基于微种群遗传算法和自适应BP算法的遥感图像分类

介绍了采用微种群遗传算法和自适应BP算法相结合的混合遗传算法来训练前向人工神经网络(BPNN)的方法。即先用微种群遗传学习算法进行全局训练,再用自适应BP算法进行精确训练,以达到加快网络收敛速度和避免陷入局部极小值的目的。将此算法用于遥感图像分类,网络的训练速度及分类结果表明,该算法收敛速度较快,预测精度较高。     

大型非球面环带精磨方法

国内大型非球面的加工流程一般是先精磨成最接近球面,然后抛光修磨成非球面。由于这种方法抛光效率低,所以整个流程的加工周期很长。提高加工效率的方法之一是在精磨阶段就将其修磨成非球面,为此提出了一种采用较大磨盘,由自校正模糊补偿控制模型控制环带精磨非球面的方法。试验结果表明,采用该方法可以得到精磨成型非球面,从而提高了大型非球面的整体加工效率。     

Application of Euler Neural Networks with Soft Computing Paradigm to Solve Nonlinear Problems Arising in Heat Transfer

In this study, a novel application of neurocomputing technique is presented for solving nonlinear heat transfer and natural convection porous fin problems arising in almost all areas of engineering and technology, especially in mechanical engineering. The mathematical models of the problems are exploited by the intelligent strength of Euler polynomials based Euler neural networks (ENN’s), optimized with a generalized normal distribution optimization (GNDO) algorithm and Interior point algorithm (IPA). In this scheme, ENN’s based differential equation models are constructed in an unsupervised manner, in which the neurons are trained by GNDO as an effective global search technique and IPA, which enhances the local search convergence. Moreover, a temperature distribution of heat transfer and natural convection porous fin are investigated by using an ENN-GNDO-IPA algorithm under the influence of variations in specific heat, thermal conductivity, internal heat generation, and heat transfer rate, respectively. A large number of executions are performed on the proposed technique for different cases to determine the reliability and effectiveness through various performance indicators including Nash–Sutcliffe efficiency (NSE), error in Nash–Sutcliffe efficiency (ENSE), mean absolute error (MAE), and Thiel’s inequality coefficient (TIC). Extensive graphical and statistical analysis shows the dominance of the proposed algorithm with state-of-the-art algorithms and numerical solver RK-4.     

四角切圆炉膛三维温度分布优化控制建模研究

炉膛燃烧三维温度分布可视化技术为实现炉内燃烧三维温度分布优化控制奠定了基础。本文采用数值计算的方法建立了炉内不同高度特征截面平均温度及相应的温度中心坐标作为中间被控变量,以各层各角燃烧器燃料量及一次风、二次风量等参数为输入变量的线性模型。检验结果表明该模型能正确反映输入变量的变化对炉内温度分布的影响,为采用自适应遗传算法实施燃烧优化控制创造了条件。     

Pivoting in Linear Complementarity: Two Polynomial-Time Cases

We study the behavior of simple principal pivoting methods for the P-matrix linear complementarity problem (P-LCP). We solve an open problem of Morris by showing that Murty’s least-index pivot rule (under any fixed index order) leads to a quadratic number of iterations on Morris’s highly cyclic P-LCP examples. We then show that on K-matrix LCP instances, all pivot rules require only a linear number of iterations. As the main tool, we employ unique-sink orientations of cubes, a useful combinatorial abstraction of the P-LCP.     

Vibration testing for anomaly detection

In this paper we propose an efficient method to reconstruct a small inclusion buried inside a body using the perturbation of modal parameters measured on the boundary of the body. We design a reconstruction algorithm based on the asymptotic expansions of the eigenvalue perturbations obtained by Ammari and Moskow (Math. Meth. Appl. Sci. 2003; 26 :67–75). We then implement this algorithm and demonstrate its viability and limitations. Copyright © 2008 John Wiley & Sons, Ltd.     

A class of nonlinear iterated function system attractors

In this article the theory of NIFS (nonlinear iterated function system) and the random iteration algorithm are expounded. By theoretical analysis, the definition domain of a class of complex polynomial hyperbolic NIFS in the complex c$c$-plane is given out and a series of the NIFS attractors are simulated. In the article, the changing regularity of the NIFS attractors are discussed when the control parameters are changed and the relationship between the totally disconnected NIFS attractor and its address set is discussed. Results show that: (1) the multiplicity of the NIFS inverse function leads to multiple addresses of the same region for NIFS attractors, and (2) the NIFS attractors have chaotic dynamic characteristics, and the evolution of the NIFS attractors depend on the choice of the range of the principal argument _θz${\theta }_z$.     

A polynomial-time DNA computing solution for the Bin-Packing Problem

It is suggested here an algorithm based on stickers for the DNA Computing model [S. Roweis, E. Winfree, R. Burgoyne, N. Chelyapov, M. Goodman, P. Rothemund, L. Adleman, A sticker-based model for DNA Computation, Journal of Computational Biology 5 (1998) 615–629] that solves the well known Bin-Packing Problem (BPP), that belongs to the class -Hard in the strong sense, in a time bounded by , where n is the quantity of items and q the space requirements expressed in bits.To the best of the authors’ knowledge, this is the first polynomial time algorithmic solution for the BPP in such a model.     

The EOQ problem with decidable warehouse capacity: Analysis, solution approaches and applications

The Economic Order Quantity (EOQ) problem is a fundamental problem in supply and inventory management. In its classical setting, solutions are not affected by the warehouse capacity. We study a type of EOQ problem where the (maximum) warehouse capacity is a decision variable. Furthermore, we assume that the warehouse cost dominates all the other inventory holding costs. We call this the EOQ-Max problem and the D-EOQ-Max problem, if the product is continuously divisible and discrete, respectively. The EOQ-Max problem admits a closed form optimal solution, while the D-EOQ-Max problem does not because its objective function may have several local minima. We present an optimal polynomial time algorithm for the discrete problem. Construction of this algorithm is supported by the fact that continuous relaxation of the D-EOQ-Max problem provides a solution that can be up to 50% worse than the optimal solution, and this worst-case error bound is tight. Applications of the D-EOQ-Max problem include supply and inventory management, logistics and scheduling.