基于局域网的多水下机器人仿真系统设计与实现

多水下机器人仿真系统是一个能够对多水下机器人系统的体系结构、协调控制、路径规划、学习算法等进行演示验证的分布式实时数字仿真系统,是开展多水下机器人技术研究的基础和有效手段.讨论了应用基于局域网的分布式仿真技术来解决多水下机器人系统仿真的问题,并详细说明了仿真系统的硬件组成和软件总体设计.

基于模糊神经网络的多移动机器人自学习协调系统

研究多移动机器人的运动规划问题．针对机器人模型未知或不精确以及环境的动态变化，提出一种自学习模糊控制器（ＦＬＣ）来进行准确的速度跟踪．首先通过神经网络的学习训练构造ＦＬＣ，再由再励学习算法来在线调节ＦＬＣ的输出，以校正机器人运动状态，实现安全协调避撞

工业机器人的小脑模型控制

本文为动力学控制工业机器人提出了一种综合学习算法,这种学习算法可将以前所学的信息用于新的控制输入.这种控制方法不需要事先知道机器人动力学,它易于应用于特殊的控制问题或修改以适应实际系统中的变化,控制方法在时间上是有效的,且很适合于定点实现.学习控制算法的有效性通过4自由度的直接驱动机器人前两个关节在重复运动中的计算机仿真实验得到了验证.

智能机器人基于作业子空间划分的自学习自适应控制方法

现有的机器人自适应控制基本上都是在建立机器人线性化的动力学模型的基础上,采用某种显式或隐式参数辨识的方法,在线地修正控制作用.本文针对机器人运动和动力学参数变化的固有特点,提出一种完全不同的自学习自适应方法.这种方法基于智能机器人分级系统中的两级结构,并且在空间域里而不是在时间域里处理机器人参数的变化.把机器人的作业空间划分成子空间,其中包括重力载荷的作用,每个子空间对应一组控制器.规划的轨迹映射到作业空间形成子空间序列.用自学习方法选择与这个序列对应的最佳控制器序列.该方法算法简单,计算量小.避开了通常的自适应方法遇到的一系列困难问题.

基于克隆选择算法的非线性优化迭代学习控制；Clonal selection algorithm based nonlinear optimal iterative learning control

为实现对模型不确定的有约束非线性系统在特定时间域上输出轨迹的有效跟踪,将改进的克隆选择算法用于求解迭代学习控制中的优化问题。提出基于克隆选择算法的非线性优化迭代学习控制。在每次迭代运算后,一个克隆选择算法用于求解下次迭代运算中的最优输入,另一个克隆选择算法用于修正系统参考模型。仿真结果表明,该方法比GA-ILC具有更快的收敛速度,能够有效处理输入上的约束以及模型不确定问题,通过少数几次迭代学习就能取得满意的跟踪效果。

Visual pattern recognition network: Its training algorithm and its optoelectronic architecture

A visual pattern recognition network and its training algorithm are proposed. The network constructed of a one-layer morphology network and a two-layer modified Hamming net. This visual network can implement invariant pattern recognition with respect to image translation and size projection. After supervised learning takes place, the visual network extracts image features and classifies patterns much the same as living beings do. Moreover we set up its optoelectronic architecture for real-time pattern recognition. (C) 1996 Optical Society of America

Iris recognition algorithm based on point covering of high-dimensional space and neural network

In this paper, we constructed a Iris recognition algorithm based on point covering of high-dimensional space and Multi-weighted neuron of point covering of high-dimensional space, and proposed a new method for iris recognition based on point covering theory of high-dimensional space. In this method, irises are trained as "cognition" one class by one class, and it doesn't influence the original recognition knowledge for samples of the new added class. The results of experiments show the rejection rate is 98.9%, the correct cognition rate and the error rate are 95.71% and 3.5% respectively. The experimental results demonstrate that the rejection rate of test samples excluded in the training samples class is very high. It proves the proposed method for iris recognition is effective.

Viruses' complete genomes classification based on geometrical learning

In this paper, we proposed a method of classification for viruses' complete genomes based on graph geometrical theory in order to viruses classification. Firstly, a model of triangular geometrical graph was put forward, and then constructed feature-space-samples-graphs for classes of viruses' complete genomes in feature space after feature extraction and normalization. Finally, we studied an algorithm for classification of viruses' complete genomes based on feature-space-samples-graphs. Compared with the BLAST algorithm, experiments prove its efficiency.

Cancer subtypes recognition and its gene expression profiles analysis based on geometrical learning

The accurate recognition of cancer subtypes is very significant in clinic. Especially, the DNA microarray gene expression technology is applied to diagnosing and recognizing cancer types. This paper proposed a method of that recognized cancer subtypes based on geometrical learning. Firstly, the cancer genes expression profiles data was pretreated and selected feature genes by conventional method; then the expression data of feature genes in the training samples was construed each convex hull in the high-dimensional space using training algorithm of geometrical learning, while the independent test set was tested by the recognition algorithm of geometrical learning. The method was applied to the human acute leukemia gene expression data. The accuracy rate reached to 100%. The experiments have proved its efficiency and feasibility.

Robust Learning Control for Manipulators with Uncertainty Disturbances Based on Internal Model

In this paper, a disturbance controller is designed for making robotic system behave as a decoupled linear system according to the concept of internal model. Based on the linear system, the paper presents an iterative learning control algorithm to robotic manipulators. A sufficient condition for convergence is provided. The selection of parameter values of the algorithm is simple and easy to meet the convergence condition. The simulation results demonstrate the effectiveness of the algorithm..