A PDP constructive algorithm for system modelling

A new constructive algorithm is presented for building neural networks that learn to reproduce output temporal sequences based on one or several input sequences. This algorithm builds a network for the task of system modelling, dealing with continuous variables in the discrete time domain. The constructive scheme makes it user independent. The network's structure consists of an ordinary set and a classification set, so it is a hybrid network like that of Stokbro et al. [6], but with a binary classification. The networks can easily be interpreted, so the learned representation can be transferred to a human engineer, unlike many other network models. This allows for a better understanding of the system structure than just its simulation. This constructive algorithm limits the network complexity automatically, hence preserving extrapolation capabilities. Examples with real data from three totally different sources show good performance and allow for a promising line of research.     

Computer-aided design of P or PI plus rate feedback compensators for linear systems

A computer-aided solution for the gains of P or PI plus rate feedback compensators based on an exact model-matching criterion is developed. The corresponding Matlab functions are developed, and the software listings are given. Using the developed design tool, control engineers are able to obtain P or PI plus rate feedback gains much more quickly than traditional methods of design. Demonstration example problems for control of linear plants are presented, and results are compared with those obtained by other approaches reported in the open literature.     

Combined frequency and time domain technique for the design of compensators for non-linear feedback control systems

Computer based methods are developed for the analysis, synthesis and design of linear compensators for a wide class of single-loop non-linear feedback systems characterized by a separable non-linear element of any general multivalued form. Preliminary analysis is performed in the complex frequency domain applying approximate analytical methods to predict and quantify possible modes of limit cycle operation. Graphical displays are used to determine initial compensator structure which will ensure the avoidance of the critical region in this domain. Subsequently, a numerical synthesis procedure is applied to adjusting the compensator parameters to meet a set of additional time domain functional subject to specified system constraints and, if possible, the absence of autonomous limit-cycle operation.     

On the design,robustness, implementation and use of quasi-linear feedback compensators

A quasi-linear feedback compensator is one in which its poles depend in an appropriate way on its gain. The reason for introducing this new concept was the desire to remove the limitation to performance imposed by a plant with more than one pole in excess of its zeros. In this article it is shown that this objective is realized for plants with zeros in the left half of the complex plane. The consequences are surprising. In time domain it is possible to track arbitrarily fast a class of reference inputs despite a large class of disturbances and uncertainty in plant parameters. The response is non-oscillatory for high enough compensator gains, which is explained by the automatic adaptation of the closed loop poles to stability and stability margins for such gains. And in frequency domain the phase margin tends to 90° while the gain margin and crossover frequency become unlimited.

Technically the design procedure of quasi-linear compensators presented here is based on our theoretical result concerning the asymptotic behaviour of the roots of certain polynomials in a complex variable which depend also on a large positive parameter.

We also show how to implement such quasi-linear compensators in practical feedback control schemes, and their use at lower gains which is the case of most industrial applications.     

一类航天器控制系统反馈调度方法研究

针对目前航天器控制系统普遍采用静态调度方式,不能够及时调整计算资源分配的问题,提出了一种基于执行时间测量的计算资源反馈调度算法。该方法一方面保证航天器在工作时间段控制精度达到指标要求,另一方面能够动态调整计算资源与控制增益,降低系统功耗。同时在计算机发生故障时保证航天器控制系统稳定运行。最后通过仿真分析验证了方法有效性。     

A route set construction algorithm for the transit network design problem

The transit network design problem (TNDP) aims to determine a set of bus routes for a public transportation system, which must be convenient from the viewpoints of both users (people who use public transportation) and operators (companies who own the resources to give the service). This article presents a constructive algorithm for the TNDP. It is specially designed to produce a set of routes that fulfils demand covering constraints, while taking into account the interests of both users and operators. Its general structure is inspired in the Route Generation Algorithm (RGA) of Baaj and Mahmassani, where its original expansion of routes by inserting individual vertices is replaced by a strategy of insertion of pairs of vertices. The algorithm proposed, called Pair Insertion Algorithm (PIA) can be used to generate initial solutions for a local improvement or evolutionary algorithm, as well as to complete an unfeasible solution with respect to demand covering constraints. Numerical results comparing PIA with RGA over a real test case show that both algorithms produce solutions with similar quality from the users viewpoint (in terms of in-vehicle travel time), while the former produces better solutions from the operators viewpoint (in terms of number of routes and total route duration) and requires a higher execution time. Since the TNDP arises in a context of strategic planning, a solution that reduces the operation cost of the system is highly desirable, even though it takes more time to be computed. The experimental study of the proposed algorithm also shows its ability to produce diverse solutions in both decision and objective spaces; this is a useful property when looking at the use of PIA as a subroutine in the context of another algorithm such as metaheuristics, in particular for a multi-objective problem like TNDP.     

A note on feedback compensators in optimal linear systems

Two basic methods for designing feedback compensators which may be used to achieve arbitrary closed-loop pole positions are now available. This correspondence shows that when each method is used within the framework of optimal control the same compensator structure results, but the more standard approach using a state estimator and a linear law appears to be the more attractive from the information now available.     

A constructive algorithm for binary neural networks: the oil-spot algorithm.

This paper presents a constructive training algorithm for supervised neural networks. The algorithm relies on a topological approach, based on the representation of the mapping of interest onto the binary hypercube of the input space. It dynamically constructs a two-layer neural network by involving successively binary examples. A convenient treatment of real-valued data is possible by means of a suitable real-to-binary codification. In the case of target functions that have efficient halfspace union representations, simulations show the constructed networks result optimized in terms of number of neurons.     

