基于人工神经元网络的电力系统短期负荷预报的实用计算

本文针对贵州电力系统日负荷的特点，提出了一种利用人工神经网络（ＡＮＮ）的ＢＰ模型进行电力系统提前２４小时的短期负荷预报的实用计算方法。进一步对ＢＰ模型的隐合层数和隐合节点数的选取进行定性的分析，同时探讨学习因子和动量项对学习收敛的影响分析，预测方法及结果得到肯定。     

生物活性材料义眼台

本文采用羟基磷灰石与生物活性玻璃制做义眼台。用液态化学反应法合成羟基磷灰石，取适宜数量的生物活性玻璃与之混合，经高温烧结制成义眼台，并进行了动物实验和临床应用。动物实验表明材料具有优异的生物相容性和一定的生物活性，临床应用效果良好。本材料是制做义眼台的新型材料。本文就材料研究、动物实验、义眼台的制做工艺进行了探讨。     

神经网络解耦控制系统的分析

本文基于神经网络的理论研究提出了一种新的神经网络解耦控制方法，该方法以被控对象的一组实测数据作为样本，训练出用神经网络描述的逆动力学模型，并将它作为解耦控制器投入到控制系统中．对某二元精馏塔控制的仿真结果表明该方法是切实可行的．     

用开关电容实现Hopfield网络的一种新方法

提出了利用开关电容网络实现连续的Ｈｏｐｆｉｅｌｄ神经网络的一种新方法，它便于单片集成，实现的网络结构具有对寄生参数不灵敏的优点     

神经网络反传模型的理论与应用研究体会

神经网络及神经计算的新理论，模型，方法及应用受重视，并用于解决某些诸如模式识别，组合优化，语言理解等复杂问题，得到系列可喜的理论成查与实际应用。我们业已将神经网络反传模型应用于多元校正，有机合成，模式识别，构效关系及分子设计等方面。     

数据挖掘与人工智能技术

数据挖掘是人工智能中的一个重要分支。对人工智能的发展及其最基本的技术：知识表示、知识推理、搜索，以及数据挖掘进行了详细介绍。通过实例给出了数据挖掘的定义，最后论述了数据挖掘与人工智能的联系，并结合国内外在该领域的研究成果指出了其广阔的发展前景。     

织物透湿性能测试与MATLAR应用研究

对各种织物透湿性能进行测试与分析．利用MATLAB6．5建立神经网络模型，选择最佳网络参数并对网络进行训练．通过应用实例提出了如何处理实验数据的方法，并通过建立大量的BP网络进行比较，筛选出最具应用价值的网络对透湿性能进行预测．     

Modelling the soundscape quality of urban waterfronts by artificial neural networks

The renewal of the urban waterfronts has become a major focus of attention for politicians and decision makers in the city’s management programs. The recognition of the patterns that define the waterfronts’ identity is essential to select new strategies of intervention for the environmental recovery. In order to create adequate environments for everyday life within a sustainable development, new links between human senses, human perception and design need to be created. Within this wide approach, the landscape and the soundscape play a significant role and can become a key driving force in the implementation of the changes. New techniques have to be tested to identify the sonic and visual parameters capable to explain the specificity of a waterfront. With this purpose, an artificial neural network (ANN) was developed, and the relative importance of the input variables was evaluated. The collected database was also analysed by multiple linear regression (MLR) to compare the outcomes of both models. The urban waterfront of Naples (Italy) was chosen as case study. The results obtained show that the performance of the neural network is better than the one of the linear regression (rANN = 0.949, rMLR = 0.639). The interpretation of the relative importance method is also quite satisfactory in the ANN.     

A note on the paper “characterization of all optimal solutions and parametric maximal flows in networks” of G. Ruhe (optimization 16 (1985) 1, 51-61)

First it is shown that the implementation of the algorithm proposed in the considered paper may cause some problems if no further specification is made as to one of its steps. Secondly, such a specification is suggested.