A novel artificial neural network (ANN) suitable for computationally intensive problems is described in this paper. The usefulness of this ANN is demonstrated for the synthesis of a microstrip line. This ANN uses a st...
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A novel artificial neural network (ANN) suitable for computationally intensive problems is described in this paper. The usefulness of this ANN is demonstrated for the synthesis of a microstrip line. This ANN uses a standard neural network architecture consisting of a hetero-associative memory and exploits a fault-tolerant number representation, which gives significant insight into a new method of fault-tolerant computing. In addition this ANN provides an efficient method for synthesizing geometrical parameters of microwave devices, when stochastic features are incorporated in the synthesis process. Further research is required to investigate the potential of this new paradigm. (C) 2005 Published by Elsevier Ltd.
This article introduces a new global optimization procedure called LARES. LARES is based on the concept of an artificial chemical process (ACP), a new paradigm which is described in this article. The algorithm's p...
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This article introduces a new global optimization procedure called LARES. LARES is based on the concept of an artificial chemical process (ACP), a new paradigm which is described in this article. The algorithm's performance was studied using a test bed with a wide spectrum of problems including random multi-modal random problem generators, random LSAT problem generators with various degrees of epistasis, and a test bed of real-valued functions with different degrees of multi-modality, discontinuity and flatness. In all cases studied, LARES performed very well in terms of robustness and efficiency.
In this note, we discuss fast randomized algorithms for determining an admissible solution for robust linear matrix inequalities (LMIs) of the form F(x, Delta) less than or equal to 0, where 0 is the optimization vari...
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In this note, we discuss fast randomized algorithms for determining an admissible solution for robust linear matrix inequalities (LMIs) of the form F(x, Delta) less than or equal to 0, where 0 is the optimization variable and Delta is the uncertainty, which belongs to a given set Delta. The proposed algorithms are based on uncertainty randomization: the first algorithm finds a robust solution in a finite number of iterations with probability one, if a strong feasibility condition holds. In case no robust solution exists, the second algorithm computes an approximate solution which minimizes the expected value of a suitably selected feasibility indicator function. The theory is illustrated by examples of application to uncertain linear inequalities and quadratic stability of interval matrices.
This paper introduces a stochastic algorithm for computing symmetric Markov perfect equilibria. The algorithm computes equilibrium policy and value functions, and generates a transition kernel for the (stochastic) evo...
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This paper introduces a stochastic algorithm for computing symmetric Markov perfect equilibria. The algorithm computes equilibrium policy and value functions, and generates a transition kernel for the (stochastic) evolution of the state of the system, It has two features that together imply that it need not be subject to the curse of dimensionality. First, the integral that determines continuation values is never calculated;rather it is approximated by a simple average of returns from past outcomes of the algorithm, an approximation whose computational burden is not tied to the dimension of the state space. Second, iterations of the algorithm update value and policy functions at a single (rather than at all possible) points in the state space. Random draws from a distribution set by the updated policies determine the location of the next iteration's updates. This selection only repeatedly hits the recurrent class of points, a subset whose cardinality is not directly tied to that of the state space. Numerical results for industrial organization problems show that our algorithm can increase speed and decrease memory requirements by several orders of magnitude.
In this paper, we review recent results concerning stochastic models for coagulation processes and their relationship to deterministic equations. Open problems related to the gelation effect are discussed. Finally, we...
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In this paper, we review recent results concerning stochastic models for coagulation processes and their relationship to deterministic equations. Open problems related to the gelation effect are discussed. Finally, we present some new conjectures based on numerical experiments performed with stochastic algorithms. (C) 2003 IMACS. Published by Elsevier Science B.V. All rights reserved.
In this paper, we review recent results concerning stochastic models for coagulation processes and their relationship to deterministic equations. Open problems related to the gelation effect are discussed. Finally, we...
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In this paper, we review recent results concerning stochastic models for coagulation processes and their relationship to deterministic equations. Open problems related to the gelation effect are discussed. Finally, we present some new conjectures based on numerical experiments performed with stochastic algorithms. (C) 2003 IMACS. Published by Elsevier Science B.V. All rights reserved.
A stochastic global optimization method is applied to the challenging problem of finding the minimum energy conformation of a cluster of identical atoms interacting through the Lennard-Jones potential. The method prop...
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A stochastic global optimization method is applied to the challenging problem of finding the minimum energy conformation of a cluster of identical atoms interacting through the Lennard-Jones potential. The method proposed incorporates within an already existing and quite successful method, monotonic basin hopping, a two-phase local search procedure which is capable of significantly enlarging the basin of attraction of the global optimum. The experiments reported confirm the considerable advantages of this approach, in particular for all those cases which are considered in the literature as the most challenging ones, namely 75, 98, 102 atoms. While being capable of discovering all putative global optima in the range considered, the method proposed improves by more than two orders of magnitude the speed and the percentage of success in finding the global optima of clusters of 75, 98, 102 atoms.
In this paper we prove the large deviation principle for a class of random walks with state-dependent noise. This type of model has important applications in queueing and communication theory and in the area of stocha...
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In this paper we prove the large deviation principle for a class of random walks with state-dependent noise. This type of model has important applications in queueing and communication theory and in the area of stochastic approximation.
This purpose of this introductory paper is threefold. First, it introduces the Monte Carlo method with emphasis on probabilistic machine learning. Second, it reviews the main building blocks of modern Markov chain Mon...
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This purpose of this introductory paper is threefold. First, it introduces the Monte Carlo method with emphasis on probabilistic machine learning. Second, it reviews the main building blocks of modern Markov chain Monte Carlo simulation, thereby providing and introduction to the remaining papers of this special issue. Lastly, it discusses new interesting research horizons.
We have created a stochastic impulse-response (IR) moment-extraction algorithm for RC circuit networks. It employs a newly discovered Feynman Sum-over-Paths Postulate. Full parallelism has been preserved. Numerical ve...
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ISBN:
(纸本)1581137621
We have created a stochastic impulse-response (IR) moment-extraction algorithm for RC circuit networks. It employs a newly discovered Feynman Sum-over-Paths Postulate. Full parallelism has been preserved. Numerical verification results for coupled RC lines confirmed rapid convergence. We believe this algorithm may find useful application in massively coupled electrical systems, such as those encountered in high-end digital-IC interconnects.
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