Prediction of molar absorptivities of color reagents and their color reactions with yttrium by artificial neural networks

The new topological indices A(x1)-A(x3) suggested in our laboratories were applied to the study of structure-property relationships between color reagents and their color reactions with yttrium. The topological indices of twenty asymmetrical phosphone bisazo derivatives of chromotropic acid were calculated. The work shows that QSPR can be used as a novel aid to predict the molar absorptivities of color reactions and in the long term to be helpful tool in-color reagent design. Multiple regression analysis and neural network were employed simultaneously in this study. The results demonstrated the feasibility and the effectiveness of the method.

Evaluation of a combined wavelet and a combined principal component analysis classification system for BCG diagnostic problem

Heart disease is one of the main factor causing death in the developed countries. Over several decades, variety of electronic and computer technology have been developed to assist clinical practices for cardiac performance monitoring and heart disease diagnosis. Among these methods, Ballistocardiography (BCG) has an interesting feature that no electrodes are needed to be attached to the body during the measurement. Thus, it is provides a potential application to asses the patients heart condition in the home. In this paper, a comparison is made for two neural networks based BCG signal classification models. One system uses a principal component analysis (PCA) method, and the other a discrete wavelet transform, to reduce the input dimensionality. It is indicated that the combined wavelet transform and neural network has a more reliable performance than the combined PCA and neural network system. Moreover, the wavelet transform requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced.

基于神经网络的机器人视觉伺服控制

视觉伺服可以应用于机器人初始定位自动导引、自动避障、轨线跟踪和运动目标跟踪等控制系统中。传统的视觉伺服系统在运行时包括工作空间定位和动力学逆运算两个过程,需要实时计算视觉雅可比矩阵和机器人逆雅可比矩阵,计算量大,系统结构复杂。本文分析了基于图像的机器人视觉伺服的基本原理,使用BP神经网络来确定达到指定位姿所需要的关节角度,将视觉信息直接融入伺服过程,在保证伺服精度的情况下大大简化了控制算法。文中针对Puma560工业机器人的模型进行了仿真实验,结果验证了该方法的有效性。

基于神经网络的一类非线性系统模型参考控制

针对一类非线性系统,提出了一种神经网络模型参考控制方案。在训练实现对象模型的网络和实现控制器的网络时,由状态方程产生训练样本。通过对倒立摆系统的仿真实验验证了控制方案和样本生成策略的有效性,在仿真实验中用不同初始状态验证了训练后的神经网络的泛化能力。