and the noise covariance Q Each RBF is assumed to be a Gaussian in x space, with center ci and width given ... admit exact and efficient inference (Here, and in what follows, we call a system linear if both the state evolution function and the state-to-output observation function are linear, and nonlinear...
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Tài liệu Kalman Filtering and Neural Networks - Chapter VII: THE UNSCENTED KALMAN FILTER pdf

Tài liệu Kalman Filtering and Neural Networks - Chapter VII: THE UNSCENTED KALMAN FILTER pdf

Hóa học - Dầu khí

... learning the parameters The use of the EKF for training neural networks has been developed by Singhal and Wu [8] and Puskorious and Feldkamp [9], and is covered in Chapter of this book The use of the ... ¼ Ck ¼  @x  @n D ^ xk ð7:29Þ  n and where Rv and Rn are the covariances of vk and nk , respectively The noise means are denoted by n ¼ E½nŠ and v ¼ E½vŠ, and are usually assumed to equal zero ... filtering (CDF) techniques developed separately by Ito and Xiong [12] and Nørgaard, Poulsen, and Ravn [13] In [7] van der Merwe and Wan show how the UKF and CDF can be unified in a general family of derivativefree...
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Tài liệu Kalman Filtering and Neural Networks - Contents pptx

Tài liệu Kalman Filtering and Neural Networks - Contents pptx

Hóa học - Dầu khí

... Cherkassky and Mulier = LEARNING FROM DATA: Concepts, Theory, and Methods Diamantaras and Kung = PRINCIPAL COMPONENT NEURAL NETWORKS: Theory and Applications Haykin = KALMAN FILTERING AND NEURAL ... Sanchez-Pena and Sznaler = ROBUST SYSTEMS THEORY AND ´ ˜ APPLICATIONS Sandberg, Lo, Fancourt, Principe, Katagiri, and Haykin = NONLINEAR DYNAMICAL SYSTEMS: Feedforward Neural Network Perspectives ´ Tao and ... Kristic, Kanellakopoulos, and Kokotovic = NONLINEAR AND ADAPTIVE CONTROL DESIGN Nikias and Shao = SIGNAL PROCESSING WITH ALPHA-STABLE DISTRIBUTIONS AND APPLICATIONS Passino and Burgess = STABILITY...
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