A general class of tensegrity structures, consisting of both compression members, that is, bars, and tensile members, that is, cables, is defined. For a given number N of bars, we define the topological structure that...
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A general class of tensegrity structures, consisting of both compression members, that is, bars, and tensile members, that is, cables, is defined. For a given number N of bars, we define the topological structure that is necessary to establish a tensegrity. Necessary and sufficient conditions for prestress mechanical equilibria of the tensegrity are then provided in terms of a nonlinear function of the position and orientation of the bars and the rest lengths of the cables.
The UDUT - U and D are respectively the upper triangular and diagonal matrices - decomposition of the generalized inertia matrix of an n-link serial manipulator, introduced elsewhere, is used here for the simulation o...
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The UDUT - U and D are respectively the upper triangular and diagonal matrices - decomposition of the generalized inertia matrix of an n-link serial manipulator, introduced elsewhere, is used here for the simulation of industrial manipulators which are mainly of serial type. The decomposition is based on the application of the Gaussian elimination rules to the recursive expressions of the elements of the inertia matrix that are obtained using the Decoupled Natural Orthogonal Complement matrices. The decomposition resulted in an efficient order n, i.e., O(n), recursive forward dynamics algorithm that calculates the joint accelerations. These accelerations are then integrated numerically to perform simulation. Using this methodology, a computer algorithm for the simulation of any n degrees of freedom (DOF) industrial manipulator comprising of revolute and/or prismatic joints is developed. As illustrations, simulation results of three manipulators, namely, a three-DOF planar manipulator, and the six-DOF Stanford arm and PUMA robot, are reported in this paper.
A computationally efficient method is proposed for computing the simplest normal forms of vector fields. A simple, explicit recursive formula is obtained for general differential equations. The most important feature ...
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A computationally efficient method is proposed for computing the simplest normal forms of vector fields. A simple, explicit recursive formula is obtained for general differential equations. The most important feature of the approach is to obtain the "simplest" formula which reduces the computation demand to minimum. At each order of the normal form computation, the formula generates a set of algebraic equations for computing the normal form and nonlinear transformation. Moreover, the new recursive method is not required for solving large matrix equations, instead it solves linear algebraic equations one by one. Thus the new method is computationally efficient. In addition, unlike the conventional normal form theory which uses separate nonlinear transformations at each order, this approach uses a consistent nonlinear transformation through all order computations. This enables one to obtain a convenient, one step transformation between the original system and the simplest normal form. The new method can treat general differential equations which are not necessarily assumed in a conventional normal form. The method is applied to consider Hopf and Bogdanov-Takens singularities, with examples to show the computation efficiency. Maple programs have been developed to provide an "automatic" procedure for applications.
Error sensitivity measure is normally a commonly used factor for se-arching the optimal structure of a neural network. Starting with the derivation of a recursive equation for the update of a reduced order parametric ...
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ISBN:
(纸本)0780378652
Error sensitivity measure is normally a commonly used factor for se-arching the optimal structure of a neural network. Starting with the derivation of a recursive equation for the update of a reduced order parametric vector based on the full order parametric vector, the error sensitivity measure for use in linear regressor and RBF network pruning is re-derived and an approximated error sensitivity measure identical to that of proposed in optimal brain damage has been obtained. Considering the training is accomplished by recursive least square method, an on-line training-pruning algorithm is proposed.
A recursive algorithm is derived for subspace system identification which is flexible and applicable to continuous/discrete-time, time-invariant/varying stochastic systems. Efficacy of the algorithm is demonstrated by...
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A recursive algorithm is derived for subspace system identification which is flexible and applicable to continuous/discrete-time, time-invariant/varying stochastic systems. Efficacy of the algorithm is demonstrated by simulations.
This work develops stochastic optimization algorithms for a class of stock liquidation problems. The stock liquidation rules are based on hybrid geometric Brownian motion models allowing regime changes that are modula...
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This work develops stochastic optimization algorithms for a class of stock liquidation problems. The stock liquidation rules are based on hybrid geometric Brownian motion models allowing regime changes that are modulated by a continuous-time finite-state Markov chain. The optimal selling policy is of threshold type and can be obtained by solving a set of two-point boundary value problems. The total number of equations to be solved is the same as the number of states of the underlying Markov chain. To reduce the computational burden, using a stochastic optimization approach, recursive algorithms are constructed to approximate the optimal threshold values. Convergence and rates of convergence of the algorithm are studied. Simulation examples are presented, and the computation results are compared with the analytic solutions. Finally, the algorithms are tested using real market data.
The Weyl-Horn theorem characterizes a relationship between the eigenvalues and the singular values of an arbitrary matrix. Based on that characterization, a fast recursive algorithm is developed to construct numerical...
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The Weyl-Horn theorem characterizes a relationship between the eigenvalues and the singular values of an arbitrary matrix. Based on that characterization, a fast recursive algorithm is developed to construct numerically a matrix with prescribed eigenvalues and singular values. Besides being of theoretical interest, the technique could be employed to create test matrices with desired spectral features. Numerical experiment shows this algorithm to be quite efficient and robust.
作者：
Eidelman, YTel Aviv Univ
Raymond & Beverly Sackler Fac Exact Sci Sch Math Sci IL-69978 Tel Aviv Israel
In this paper, we continue the study of a class of structured matrices which may be treated as a generalization of the class of Hessenberg matrices. For this cl;class fast recursive algorithms for solution of the corr...
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In this paper, we continue the study of a class of structured matrices which may be treated as a generalization of the class of Hessenberg matrices. For this cl;class fast recursive algorithms for solution of the corresponding linear systems are obtained. The implementation of algorithms is illustrated by numerical experiments. (C) 2000 Elsevier Science Ltd. All rights reserved.
recursive algorithms have been found very effective for realization using software and very large scale integrated circuit (VLSI) techniques. Recently, some recursive algorithms have been proposed for the realization ...
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recursive algorithms have been found very effective for realization using software and very large scale integrated circuit (VLSI) techniques. Recently, some recursive algorithms have been proposed for the realization of the inverse discrete cosine transform (IDCT). In this paper, an efficient recursive algorithm for the IDCT with arbitrary length is presented. By using some appropriate iterative techniques, the formulation of the IDCT can be implemented effectively using recursive equations, and the hardware complexity is further reduced as compared with the approaches in the literature.
This paper presents a novel fully recursive method, a direct differentiation based approach, which facilitates first-order sensitivity analysis in optimal design problems involving multibody dynamic systems. A state s...
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This paper presents a novel fully recursive method, a direct differentiation based approach, which facilitates first-order sensitivity analysis in optimal design problems involving multibody dynamic systems. A state space O(n) dynamic analysis algorithm based on a velocity space projection method, as promoted by Kane [18], forms the foundation of the underlying formulation. This algorithm can significantly reduce the massive number of mathematical and associated computational operations involved in explicitly generating and solving the sensitivity equations. This benefit is particularly evident for systems involving a combination of many state variables and design parameters. The development presented in this paper focuses on chain systems to illustrate the recursive nature of the algorithm. The computational efficiency and solution accuracy of the presented algorithm are investigated through the procedures application to the simulation and design sensitivity determination of spatial chain systems involving 2, 4, 6, ..., 24 degrees of freedom, as well as a simple planar double pendulum.
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