Results 1  10
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59
A ComponentBased Approach to Modeling and Simulating MixedSignal and Hybrid Systems
 ACM Trans. on Modeling and Computer Simulation, special
, 2003
"... Systems with both continuous and discrete behaviors can be modeled using a mixedsignal style or a hybrid systems style. This paper presents a componentbased modeling and simulation framework that supports both modeling styles. The component framework, based on an actor metamodel, takes a hierarch ..."
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Cited by 20 (9 self)
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Systems with both continuous and discrete behaviors can be modeled using a mixedsignal style or a hybrid systems style. This paper presents a componentbased modeling and simulation framework that supports both modeling styles. The component framework, based on an actor metamodel, takes a hierarchical approach to manage heterogeneity in modeling complex systems. We describe how ordinary differential equations, discreteevent systems, and finite state machines can be built under this metamodel. A mixedsignal system is a hierarchical composition of continuoustime and discreteevent models, and a hybrid system is a hierarchical composition of continuoustime and finitestatemachine models. Hierarchical composition and information hiding help building clean models and efficient execution engines. Simulation technologies, in particular, the interaction between a continuoustime ODE solving engine and various discrete simulation engines are discussed. A signal type system is introduced to schedule hybrid components inside a continuoustime environment. Breakpoints are used to control the numerical integration step sizes so that discrete events are handled properly. A "refiring" mechanism and a "rollback" mechanism are designed to manage continuous components inside a discreteevent environment. The technologies are implemented in the Ptolemy II software environment. Examples are given to show the applications of this framework in mixedsignal and hybrid systems.
Toward the formal foundation of ant programming
 in Ant Algorithms – Proceedings of ANTS 2002 – Third International Workshop, ser. LNCS, M. Dorigo et al., Eds
, 2002
"... Abstract. This paper develops the formal framework of ant programming with the goal of gaining a deeper understanding on ant colony optimization and, more in general, on the principles underlying the use of an iterated Monte Carlo approach for the multistage solution of combinatorial optimization p ..."
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Cited by 14 (2 self)
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Abstract. This paper develops the formal framework of ant programming with the goal of gaining a deeper understanding on ant colony optimization and, more in general, on the principles underlying the use of an iterated Monte Carlo approach for the multistage solution of combinatorial optimization problems. Ant programming searches for the optimal policy of a multistage decision problem to which the original combinatorial problem is reduced. In order to describe ant programming we adopt on the one hand concepts of optimal control, and on the other hand the ant metaphor suggested by ant colony optimization. In this context, a critical analysis is given of notions such as state, representation, and sequential decision process under incomplete information. 1
Approximate Identification and Control Design  with application to a mechanical system
, 1992
"... ..."
For a Formal Foundation of the Ant Programming Approach to Combinatorial Optimization  Part 1: The problem, the representation, and the general solution strategy
 ATR Human Information Processing Research Laboratories
, 2000
"... This paper develops the formal framework of ant programming with the goal of gaining a deeper understanding on ant colony optimization, a heuristic method for combinatorial optimization problems inspired by the foraging behavior of ants. Indeed, ant programming allows a deeper insight into the gener ..."
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Cited by 5 (2 self)
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This paper develops the formal framework of ant programming with the goal of gaining a deeper understanding on ant colony optimization, a heuristic method for combinatorial optimization problems inspired by the foraging behavior of ants. Indeed, ant programming allows a deeper insight into the general principles underlying the use of an iterated Monte Carlo approach for the multistage solution of a combinatorial optimization problem.
BROCKETT’S PROBLEM IN THE THEORY OF STABILITY OF LINEAR DIFFERENTIAL EQUATIONS
"... Abstract. Algorithms for nonstationary linear stabilization are constructed. Combined with a nonstabilizabiity criterion, these algorithms result in the solution of the Brockett problem in a number of cases. In the book [1], R. Brockett formulated the following problem. For a triplet of matrices A, ..."
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Cited by 4 (0 self)
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Abstract. Algorithms for nonstationary linear stabilization are constructed. Combined with a nonstabilizabiity criterion, these algorithms result in the solution of the Brockett problem in a number of cases. In the book [1], R. Brockett formulated the following problem. For a triplet of matrices A, B, and C, what conditions ensure the existence of a matrix K(t) such that the system (1)
Invariant Subspace Methods in Linear MultivariableDistributed Systems and LumpedDistributed Network Synthesis
, 1976
"... Linear multivariabledistributed systems and synthesis problems for lumpeddistributed networks are analyzed. The methgods used center around the invariant subspace theory of HelsonLax and the theory of vectorial Hardy functions. Statespace and transfer function models are studied and their relati ..."
