47,574 research outputs found
Lyapunov Exponents in Random Boolean Networks
A new order parameter approximation to Random Boolean Networks (RBN) is
introduced, based on the concept of Boolean derivative. A statistical argument
involving an annealed approximation is used, allowing to measure the order
parameter in terms of the statistical properties of a random matrix. Using the
same formalism, a Lyapunov exponent is calculated, allowing to provide the
onset of damage spreading through the network and how sensitive it is to
minimal perturbations. Finally, the Lyapunov exponents are obtained by means of
different approximations: through distance method and a discrete variant of the
Wolf's method for continuous systems.Comment: 16 pages, 5 eps-figures included, article submitted to Physica
Efficient Instantiation of Parameterised Boolean Equation Systems to Parity Games
Parameterised Boolean Equation Systems (PBESs) are sequences of Boolean fixed point equations with data variables, used for, e.g., verification of modal μ-calculus formulae for process algebraic specifications with data. Solving a PBES is usually done by instantiation to a Parity Game and then solving the game. Practical game solvers exist, but the instantiation step is the bottleneck. We enhance the instantiation in two steps. First, we transform the PBES to a Parameterised Parity Game (PPG), a PBES with each equation either conjunctive or disjunctive. Then we use LTSmin, that offers transition caching, efficient storage of states and both distributed and symbolic state space generation, for generating the game graph. To that end we define a language module for LTSmin, consisting of an encoding of variables with parameters into state vectors, a grouped transition relation and a dependency matrix to indicate the dependencies between parts of the state vector and transition groups. Benchmarks on some large case studies, show that the method speeds up the instantiation significantly and decreases memory usage drastically
Construction and analysis of causally dynamic hybrid bond graphs
Engineering systems are frequently abstracted to models with discontinuous behaviour (such as a switch or contact),
and a hybrid model is one which contains continuous and discontinuous behaviours. Bond graphs are an established
physical modelling method, but there are several methods for constructing switched or ‘hybrid’ bond graphs, developed
for either qualitative ‘structural’ analysis or efficient numerical simulation of engineering systems. This article proposes a
general hybrid bond graph suitable for both. The controlled junction is adopted as an intuitive way of modelling a discontinuity in the model structure. This element gives rise to ‘dynamic causality’ that is facilitated by a new bond graph notation. From this model, the junction structure and state equations are derived and compared to those obtained by
existing methods. The proposed model includes all possible modes of operation and can be represented by a single set
of equations. The controlled junctions manifest as Boolean variables in the matrices of coefficients. The method is more
compact and intuitive than existing methods and dispenses with the need to derive various modes of operation from a
given reference representation. Hence, a method has been developed, which can reach common usage and form a platform for further study
A Quasi-Random Approach to Matrix Spectral Analysis
Inspired by the quantum computing algorithms for Linear Algebra problems
[HHL,TaShma] we study how the simulation on a classical computer of this type
of "Phase Estimation algorithms" performs when we apply it to solve the
Eigen-Problem of Hermitian matrices. The result is a completely new, efficient
and stable, parallel algorithm to compute an approximate spectral decomposition
of any Hermitian matrix. The algorithm can be implemented by Boolean circuits
in parallel time with a total cost of Boolean
operations. This Boolean complexity matches the best known rigorous parallel time algorithms, but unlike those algorithms our algorithm is
(logarithmically) stable, so further improvements may lead to practical
implementations.
All previous efficient and rigorous approaches to solve the Eigen-Problem use
randomization to avoid bad condition as we do too. Our algorithm makes further
use of randomization in a completely new way, taking random powers of a unitary
matrix to randomize the phases of its eigenvalues. Proving that a tiny Gaussian
perturbation and a random polynomial power are sufficient to ensure almost
pairwise independence of the phases is the main technical
contribution of this work. This randomization enables us, given a Hermitian
matrix with well separated eigenvalues, to sample a random eigenvalue and
produce an approximate eigenvector in parallel time and
Boolean complexity. We conjecture that further improvements of
our method can provide a stable solution to the full approximate spectral
decomposition problem with complexity similar to the complexity (up to a
logarithmic factor) of sampling a single eigenvector.Comment: Replacing previous version: parallel algorithm runs in total
complexity and not . However, the depth of the
implementing circuit is : hence comparable to fastest
eigen-decomposition algorithms know
Efficient reanalysis of structures by a direct modification method
A procedure for the local stiffness modifications of large structures is described. It enables structural modifications without an a priori definition of the changes in the original structure and without loss of efficiency due to multiple loading conditions. The solution procedure, implemented in NASTRAN, involved the decomposed stiffness matrix and the displacement vectors of the original structure. It solves the modified structure exactly, irrespective of the magnitude of the stiffness changes. In order to investigate the efficiency of the present procedure and to test its applicability within a design environment, several real and large structures were solved. The results of the efficiency studies indicate that the break-even point of the procedure varies between 8% and 60% stiffness modifications, depending upon the structure's characteristics and the options employed
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