有效汇率、贸易收支与人民币升值

要求人民币升值的理由之一就是人民币实际汇率存在低估．人为地对人民币升值将对我国经济违成非常大的负面影响。通过对人民币实际有效汇率的估计和对我国贸易收支以及国际储备的考察证明人民币汇率不存在低估。同时提出相应的政策建议来缓解人民币升值压力,并对现行人民币汇率制度进行改革．     

Short-term load forecasting based on fuzzy neural network

The fuzzy neural network is applied to the short-term load forecasting. The fuzzy rules and fuzzy membership functions of the network are obtained through fuzzy neural network learming. Three inference algorithms, i.e. themultiplicative inference, the maximum inference and the minimum inference, are used for comparison. The learningalgorithms corresponding to the inference methods are derived from back-propagation algorithm. To validate the fuzzyneural network model, the network is used to Predict short-term load by compaing the network output against the realload data from a local power system supplying electricity to a large steel manufacturer. The experimental results aresatisfactory.     

应用聚类和模糊神经网络设计模糊系统

基于聚类技术和一类模糊神经网络提出一种设计模糊系统的混合方法,通过一个无监督的聚类算法自组织地确定模糊规则的数目及生成一个初始的模糊规则库,构建一类模糊神经网络,通过调整网络的权值,使规则库中的参数更加准确,并以函数逼近问题为例验证了该方法的有效性。     

变系数EV模型在人民币汇率中的应用

将自适应变系数EV模型在计算机模拟实验的基础上推广到人民币汇率应用中,并利用该模型对人民币美元汇率的月度数据进行拟合预测.实证研究结果表明:自适应变系数EV模型的拟合效果优于FAR模型、AFAR模型的拟合效果,其预测精度也是这几种模型中最优的,可以为研究人民币美元汇率今后的趋势提供帮助.     

EFNN一种增强型模糊神经网络

提出了一种较为广义的增强型模糊神经网络,以达到更高的非线性系统逼近能力。该网络模糊规则的结论以函数形式给出,从而决定了网络的结构由两个网络组成,即特征网络和功能网络。网络采用梯度算法来修正网络的参数。仿真表明：该网络具有较强的非线性逼近能力和较快的学习速度。     

采用神经网络与模糊曲线相结合的短期电力负荷预测方法

文章提出了一种神经网络(ANN)与模糊曲线(Fuzzy Curve)有机结合的短期负荷预测方法,该方法采用ANN作为基本负荷预测,再用模糊曲线考虑影响负荷变化的因素(如天气的迅速变化、重大节假日等),对基本负荷预测做出修正,从而得到最终的负荷预测值。同时,针对传统BP学习算法的缺点,将BP算法和模拟退火算法的优点相结合以提高网络的学习性能。实例表明,该ANN—FC模型实用有效、精度高。     

Approximation capability of regular fuzzy neural networks to continuous fuzzy functions

The approximation capability of regular fuzzy neural networks to fuzzy functions is studied. When σ is a nonconstant, bounded and continuous function on R, some equivalent conditions are obtained, with which continuous fuzzy functions can be approximated to any degree of accuracy by the four-layer feedforward regular fuzzy neural networks.Finally a few examples of such fuzzy functions are given.     

模糊与神经网络相结合的控制方法

介绍了一种模糊与神经网络相结合的控制方法,借以克服模糊控制存在稳态偏差的不足和神经网络对小偏差信号不敏感的缺陷。对复杂的具有大惯性、大迟延热工对象的仿真研究表明,该控制方法较其它控制方法不仅改善了系统的动态性能,并在一定程度上克服了热工对象的时变对控制效果影响。     

高阶模糊BP神经网络及其在滚动轴承故障诊断中的应用

将模糊逻辑理论与高阶BP神经网络结合起来,讨论了高阶模糊BP神经网络的结构、特点、二阶算法以及隶属函数的确定。并将该神经网络模型用于滚动轴承的故障诊断中,试验表明高阶模糊BP神经网络对轴承故障模式具有稳定、准确的识别能力,是一种行之有效的诊断方法。     

Using genetic algorithm based fuzzy adaptive resonance theory for clustering analysis

In the clustering applications field, fuzzy adaptive resonance theory system has been widely applied. But, three parameters of fuzzy adaptive resonance theory need to be adjusted manually for obtaining better clustering. It needs much time to test and does not assure a best result. Genetic algorithm is an optimal mathematical search technique based on the principles of natural selection and genetic recombination. So, to make the fuzzy adaptive resonance theory parameters choosing process automation, an approach incorporating genetic algorithm and fuzzy adaptive resonance theory neural network has been applied. Then, the best clustering result can be obtained.Through experiment, it can be proved that the most appropriate parameters of fuzzy adaptive resonance theory can be gained effectively by this approach.