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Cited by 4 (0 self)
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Linear multivariabledistributed systems and synthesis problems for lumpeddistributed networks are analyzed. The methgods used center around the invariant subspace theory of HelsonLax and the theory of vectorial Hardy functions. Statespace and transfer function models are studied and their relations analyzed. We single out a class of systems and networks with nonrational transfer functions (scattering matrices), for which several of the wellkbown results for lumped systems and networks are generalized. In particular we develop the relations between singularities of transfer functions and "natural modes" of the systems, a degree theory for infinitedimensional linear systems and a synthesis via lossless embedding of the scattering matrix. Finally coprime factorizations for this class of systems are developed.
Techniques of Identification with the Stochastic Computer
 in "Proc. International Federation of Automatic Control Symposium on Identification, Progue
, 1967
"... Summary A major obstacle to the practical application of advanced techniques for process identification is lack of suitable hardware. By its very nature an identification computer must be able to store and adjust large numbers of variable parameters, and to use these for purposes of prediction and c ..."
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Cited by 3 (3 self)
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Summary A major obstacle to the practical application of advanced techniques for process identification is lack of suitable hardware. By its very nature an identification computer must be able to store and adjust large numbers of variable parameters, and to use these for purposes of prediction and control. Conventional analog and digital computers both have disadvantages in this application, and there is a need for hardware specifically designed for identification purposes, economical in cost, reliable and driftfree like the digital computer, and capable of parallel operation to achieve the sizeindependent bandwidth of the analog computer. In particular this hardware should take full advantage of the advanced state of integrated circuit technology. This paper outlines the principles and structure of the Stochastic Computer, and describes three identification techniques of increasing generality which take especial advantage of novel features in stochastic computing elements. The first technique is one of steepest descent to the best linear relationship between the inputs and outputs of the process to be identified—three conventional techniques, including polaritycoincidence correlation, are compared with three stochastic techniques. The second technique is one of statistical decision theory, in which Bayes Theorem is used to invert conditional probabilities and bring them to a form suitable for estimation and prediction. The final technique is one of Markov modelling of the stateclass transitions of the process. In conclusion it is suggested that advances in integrated circuit technology mean that feasible control practice will, in a few years time, have advanced beyond control theory—at least as it stands at present.
Generalizations for the eigenvalue and pole concept with respect to linear timevarying systems
 Proc. ProRisc/IEEE Workshop CSSP, Mierlo, the
, 1997
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Expected Convergence Properties of BGP
"... Border Gateway Protocol (BGP) is the de facto standard used for interdomain routing. Since packet forwarding may not be possible until stable routes are learned, it is not only critical for BGP to converge but it is important that the convergence be rapid. The distributed and asynchronous nature of ..."
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Cited by 3 (0 self)
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Border Gateway Protocol (BGP) is the de facto standard used for interdomain routing. Since packet forwarding may not be possible until stable routes are learned, it is not only critical for BGP to converge but it is important that the convergence be rapid. The distributed and asynchronous nature of BGP in conjunction with local policies makes it difficult to analyze with respect to convergence behavior. We present a novel model which, to our knowledge, is the first one to permit analysis of convergence in the aggregate (i.e., over all message exchange orders between routers regarding route advertisements), rather than worst case behavior. We introduce the notion of probabilistic safety as requiring the probability of convergence to be 1. We provide a necessary and sufficient condition characterizing probabilistic safety that shows that probabilistic safety accommodates BGP configurations whose potential divergence stems solely from pathological message sequences. More generally, we show how to compute for any BGP configuration its probability of convergence. For probabilistically safe configurations, we present procedures for computing their expected time to converge as well as the probability distribution on their convergence times. The ability to compute these quantitative characteristics makes our work “constructive ” and provides the basis for further understanding and deriving procedures for optimizing network characteristics. Finally, we simulate several network examples and verify the consistency between our analysis and the simulations. 1
Initial Conditions, Generalized Functions, and the Laplace Transform Troubles at the origin
"... Version 5.5 The unilateral Laplace transform is widely used to analyze signals, linear models, and control systems, and is consequently taught to most engineering undergraduates. In our courses at MIT in the departments of electrical engineering and computer science, mathematics, and mechanical engi ..."
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Version 5.5 The unilateral Laplace transform is widely used to analyze signals, linear models, and control systems, and is consequently taught to most engineering undergraduates. In our courses at MIT in the departments of electrical engineering and computer science, mathematics, and mechanical engineering, we have found some significant pitfalls associated with teaching our students to understand and apply the Laplace transform. We have independently concluded that one reason students find the Laplace transform difficult is that there are significant confusions present in many of the standard textbook presentations of this subject, in all three of our disciplines. A key issue is the treatment of the origin. Many texts [1]–[5] define the Laplace transform of a time function f(t) as L{f(t)} = f(t)e −st dt