近线性神经和元非线性神经元

人工神经网络(ANN)中普遍采用的神经元模型具有单调有界的I／O特性,该文称其为近线性神经元(ALN,approximate－linear neuron),ALN既简化了网络的复杂性,也是限制ANN能力的一个瓶径.通过比较Hopfield神经网络的稳定性和Conway生命游戏的复杂行为,该文提出非线性神经元(NLN,non－linear neuron)的概念,其I／O特性是完全非线性的.ALN可以进一步提高ANN的非线性,从而增强它的功能,余数制神经网络是一个例子.     

Evolving Teams of Predictors with Linear Genetic Programming

This paper applies the evolution of GP teams to different classification and regression problems and compares different methods for combining the outputs of the team programs. These include hybrid approaches where (1) a neural network is used to optimize the weights of programs in a team for a common decision and (2) a realnumbered vector (the representation of evolution strategies) of weights is evolved with each term in parallel. The cooperative team approach results in an improved training and generalization performance compared to the standard GP method. The higher computational overhead of team evolution is counteracted by using a fast variant of linear GP.     

Performance Analysis of Periodic Control for l1 and l∞ Disturbance Rejection

This paper presents a performance analysis of discrete time periodically time varying controllers for the rejection of l_p specific and uniform disturbances. Earlier results on l₂ performance are extended to l₁ and l_∞ performance to present a unified treatment of l_p performance for all p ε [1, ∞]. For a given linear time varying periodic controller, a linear time invariant controller is constructed and necessary and sufficient conditions are presented under which the linear time invariant controller gives strictly better l_p disturbance rejection performance than the time varying periodic controller.     

Solving multiple objective quasi-convex goal programming problems by linear programming

This study presents a novel means of resolving multiple objective goal programming (GP) problems with quasi-convex linear penalty functions. The proposed method initially expresses a quasi-convex function by the maximum operator of two convex functions, then solves it via a linear programming technique. The proposed method does not contain any zero–one variables; nor does it require dividing the multi-objective quasi-convex GP problem into large sub-problems as in conventional methods. Some illustrative examples are provided.     

分段延迟微分方程线性-方法数值解渐近稳定性

This paper deals with the stability analysis of numerical solutionof linear -methods for delay differential equations. We focuson the linear test equation x(t)=ax(t)+bx(［t］), where a,b areconstants and ［t］ is the largest-integer function. Sufficent conditionsare given for the numerical solution to be asymptotic stable     

A Linear Algorithm for Computing the Homography from Conics in Correspondence

This paper presents a study, based on conic correspondences, on the relationship between two perspective images acquired by an uncalibrated camera. We show that for a pair of corresponding conics, the parameters representing the conics satisfy a linear constraint. To be more specific, the parameters that represent a conic in one image are transformed by a five-dimensional projective transformation to the parameters that represent the corresponding conic in another image. We also show that this transformation is expressed as the symmetric component of the tensor product of the transformation based on point/line correspondences and itself. In addition, we present a linear algorithm for uniquely determining the corresponding point-based transformation from a given conic-based transformation up to a scale factor. Accordingly, conic correspondences enable us to easily handle both points and lines in uncalibrated images of a planar object.     

A Neural Network Approach to the Frictionless Grasping Problem

This article presents a heuristic technique used for solving linear complementarity problems(LCP). Determination of minimum forces needed to firmly grasp an object by a multifingered robot gripper for different external force and finger positions is our proposed application. The contact type is assumed to be frictionless. The interaction in the gripper–object system is formulated as an LCP. A numerical algorithm (Lemke) is used to solve the problem [3]. Lemke is a direct deterministic method that finds exact solutions under some constraints. Our proposed neural network technique finds almost exact solutions in solvable positions, and very good solutions for positions that Lemke fails to find solutions. A new adaptive technique is used for training the neural network and it is compared with the standard technique. Mathematical analysis for the convergence of the proposed technique is presented.     

基于Hopfield神经网络的线性系统参数辨识

基于Ｈｏｐｆｉｅｌｄ神经网络的优化计算原理,提出了一种新的线性系统参数辨识方法,利用连续型ＨＮＮ辨识线性系统Ｉ／Ｏ差分方程计算系统的传递函数矩阵,最终获得了可观标准型的状态空间表达式,仿真结果证明了这种只方法的有效性。     

不确定时滞线性系统的采样控制

采样控制作为一种数字控制的直接设计方法,近年来引起了广泛的重视,目前采样控制不易处理模型的不确定性和时滞,本语文给不确定时滞系统的采样控制算法,结果表示为一些矩阵不等式,并给中用ＬＭＩ求解的迭代算法。     

精细折线边界的粗拟合

讨论了用较少的折线段来表示精细折线边界的问题 .提出了解决该问题的一个有效算法 ,并对该算法进行了分析