This paper considers the shortest time limit transportation problem with storage cost upper limitation on bipartite graph. The authors prove that its feasible solution corresponds to that of transportation problem uni...
详细信息
This paper considers the shortest time limit transportation problem with storage cost upper limitation on bipartite graph. The authors prove that its feasible solution corresponds to that of transportation problem uniquely, the basic feasible solution of transportation problem corresponds to its basic solution, and the optimal solution of transportation problem is one of its optimal solutions. Then we revise the transportation cost of the edge with longer transportation time into continuously, and get its optimal solution by solving the transportation problem. Finally, the authors present an example to show that the algorithm runs easily and quickly.
The problem of uncertainty knowledge has been tackled for a long time by philosophers, logicians and math.maticians. Recently it becomes a crucial issue for computer scientists, particularly in the area of artificial ...
详细信息
ISBN:
(纸本)0780384032
The problem of uncertainty knowledge has been tackled for a long time by philosophers, logicians and math.maticians. Recently it becomes a crucial issue for computer scientists, particularly in the area of artificial intelligence (AI). The Set pair analysis (SPA) theory, proposed by Keqin Zhao, is a novel uncertainty theory. The core of this theory is to consider certainties and uncertainties as a certain-uncertain system, and to depict uniformly all kinds of uncertainties such as random uncertainty, fuzzy uncertainty, indeterminate-known uncertainty, unknown and unexpected incident uncertainty, and uncertainty that results from imperfective information, using a connection degree formula that can fully embody its idea. SPA has been applied to many fields successfully such as industry, agriculture, forestry, education, physical education, military affairs, traffic, data fusion, decision-making, forecasting, comprehensive evaluation, and network planning, etc. The reason is that there exists abundant systems information such as system structure information, system theory information, etc, in SPA. In this paper, the systems information in SPA is discussed, and its applications are also given.
This paper is concerned with the problem of global stabilization by output feedback for a class of time-delay nonlinear systems that are dominated by a triangular system satisfying linear growth condition. By construc...
详细信息
This paper is concerned with the problem of global stabilization by output feedback for a class of time-delay nonlinear systems that are dominated by a triangular system satisfying linear growth condition. By constructing the appropriate Lyapunov-Krasovskii functionals (LKF), the linear and memoryless output feedback controller making the closed-loop system globally asymptotically stable (GAS) is explicitly constructed. An example is given to show that the proposed design procedures are very simple and efficient.
The problem of fuzzy optimal control for nonlinear time-delay system was considered based on the framework of T-S (Takagi-Sugeno) fuzzy model and parallel distributed compensation (PDC) controller. The controller proc...
详细信息
The problem of fuzzy optimal control for nonlinear time-delay system was considered based on the framework of T-S (Takagi-Sugeno) fuzzy model and parallel distributed compensation (PDC) controller. The controller procedure is to solve an optimization problem with linear matrix inequality (LMI) constraints whose feasible solution can be determined by efficient numerical algorithms. An example is given to demonstrate the application of the proposal design methods.
A new learning algorithm based on magnified Error is proposed to speedup the training of back-propagation neural networks, and to improve the performances of neural network. The key to this algorithm lies in varying t...
详细信息
ISBN:
(纸本)0780384032
A new learning algorithm based on magnified Error is proposed to speedup the training of back-propagation neural networks, and to improve the performances of neural network. The key to this algorithm lies in varying the error item of output layer, which magnify the backward propagated error signal especially when the weight adjustment of output layer is slow or even suppressed. Therefore, the algorithm is able to get rid of the influence of "flat spot" problem, and solve the slow convergence problem. Consequently the convergence rate can be accelerated, and the training has great capability in meeting the convergence criteria quickly with a simple network structure. Experiments on parity-3 problem and soybean data classification problem show that this method has advantages of faster learning speed and less computational cost than most of the improved algorithms such as sigmoid-prime offset technique (SPO), scaled linear approximation of sigmoid method (SLA) and so on.
作者：
Du, YihongSch. of Math.
Stat./Comp. Science University of New England Armidale NSW 2351 Australia
We consider the elliptic problem -Δu-λu=a(x)up, with p>1 and a(x) sign-changing. Under suitable conditions on p and a(x), we extend the multiplicity, existence and nonexistence results known to hold for this equa...
详细信息
CoGIS is a platform that supports multiple users to edit space data cooperatively in a distributed environment. By using this platform, users can request and retrieve space data remotely, edit and process space data c...
详细信息
One modification of the fast ignitor is the block ignitor for laser fusion it is similar to the Nuckolls-Wood's sch.me where PW-ps laser pulses ignite nearly uncompressed solid DT fuel, generating more than 100MJ ...
详细信息
ISBN:
(纸本)0894486861
One modification of the fast ignitor is the block ignitor for laser fusion it is similar to the Nuckolls-Wood's sch.me where PW-ps laser pulses ignite nearly uncompressed solid DT fuel, generating more than 100MJ fusion energy from 10kJ laser pulses by using relativistic electron beams. Avoiding the complexities of the electron beams, the block ignition uses nearly sub-relativistic ions produced by the nonlinear forces in the new skin layer interaction sch.me. In the skin layer interaction, it is essential to carefully control the prepulse, where a ten wavelength thick corona produces a swelling by a factor of about 3. The numerical treatment is using the genuine two-fluid model to compute details to understand how the nonlinear ponderomotive force generate blocks with the desired velocity for igniting.
This paper describes an analysis of spectral data transformations for selection of absorption features and spectral patterns that are sensitive to carbon and nitrogen content of diverse temperate pastures in southeast...
详细信息
This paper describes an analysis of spectral data transformations for selection of absorption features and spectral patterns that are sensitive to carbon and nitrogen content of diverse temperate pastures in southeastern Australia. This work used a database of spectra acquired from field campaigns together with wet chemistry estimates of biochemicals including lignin and cellulose, and near infrared spectroscopy estimates of crude protein from coincident field samples. The spectra were grouped in a matrix based on species composition, phenology, greenness and time of acquisition. Absorption features derived from first derivative transforms and continuum removal for known spectral bands showed some predictive potential for crude protein and cellulose. Predictive potential was enhanced if analysis was restricted to green grass-clover pasture samples. The methods require further refinement and testing before they are ported to analysis of airborne and satellite hyperspectral imagery.
Radial basis function (RBF) neural networks have been, extensively used for classification and regression due to the fact that they can provide fast linear algorithms to approximate any regular function. The most crit...
详细信息
ISBN:
(纸本)9783540220046
Radial basis function (RBF) neural networks have been, extensively used for classification and regression due to the fact that they can provide fast linear algorithms to approximate any regular function. The most critical issue in the construction of an RBF network for a given task is to determine the total number of radial basis functions, their centers and widths. Conventional methods of training an RBF network are to specify the radial basis function centers by searching for the optimal cluster centers of the training examples. This paper proposes a novel learning algorithm for construction of radial basis function by sensitive vectors (SenV), to which the output is the most sensitive. Our experiments are conducted on four benchmark datasets, and the results show that our proposed SenV-RBF classifier outperforms conventional RBFs and achieves the same level of accuracy as support vector machine.
暂无评论