Search results for "probability"
showing 10 items of 3417 documents
Statistical criteria for early-stopping of support vector machines
2007
This paper proposes the use of statistical criteria for early-stopping support vector machines, both for regression and classification problems. The method basically stops the minimization of the primal functional when moments of the error signal (up to fourth order) become stationary, rather than according to a tolerance threshold of primal convergence itself. This simple strategy induces lower computational efforts and no significant differences are observed in terms of performance and sparsity.
Passivity-based output feedback control of Markovian jump systems with discrete and distributed time-varying delays
2013
In this article, we present a new method in designing mode-dependent passivity-based output feedback controllers for Markovian jump systems with time-varying delays. Both discrete and distributed delays are considered in the model. A Lyapunov–Krasovskii function is constructed to establish new required sufficient conditions for ensuring exponentially mean-square stability and the passivity criteria, simultaneously. The method produces linear matrix inequality formulation that allows obtaining controller gains based on a convex optimisation method. Finally, a numerical example is given to illustrate the effectiveness of our approach.
A passivity approach to control of Markovian jump systems with mixed time-varying delays
2013
This paper investigated the problem of control design for a class of stochastic systems with Markovian jump parameters and time-varying delays. For the model under consideration, a passivity-based approach is introduced for designing mode-dependent output feedback controllers with mixed discrete and distributed delays. A Lypunov-Krasovskii function (LKF) is defined to establish new required sufficient conditions for ensuring exponentially mean-square stability and the passivity criteria, simultaneously. Moreover, controller gains are calculated based on a convex optimization method by solving a Linear Matrix Inequality (LMI). Finally, simulation results are provided to illustrate the effect…
Limit Analysis of Structures with Stochastic Strength Variations∗
1972
Abstract On the basis of a probabilistic fomulation of the fundamental theorems of “limit analysis,” a procedure is developed which allows, with a very limited amount of computing work, the determination of a domain containing the probability distribution curve of the collapse load factor of any structure that satisfies the usual conditions for validity of the limit analysis, but has randomly distributed limit strengths. Further improvements of the bounds thus obtained can be achieved by the equivalent of either the equilibrium or the kinematic methods of limit analysis.
Non Gaussian closure techniques for the analysis of R-FBI isolation system
1997
The Resilient-Friction Base Isolator (R-FBI) stochastic response under severe ground motion modelled as a stationary and non-stationary zero mean stochastic white noise processes is performed. The moment equation approach is applied and the non-normal response is obtained by means of a non-Gaussian closure technique, based on the Gram-Charlier asymptotic expansion of the response probability density function. Results are compared with the equivalent non linearization technique and with results obtained by means of Monte Carlo simulation.
Reference Priors in a Variance Components Problem
1992
The ordered group reference prior algorithm of Berger and Bernardo (1989b) is applied to the balanced variance components problem. Besides the intrinsic interest of developing good noninformative priors for the variance components problem, a number of theoretically interesting issues arise in application of the proposed procedure. The algorithm is described (for completeness) in an important special case, with a detailed heuristic motivation.
A Local Selection Algorithm for Switching Function Minimization
1984
The minimization algorithms which do not require any preliminary generation of all the prime implicants (PI's) of a function are the most efficient. In this work a new algorithm is described which follows such an approach. It is based on a local selection of PI's carried out by examining a set of vertices whose number is never greater than the number of PI's of a minimum cost cover. This algorithm takes advantage of a technique which uses numerical equivalents of the function vertices as pointers. For this reason it is well suited for implementation by computer. To illustrate the features of this algorithm a few examples are reported.
Solutions for districting problems with chance-constrained balancing requirements
2021
Abstract In this paper, a districting problem with stochastic demands is investigated. The goal is to divide a geographic area into p contiguous districts such that, with some given probability, the districts are balanced with respect to some given lower and upper thresholds. The problem is cast as a p -median problem with contiguity constraints that is further enhanced with chance-constrained balancing requirements. The total assignment cost of the territorial units to the representatives of the corresponding districts is used as a surrogate compactness measure to be optimized. Due to the tantalizing purpose of deriving a deterministic equivalent for the problem, a two-phase heuristic is d…
Multi-start methods for combinatorial optimization
2013
Abstract Multi-start methods strategically sample the solution space of an optimization problem. The most successful of these methods have two phases that are alternated for a certain number of global iterations. The first phase generates a solution and the second seeks to improve the outcome. Each global iteration produces a solution that is typically a local optimum, and the best overall solution is the output of the algorithm. The interaction between the two phases creates a balance between search diversification (structural variation) and search intensification (improvement), to yield an effective means for generating high-quality solutions. This survey briefly sketches historical devel…
The design of absorbing Bayesian pursuit algorithms and the formal analyses of their ε-optimality
2016
The fundamental phenomenon that has been used to enhance the convergence speed of learning automata (LA) is that of incorporating the running maximum likelihood (ML) estimates of the action reward probabilities into the probability updating rules for selecting the actions. The frontiers of this field have been recently expanded by replacing the ML estimates with their corresponding Bayesian counterparts that incorporate the properties of the conjugate priors. These constitute the Bayesian pursuit algorithm (BPA), and the discretized Bayesian pursuit algorithm. Although these algorithms have been designed and efficiently implemented, and are, arguably, the fastest and most accurate LA report…