Computational design techniques for reduced-order compensators

The authors propose two computational techniques for the pole-placement problem using reduced-order compensators. Computationally efficient algorithms are presented for the observer-based compensator and the Brasch-Pearson compensator. The key tool for these algorithms is the concept of two coupled Sylvester equations, the solutions of which completely characterize the desired reduced-order compensators     

交互式多模型算法的模型集设计

针对交互式多模型（IMM）算法中模型集的设计问题，提出一种模型集设计方法．在已知真实模式概率分布函数的情况下，根据要求的模型数目，等概率划分模式空间，在每个等概率模式空间上，利用方差的含义获得最大范围地覆盖模式空间的模型集，并给出一个例子说明模型集的设计方法．应用Monte-Carlo仿真．将所设计的模型集用于IMM算法进行目标跟踪．仿真结果显示跟踪精度高，表明了该模型集设计方法的有效性．     

Improved FPT algorithm for feedback vertex set problem in bipartite tournament

We present an O(k³n²+n³) time FPT algorithm for the feedback vertex set problem in a bipartite tournament on n vertices with integral weights. This improves the previously best known O(k^3.12n⁴) time FPT algorithm for the problem.     

基于随机演化的最小反馈弧集的改进算法

最小反馈弧集问题是一类组合优化问题,在实践中具有广泛的应用。随机演化是解决组合优化问题的一种通用的迭代随机过程。提出了一种基于随机演化的最小反馈弧集问题的改进算法。实验结果表明,改进之后的算法不仅提高了解的质量而且还减少了运行时间。     

磁悬浮系统的加速度计反馈控制算法

设计了一种基于加速度计信号改善磁浮列车悬浮性能的控制算法.将固定在电磁铁上的加速度计的输出直接引入反馈控制最,使得悬浮控制系统在不同轨道曲线段上具有相同的表达式,从而使反馈线性化算法得以应用;利用离散跟踪一微分器,从测量到的间隙信号提取其一阶、二阶微分,使实际控制算法得以实现.仿真计算表明,该算法消除了列车进、出竖曲线的冲击,提高了悬浮性能.     

A constructive algorithm for training cooperative neural network ensembles

Presents a constructive algorithm for training cooperative neural-network ensembles (CNNEs). CNNE combines ensemble architecture design with cooperative training for individual neural networks (NNs) in ensembles. Unlike most previous studies on training ensembles, CNNE puts emphasis on both accuracy and diversity among individual NNs in an ensemble. In order to maintain accuracy among individual NNs, the number of hidden nodes in individual NNs are also determined by a constructive approach. Incremental training based on negative correlation is used in CNNE to train individual NNs for different numbers of training epochs. The use of negative correlation learning and different training epochs for training individual NNs reflect CNNEs emphasis on diversity among individual NNs in an ensemble. CNNE has been tested extensively on a number of benchmark problems in machine learning and neural networks, including Australian credit card assessment, breast cancer, diabetes, glass, heart disease, letter recognition, soybean, and Mackey-Glass time series prediction problems. The experimental results show that CNNE can produce NN ensembles with good generalization ability.     

A constructive bin-oriented heuristic for the two-dimensional bin packing problem with guillotine cuts

A new heuristic algorithm for solving the two-dimensional bin-packing problem with guillotine cuts (2DBP|?|G)$(2\mathrm{DBP}|?|\mathrm{G})$ is presented. The heuristic constructs a solution by packing a bin at a time. Central to the adopted solution scheme is the principle of average-area sufficiency proposed by the authors for guiding selection of items to fill a bin. The algorithm is tested on a set of standard benchmark problem instances and compared with existing heuristics producing the best-known results. The results presented attest to the efficacy of the proposed scheme.     

An active set rqp algorithm for engineering design optimization

Optimum design of complex engineering systems needs a globally and superlinearly convergent (robust and efficient) algorithm using active set strategy. Such an algorithm based on extensions of Pshenichny's linearization method is derived in the paper. In the original method, a linearized subproblem with a quadratic step-size constraint is used to compute a direction of design change. No second-order information is computed or approximated for use in the direction finding problem. Therefore, the rate of convergence is only linear. In the paper, we propose to incorporate a variable metric W which is a positive-definite approximation to the Hessian of the Lagrange function. This gives local superlinear rate of convergence to the algorithm. Some other computational improvements are discussed and incorporated. The proposed improvements appear to be quite simple. They are, however, quite significant for applications to engineering design problems. This is explained and several small-scale problems are solved using a program based on the modified algorithm. Results are compared to Pshenichny's original algorithm. The modified algorithm is considerably more efficient compared to the previous algorithm. It also appears to be quite robust, though more extensive testing is needed on a wider range of problems.     

CARVE-a constructive algorithm for real-valued examples

A constructive neural-network algorithm is presented. For any consistent classification task on real-valued training vectors, the algorithm constructs a feedforward network with a single hidden layer of threshold units which implements the task. The algorithm, which we call CARVE, extends the "sequential learning" algorithm of Marchand et al. (1990) from Boolean inputs to the real-valued input case, and uses convex hull methods for the determination of the network weights. The algorithm is an efficient training scheme for producing near-minimal network solutions for arbitrary classification tasks. The algorithm is applied to a number of benchmark problems including German and Sejnowski's sonar data, the Monks problems and Fisher's iris data. A significant application of the constructive algorithm is in providing an initial network topology and initial weights for other neural-network training schemes, and this is demonstrated by application to backpropagation.