Numeral eddy current sensor modelling based on genetic neural network

This paper presents a method used to the numeral eddy current sensor modelling based on the genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced. In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network （GNN） according to measurement data. So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network. The nonlinear model has the advantages of strong robustness, on-line modelling and high precision. The maximum nonlinearity error can be reduced to 0.037% by using GNN. However, the maximum nonlinearity error is 0.075% using the least square method.     

Synchronization of stochastically hybrid coupled neural networks with coupling discrete and distributed time-varying delays

A general model of linearly stochastically coupled identical connected neural networks with hybrid coupling is proposed, which is composed of constant coupling, coupling discrete time-varying delay and coupling distributed timevarying delay. All the coupling terms are subjected to stochastic disturbances described in terms of Brownian motion, which reflects a more realistic dynamical behaviour of coupled systems in practice. Based on a simple adaptive feedback controller and stochastic stability theory, several sufficient criteria are presented to ensure the synchronization of linearly stochastically coupled complex networks with coupling mixed time-varying delays. Finally, numerical simulations illustrated by scale-free complex networks verify the effectiveness of the proposed controllers.     

涡流检测的数值模拟与缺陷定量分析

随着涡流检测技术的不断发展，不仅要求准确检测出缺陷，且还需对缺陷进行定量、定性评价。涡流检测中阻抗信号变化是进行缺陷检测和定量分析的依据：测量或计算缺陷的阻抗信号，称为涡流检测的正向问题；从阻抗信号推断出缺陷的定量定性特征，则是逆向问题。借助于有限元模拟方法，对涡流检测时的正向问题进行了求解计算；利用傅里叶变换和神经网络方法，对涡流检测时的逆向问题进行了分析。     

降低轴流风机噪声的两种方法

本文对轴流风机的噪声源进行了理论和实验分析,对降低风机噪声的方法进行了总结。在此基础上,研究了降低风机噪声的两种新方法——叶片不等距分布方法和叶片穿孔方法。理论分析和实验结果表明,这两种方法为降低轴流风机噪声提供了新的途径。     

解线性不等式的神经网络

本文提出两个解线性不等式的Hopfiedl-Tank型的神经网络。第一个网络模拟同时松弛投影方法，第二个网络是二次规划方法。当线性不等式的解集非空时，这两个方法都给出该线性不等式的解。同时我们还给出了这两个网络的数值模拟。     

一类发展型方程对时间离散化的解的误差估计

研究了一类带初边值问题的发展方程关于时间的离散化，从而把它转化为椭圆型边值问题，得到了近似解的误差估计。     

人工神经网络用于化学数据解析的研究（I）：逼近规律与过拟合

对多层前传网络的过拟合问题进行了探讨，定义了逼近误差和逼近度作为人工神经网络（ＡＮＮ）的建模评价指标，通过应用于多元非线性校正的数值模拟和实际药物光度分析数据解析，表明该指示明确，便于掌握，且能较好的地定量表述ＡＮＮ逼近规律的程度。     

关于一类连续最优控制问题的一种可实现的离散方法

本文对具有状态终端约束、控制受限的非线性连续最优控制问题给出一种新的可实现的离散方法，此方法通过求解非线最小二乘问题避免这类问题离散后出现的不可行现象，文中给出这种做法的理论证明和实现方案。