The controller takes full advantage of the features of ti tms3201f240xa processor , such as high performance processing capabilities , plenty of peripherals on chip and high real time control . by the vital of these features the fast ac sampling , synchronic pulse , digital shift phase trigger pulse , can bus communication , pid control method and excitation limitations are realized . the pretreatment of the switch signals and the test of the shift phase trigger pulses are realized by using the complex programmable device ( cpld ) 该励磁控制器充分发挥了tms3201f240xadsp处理器的强大的数据运算能力、丰富的片内外设资源和较强实时控制能力，用单片系统结构实现了快速交流采样，数字同步脉冲形成， can现场总线网络、以及pid控制算法和各种励磁限制等功能；用cpld器件实现了开关量抖动预处理和可控硅触发脉冲遗漏的检测，减轻了dsp处理器负担，提高了励磁控制器可靠性、抗干扰能力和控制能力；使用vc + + 6 . 0开发的基于windows操作系统的励磁系统调试软件，人机界面简洁友好，使用操作灵活方便。
Firstly , the paper , combining the characteristic of synchronous pulse bursts and inhibition with the modified pcnn model , presents a way of finding the foveation points in the images adaptively and effectively , and simulates the human vision system . secondly , pcnn is extended to pcnns , based on the properties of information couple and transmission , an algorithm that is used to fuse images of the same target got by several sensors to an image is presented to simulate the human vision system . thirdly , combining the properties of synchronous pulse bursts , capture , and transmission and competition of waves , the paper presents two ways of classification , one is an algorithm based on the properties of neuron to capture and inhibit to classify the data taking on any complex unlinear distribution robustly , the other is based on the restricted distance and modified of the former to remove the influence of inferior samples in classification ; fin ally , based on the accumulative difference pictures , and the forming and transmission of pcnn wave , selecting and controlling the direction of autowave by connecting the neighbouring neurons selectively , the paper presents a way to simulate the tracks of moving object and detect the moving direction 首先结合pcnn的同步脉冲发放和侧抑制特性，提出了基于改进型pcnn的图像凹点检测算法，该算法是一种自适应而有效的图像凹点检测方法，并且较好地仿真了人类视觉系统；然后，结合信息传递和信息耦合特性，将pcnn扩展成pcnns ( pcnn网络群) ，提出了一种基于pcnns的图像融合算法，能够将多个传感器获取的同一目标的图像信息融合到一幅图像中，有效模拟了人类视觉系统；另外，结合pcnn的同步脉冲发放特性、捕获特性和波的传播竞争特性，开拓地将pcnn用于模式分类中，提出了基于耦合神经元点火捕获抑制特性的分类方法和改进的约束距离下的pcnn分类方法，前者可实现对样本空间中任意复杂分布训练样本的稳健非线性分类，而后者能够消除训练样本中刺点对分类的影响；最后，结合累积差分图像思想、 pcnn波的形成与传播特性，通过各神经元之间连接取向来选择与控制自动波的流向，将pcnn用于运动视觉分析中的运动轨迹模拟及运动方向检测。
