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stochastic algorithm-based optimization using artificial intelligence/ machine learning models for sorption enhanced steam methane reformer reactor

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COMPUTERS & CHEMICAL ENGINEERING 2025年 196卷

作者： Bishnu, Sumit K. Alnouri, Sabla Y. Al Mohannadi, Dhabia M. IIT Madras Detect Technol Res Pk Chennai 600113 India Qatar Univ Coll Engn Gas Proc Ctr POB 2713 Doha Qatar Hamad Bin Khalifa Univ Coll Sci & Engn Doha Qatar Texas A&M Univ Qatar Dept Chem Engn PO 23874 Doha Qatar

There is a need for comprehensive tools that combine data-driven modeling with optimization techniques. In this work, a robust Random Forest Regression (RFR) model was developed to capture the behavior and characteristics of a Sorption Enhanced Steam Methane Reformer (SE-SMR) Reactor system. This model was then integrated into a Simulated Annealing (SA) optimization framework that helped identify the optimal operating conditions for the unit. The combined approach demonstrates the potential of using machine learning models in conjunction with optimization techniques to improve the solving process. The proposed methodology achieved an optimal methane conversion rate of 0.99979, and was successful in effectively identifying the optimal operating conditions that were required for near-complete conversion.

关键词： stochastic algorithm Optimization Artificial intelligence Machine learning Solver Simulated annealing

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Single-Loop Variance-Reduced stochastic algorithm for Nonconvex-Concave Minimax Optimization

Single-Loop Variance-Reduced Stochastic Algorithm for Noncon...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jiang, Xia Zhu, Linglingzhi Zheng, Taoli So, Anthony Man-Cho Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9798350368741

Nonconvex-concave (NC-C) finite-sum minimax problems have broad applications in decentralized optimization and various machine learning tasks. However, the nonsmooth nature of NC-C problems makes it challenging to design effective variance reduction techniques. Existing vanilla stochastic algorithms using uniform samples for gradient estimation often exhibit slow convergence rates and require bounded variance assumptions. In this paper, we develop a novel probabilistic variance reduction updating scheme and propose a single-loop algorithm called the probabilistic variance-reduced smoothed gradient descent-ascent (PVR-SGDA) algorithm. The proposed algorithm achieves an iteration complexity of O(ϵ−4), surpassing the best-known rates of stochastic algorithms for NC-C minimax problems and matching the performance of the best deterministic algorithms in this context. Finally, we demonstrate the effectiveness of the proposed algorithm through numerical simulations. © 2025 IEEE.

关键词： nonconvex-concave minimax optimization single-loop stochastic algorithm variance reduction

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A stochastic algorithm to compute optimal probabilities in the chaos game

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ADVANCES IN APPLIED PROBABILITY 2001年第2期33卷 423-436页

作者： Morato, LM Siri, P Univ Verona Fac Sci MMFFNN I-37134 Verona Italy

We present a stochastic algorithm which generates optimal probabilities for the chaos game to decompress an image represented by the fixed point of an IFS operator. The algorithm can be seen as a sort of time-inhomogeneous regenerative process. We prove that optimal probabilities exist and, by martingale methods, that the algorithm converges almost surely. The method holds for IFS operators associated with any arbitrary number of possibly overlapping affine contraction maps on the pixels space.

关键词： image decompression stochastic algorithm martingales

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USE OF RECURSIVE stochastic algorithm FOR NEURAL NETWORKS SYNTHESIS

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APPLIED MATHEMATICAL MODELLING 1993年第8期17卷 444-448页

作者： POZNYAK, AS NAJIM, K CHTOUROU, M USSR ACAD SCI INST CONTROL SCIMOSCOWRUSSIA ENSIGC GRECO SARTACNRSURA 192F-31078 TOULOUSEFRANCE

A new method based on a recursive stochastic algorithm is presented for neural networks synthesis. The cost function of the difference between the output of the net and the output of the process to be modelled by the net has nonunique stationary points. The common optimization techniques lead to local optima. It is shown that the solution of this optimization problem is connected with the construction of a convex envelope, characterized by local extrema of the initial problem. Then a recursive stochastic random search algorithm is derived for finding the optimum using realizations of a random variable associated with the function to be minimized. The application of this method is illustrated by an experimental example concerning neural networks synthesis for an industrial calcinator.

