Hyperparameter on-line learning of stochastic resonance based threshold networks

Aiming at training the feed-forward threshold neural network consisting of nondifferentiable activation functions, the approach of noise injection forms a stochastic resonance based threshold network that can be optimized by various gradient-based optimizers. The introduction of injected noise extends the noise level into the parameter space of the designed threshold network, but leads to a highly non-convex optimization landscape of the loss function. Thus, the hyperparameter on-line learning procedure with respective to network weights and noise levels becomes of challenge. It is shown that the Adam optimizer, as an adaptive variant of stochastic gradient descent, manifests its superior learning ability in training the stochastic resonance based threshold network effectively. Experimental results demonstrate the significant improvement of performance of the designed threshold network trained by the Adam optimizer for function approximation and image classification.     

Simulation of viscous water column collapse using adapting hierarchical grids

An adaptive hierarchical grid‐based method for predicting complex free surface flows is used to simulate collapse of a water column. Adapting quadtree grids are combined with a high‐resolution interface‐capturing approach and pressure‐based coupling of the Navier–Stokes equations. The Navier–Stokes flow solution scheme is verified for simulation of flow in a lid‐driven cavity at Re=1000. Two approaches to the coupling of the Navier–Stokes equations are investigated as are alternative face velocity and hanging node interpolations. Collapse of a water column as well as collapse of a water column and its subsequent interaction with an obstacle are simulated. The calculations are made on uniform and adapting quadtree grids, and the accuracy of the quadtree calculations is shown to be the same as those made on the equivalent uniform grids. Results are in excellent agreement with experimental and other numerical data. A sharp interface is maintained at the free surface. The new adapting quadtree‐based method achieves a considerable saving in the size of the computational grid and CPU time in comparison with calculations made on equivalent uniform grids. Copyright © 2005 John Wiley & Sons, Ltd.     

束晕-混沌的复杂性理论与控制方法及其应用前景

本文系统论述涉及强流加速器等强流离子束装置中产生的束晕-混沌的复杂性理论与控制方法及其应用前景。强流离子束在核材料生产与增殖、洁净核能、放射性废物嬗变、放射性药物生产、重离子聚变、高能物理、核科学与工程、国防与民用工业和医疗等许多方面都有极其重要的应用潜力和诱人的发展前景。尤其是，近年来强流加速器驱动的放射性洁净核能系统是国内外关注的热门课题，因为它比常规核电更安全、更干净、更便宜。但是，强流离子束形成的束晕-混沌的复杂性现象已引起了国内外广泛关注，需要加以抑制、控制和消除这类现象，解决这一难题已经成为强流离子束应用中的关键问题之一。目前不仅必须深入研究这类束晕-混沌的复杂特性及其产生的物理机制，而且需要研究如何实现对束晕-混沌的有效控制，并寻求和发展其新理论、新方法和新技术。这就向强流离子束物理和非线性-复杂性科学及其技术提出了一系列极富挑战性的新课题。本文结合国内外的研究概况，根据我们多年来的研究成果，特别是我们首创性地提出了一些束晕-混沌的有效控制方法，它们包括：非线性反馈控制法，小波反馈控制法，变结构控制法，延迟反馈控制法，参数自适应控制法等，进行重点的介绍。对上述课题当前的主要进展及相关问题进行系统的总结和比较全面综述的评论。最后，指出该领域今后的研究方向，以推动这个崭新领域的深入研究和应用发展。     

基于进化人工神经网络的组合系统滤波技术

在组合系统运用Kalman滤波器技术时,准确的系统模型和可靠的观测数据是保证其性能的重要因素,否则将大大降低Kalman滤波器的估计精度,甚至导致滤波器发散.为解决上述Kalman应用中的实际问题,提出了一种新颖的基于进化人工神经网络技术的自适应Kalman滤波器.仿真试验表明该算法可以在系统模型不准确时、甚至外部观测数据短暂中断时,仍能保证Kalman滤波器的性能.     

Adaptive remeshing for ductile fracture prediction in metal formingRemaillage adaptatif pour la prévision de la rupture ductile en mise en forme

The analysis of mechanical structures using the Finite Element Method in the framework of large elastoplastic strain, needs frequent remeshing of the deformed domain during computation. Indeed, the remeshing is due to the large geometrical distortion of finite elements and the adaptation to the physical behavior of the solution. This paper gives the necessary steps to remesh a mechanical structure during large elastoplastic deformations with damage. An important part of this process is constituted by geometrical and physical error estimates. The proposed method is integrated in a computational environment using the ABAQUS/Explicit solver and the BL2D-V2 adaptive mesher. To cite this article: H. Borouchaki et al., C. R. Mecanique 330 (2002) 709–716.     

浅水中污染物扩散的自适应有限元分析法

介绍浅水中污染物扩散分析中的有限元法.分析包括两个部分:1)流场速度、水面高度的计算;2)根据扩散模型计算污染物浓度场.联合使用了自适应网格技术以期提高解的精度,同时减少计算时间和计算机内存的消耗.通过几个有已知解的实例验证了有限元公式和计算机程序.最后,使用这种联合方法分析泰国Chao Phraya河附近海湾中的污染物扩散.     

Locally Adaptive Wavelet Empirical Bayes Estimation of a Location Parameter

The traditional empirical Bayes (EB) model is considered with the parameter being a location parameter, in the situation when the Bayes estimator has a finite degree of smoothness and, possibly, jump discontinuities at several points. A nonlinear wavelet EB estimator based on wavelets with bounded supports is constructed, and it is shown that a finite number of jump discontinuities in the Bayes estimator do not affect the rate of convergence of the prior risk of the EB estimator to zero. It is also demonstrated that the estimator adjusts to the degree of smoothness of the Bayes estimator, locally, so that outside the neighborhoods of the points of discontinuities, the posterior risk has a high rate of convergence to zero. Hence, the technique suggested in the paper provides estimators which are significantly superior in several respects to those constructed earlier.     

松纵坑切梢小蠹虫害的仿真模型与生态防治的优化策略

根据松纵坑切梢小蠹的主要生物学特征,通过分析抚育间伐持续控制松纵坑切梢小蠹虫害的机理,应用复杂适应系统(CA S)理论,建立了模拟松纵坑切梢小蠹虫害的一种动态演化数学模型,并讨论了模型的收敛性.然后选用昆明小哨林场为背景,进行了各种抚育间伐决策的仿真,从中优选抚育间伐决策.结果表明:模拟优化方案与实际调查统计分析结果一致:均为弱度间伐与中弱度间伐控制蠹害效果最好.最后通过森林积材量与虫害程度之间的优化,给出了抚育间伐的优化策略.     

Combining Swaps and Node Weights in an Adaptive Greedy Approach for the Maximum Clique Problem

In this work, the NP-hard maximum clique problem on graphs is considered. Starting from basic greedy heuristics, modifications and improvements are proposed and combined in a two-phase heuristic procedure. In the first phase an improved greedy procedure is applied starting from each node of the graph; on the basis of the results of this phase a reduced subset of nodes is selected and an adaptive greedy algorithm is repeatedly started to build cliques around such nodes. In each restart the selection of nodes is biased by the maximal clique generated in the previous execution. Computational results are reported on the DIMACS benchmarks suite. Remarkably, the two-phase procedure successfully solves the difficult Brockington-Culberson instances, and is generally competitive with state-of-the-art much more complex heuristics.