一种新的多智能体强化学习算法及其在多机器人协作任务中的应用

在多机器人系统中 ,评价一个机器人行为的好坏常常依赖于其它机器人的行为 ,此时必须采用组合动作以实现多机器人的协作 ,但采用组合动作的强化学习算法由于学习空间异常庞大而收敛得极慢 .本文提出的新方法通过预测各机器人执行动作的概率来降低学习空间的维数 ,并应用于多机器人协作任务之中 .实验结果表明 ,基于预测的加速强化学习算法可以比原始算法更快地获得多机器人的协作策略 .

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.