关键词： CALCINATOR NEURAL NETWORK OPTIMIZATION stochastic algorithm

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An improved stochastic algorithm for temperature-dependent homogeneous gas phase reactions

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JOURNAL OF COMPUTATIONAL PHYSICS 2003年第1期185卷 139-157页

作者： Kraft, M Wagner, W Univ Cambridge Dept Chem Engn Cambridge CB2 3RA England Weierstrass Inst Appl Anal & Stochast Berlin Germany

We propose an improved stochastic algorithm for temperature-dependent homogeneous gas phase reactions. By combining forward and reverse reaction rates, a significant gain in computational efficiency is achieved. Two modifications of modelling the temperature dependence (with and without conservation of enthalpy) are introduced and studied quantitatively. The algorithm is tested for the combustion of n-heptane, which is a reference fuel component for internal combustion engines. The convergence of the algorithm is studied by a series of numerical experiments and the computational cost of the stochastic algorithm is compared with the DAE code DASSL. If less accuracy is needed the stochastic algorithm is faster on short simulation time intervals. The new stochastic algorithm is significantly faster than the original direct simulation algorithm in all cases considered. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： homogeneous gas phase reaction stochastic algorithm Markov process temperature algorithm partial equilibrium

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Asymptotic behavior of a Markovian stochastic algorithm with constant step

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SIAM JOURNAL ON CONTROL AND OPTIMIZATION 1999年第5期37卷 1456-1482页

作者： Fort, JC Pagès, G Univ Nancy 1 Inst E Cartan F-54506 Vandoeuvre Nancy France Univ Paris 01 SAMOS F-75634 Paris 13 France Univ Paris 06 Probabil Lab URA 224 F-75252 Paris 05 France Univ Paris 12 UFR Sci F-94010 Creteil France

We first derive from abstract results on Feller transition kernels that, under some mild assumptions, a Markov stochastic algorithm with constant step size epsilon usually has a tight family of invariant distributions nu(epsilon), epsilon is an element of (0;epsilon(0)], whose weak limiting distributions as epsilon down arrow 0 are all flow-invariant for its ODE. Then the main part of the paper deals with a kind of converse: what are the possible limiting distributions among all flow-invariant distributions of the ODE? We first show that no repulsive invariant (thin) set can belong to their supports. When the ODE is a stochastic pseudogradient descent, these supports cannot contain saddle or spurious equilibrium points either, so that they are eventually supported by the set of local minima of their potential. Such results require only the random perturbation to lie in L-2. Various examples are treated, showing that these results yield some weak convergence results for the nu(epsilon)'s, sometimes toward a saddle point when the algorithm is not a pseudogradient.

关键词： stochastic algorithm stationary distribution Markov chain gradient descent saddle point

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Addendum to 'a stochastic algorithm to compute optimal probabilities in the chaos game': A new convergence criterion

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ADVANCES IN APPLIED PROBABILITY 2004年第1期36卷 318-323页

作者： Morato, LM Siri, P Fac Sci Settore Matemat Dipartimento Informat I-37134 Verona Italy

We propose a new convergence criterion for the stochastic algorithm for the optimization of probabilities (SAOP) described in an earlier paper. The criterion is based on the dissection principle for irreducible finite... 详细信息

关键词： image data stochastic algorithm Markov chain martingale

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Simulation of ammonium and chromium transport in porous media using coupling scheme of a numerical algorithm and a stochastic algorithm

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WATER SCIENCE AND TECHNOLOGY 2009年第8期59卷 1577-1584页

