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51.
The measure of uncertainty is adopted as a measure of information. The measures of fuzziness are known as fuzzy information measures. The measure of a quantity of fuzzy information gained from a fuzzy set or fuzzy system is known as fuzzy entropy. Fuzzy entropy has been focused and studied by many researchers in various fields. In this paper, firstly, the axiomatic definition of fuzzy entropy is discussed. Then, neural networks model of fuzzy entropy is proposed, based on the computing capability of neural networks. In the end, two examples are discussed to show the efficiency of the model. 相似文献
52.
采用AMI方法研究了10种新型的磺酰脲类除草剂的电子结构,并以原子的Mulliken净电荷和除草剂在不同浓度(100,10mg/L)下对油菜、稗草两种作物的根、茎部位的抑制率为训练样本集。构造并训练得到具有活性预测能力的BP神经网络.结果表明,该BP网络不仅能对训练样本很好拟合。亦能对未知化合物的活性作出很好的预测. 相似文献
53.
54.
面对多样化的市场需求,企业的销售渠道也变得异常复杂,利用网络图可以简化实际问题,并能形象地描述它。本根据网络知识建立了产品分销模型,探求企业在市场需求固定和随机时的最优收益。最后,章还进行实例验算,并做出相应的结果分析。 相似文献
55.
Kojima T. Inoue E. Tsuchiya M. Ishimaru K. 《Journal of Thermal Analysis and Calorimetry》2003,72(2):737-742
TG, swelling, and viscometric studies are presented for the residues, PTHF/IPDI polymer networks, and the extracts, the linear
polymers and unreacted IPDI, after Soxhlet extraction of PTHF/IPDI reaction products. The products are obtained by reacting
PTHF with 650, 1400, (2×650+1×2900), or 2900 of molecular mass with IPDI at various concentrations in bulk. The results on
the swelling and the viscosity experiments suggest that the PTHF/IPDI reaction products have a usual expectable structure.
All the TG curves are a double stage curve. The initial stage and the last stage seem to reflect decomposition of PTHF chains
and vaporization of the remainder, IPDI, respectively. These are analyzed by a trial-and-error construction, supposing double
event behavior. The values of ratio of mass loss associated with the initial event, W01, to the mass loss associated with the last event, W02, are smaller than the expectable those. This suggests that Event 2 involves vaporization of the decomposition products of
PTHF moieties bonded to IPDI in addition to vaporization of IPDI.
This revised version was published online in July 2006 with corrections to the Cover Date. 相似文献
56.
Vladimir E. Bondarenko 《Complexity》2005,11(2):39-52
Information processing and two types of memory in an analog neural network model with time delay that produces chaos similar to the human and animal EEGs are considered. There are two levels of information processing in this neural network: the level of individual neurons and the level of the neural network. Similar to the state of brain, the state of chaotic neural network is defined. It is characterized by two types of memories (memory I and memory II) and correlation structure between the neurons. In normal (unperturbed) state, the neural network generates chaotic patterns of averaged neuronal activities (memory I) and patterns of oscillation amplitudes (memory II). In the presence of external stimulation, the activity patterns change, showing changes in both types of memory. As in experiments on stimulation of the brain, the neural network model shows synchronization of neuronal activities due to stimulus measured by Pearson's correlation coefficient. An increase in neural network asymmetry (increase of the neural network excitability) leads to the phenomenon similar to the epilepsy. Modeling of brain injury, Parkinson's disease, and dementia is performed by removing and weakening interneuron connections. In all cases, the chaotic neural network shows a decrease of the degree of chaos and changes in both types of memory similar to those observed in experiments with healthy human subjects and patients with Parkinson's disease and dementia. © 2005 Wiley Periodicals, Inc. Complexity 11:39–52, 2005 相似文献
57.
58.
资源有限网络计划的PRWI启发式优化方法 总被引:1,自引:0,他引:1
本文在综合考虑了有资源约束的网络计划结构特征、资源强度、时间约束等方面因素的基础上,提出了一种新的资源优化的启发式优化方法—PRWI方法,并通过分析证明了该方法处理问题的效果较现有的其它方法好。 相似文献
59.
Andrew V. Goldberg Michael D. Grigoriadis Robert E. Tarjan 《Mathematical Programming》1991,50(1-3):277-290
Goldfarb and Hao (1990) have proposed a pivot rule for the primal network simplex algorithm that will solve a maximum flow problem on ann-vertex,m-arc network in at mostnm pivots and O(n
2
m) time. In this paper we describe how to extend the dynamic tree data structure of Sleator and Tarjan (1983, 1985) to reduce the running time of this algorithm to O(nm logn). This bound is less than a logarithmic factor larger than those of the fastest known algorithms for the problem. Our extension of dynamic trees is interesting in its own right and may well have additional applications.Research partially supported by a Presidential Young Investigator Award from the National Science Foundation, Grant No. CCR-8858097, an IBM Faculty Development Award, and AT&T Bell Laboratories.Research partially supported by the Office of Naval Research, Contract No. N00014-87-K-0467.Research partially supported by the National Science Foundation, Grant No. DCR-8605961, and the Office of Naval Research, Contract No. N00014-87-K-0467. 相似文献
60.
提出了一种用于修正光学神经网络硬件系统误差的虚拟神经网络模型,光学实验结果表明该网络能够有效地消除硬件系统误差对实验结果的影响。 相似文献