作者： Palanichamy, Jegathambal Schuettrumpt, Holger Koengeter, Juergen Becker, Torsten Palani, Sundarambal Rhein Westfal TH Aachen Inst Hydraul Engn & Water Resources Management D-52056 Aachen Germany Natl Univ Singapore Trop Marine Sci Inst Singapore 119223 Singapore

The migration of the species of chromium and ammonium in groundwater and their effective remediation depend on the various hydro-geological characteristics of the system. The computational modeling of the reactive transport problems is one of the most preferred tools for field engineers in groundwater studies to make decision in pollution abatement. The analytical models are less modular in nature with low computational demand where the modification is difficult during the formulation of different reactive systems. Numerical models provide more detailed information with high computational demand. Coupling of linear partial differential Equations (PDE) for the transport step with a non-linear system of ordinary differential equations (ODE) for the reactive step is the usual mode of solving a kinetically controlled reactive transport equation. This assumption is not appropriate for a system with low concentration of species such as chromium. Such reaction systems can be simulated using a stochastic algorithm. In this paper, a finite difference scheme coupled with a stochastic algorithm for the simulation of the transport of ammonium and chromium in subsurface media has been detailed.

关键词： groundwater pollution modelling porous media simulation stochastic algorithm

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Simulation of anaerobic digestion processes using stochastic algorithm

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JOURNAL OF ENVIRONMENTAL HEALTH SCIENCE AND ENGINEERING 2014年第1期12卷 1-8页

作者： Palanichamy, Jegathambal Palani, Sundarambal Karunya Univ Water Inst Coimbatore 641114 Tamil Nadu India Natl Univ Singapore Trop Marine Sci Inst Singapore 119227 Singapore

Background: The Anaerobic Digestion (AD) processes involve numerous complex biological and chemical reactions occurring simultaneously. Appropriate and efficient models are to be developed for simulation of anaerobic digestion systems. Although several models have been developed, mostly they suffer from lack of knowledge on constants, complexity and weak generalization. The basis of the deterministic approach for modelling the physico and bio-chemical reactions occurring in the AD system is the law of mass action, which gives the simple relationship between the reaction rates and the species concentrations. The assumptions made in the deterministic models are not hold true for the reactions involving chemical species of low concentration. The stochastic behaviour of the physicochemical processes can be modeled at mesoscopic level by application of the stochastic algorithms. Method: In this paper a stochastic algorithm (Gillespie Tau Leap Method) developed in MATLAB was applied to predict the concentration of glucose, acids and methane formation at different time intervals. By this the performance of the digester system can be controlled. The processes given by ADM1 (Anaerobic Digestion Model 1) were taken for verification of the model. Results: The proposed model was verified by comparing the results of Gillespie's algorithms with the deterministic solution for conversion of glucose into methane through degraders. At higher value of 'tau' (timestep), the computational time required for reaching the steady state is more since the number of chosen reactions is less. When the simulation time step is reduced, the results are similar to ODE solver. Conclusion: It was concluded that the stochastic algorithm is a suitable approach for the simulation of complex anaerobic digestion processes. The accuracy of the results depends on the optimum selection of tau value.

关键词： Anaerobic digestion stochastic algorithm Gillespie algorithm Modeling

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IQ-TREE: A Fast and Effective stochastic algorithm for Estimating Maximum-Likelihood Phylogenies

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MOLECULAR BIOLOGY AND EVOLUTION 2015年第1期32卷 268-274页

作者： Lam-Tung Nguyen Schmidt, Heiko A. von Haeseler, Arndt Bui Quang Minh Med Univ Vienna Univ Vienna Max F Perutz Labs Ctr Integrat Bioinformat Vienna Vienna Austria Univ Vienna Fac Comp Sci Vienna Austria

Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%. IQ-TREE is freely available at http://***/software/iqtree.

关键词： phylogenetic inference phylogeny maximum likelihood stochastic algorithm

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