Design for CNN Templates with Performance of Global Connectivity Detection

The cellular neural/nonlinear network (CNN) is a powerful tool for image and video signal processing, robotic and biological visions. This paper discusses a general method for designing template of the global connectivity detection (GCD) CNN, which provides parameter inequalities for determining parameter intervals for implementing the corresponding functions. The GCD CNN has stronger ability and faster rate for determining global connectivity in binary patterns than the GCD CNN proposed by Zarandy. An example for detecting the connectivity in complex patterns is given.     

Pattern recognition system for the discrimination of multiple sclerosis from cerebral microangiopathy lesions based on texture analysis of magnetic resonance images

In this study, a pattern recognition system has been developed for the discrimination of multiple sclerosis (MS) from cerebral microangiopathy (CM) lesions based on computer-assisted texture analysis of magnetic resonance images. Twenty-three textural features were calculated from MS and CM regions of interest, delineated by experienced radiologists on fluid attenuated inversion recovery images and obtained from 11 patients diagnosed with clinically definite MS and from 18 patients diagnosed with clinically definite CM. The probabilistic neural network classifier was used to construct the proposed pattern recognition system and the generalization of the system to unseen data was evaluated using an external cross validation process. According to the findings of the present study, statistically significant differences exist in the values of the textural features between CM and MS: MS regions were darker, of higher contrast, less homogeneous and rougher as compared to CM.     

深浅层特征及模型融合的说话人识别

为了进一步提高说话人识别系统的性能,提出基于深、浅层特征融合及基于I-Vector的模型融合的说话人识别。基于深、浅层特征融合的方法充分考虑不同层级特征之间的互补性,通过深、浅层特征的融合,更加全面地描述说话人信息;基于I-Vector模型融合的方法融合不同说话人识别系统提取的I-Vector特征后进行距离计算,在系统的整体结构上综合了不同说话人识别系统的优势。通过利用CASIA南北方言语料库进行测试,以等错误率为衡量指标,相比基线系统,基于深、浅层特征融合的说话人识别其等错误率相对下降了54.8%,基于I-Vector的模型融合的方法其等错误率相对下降了69.5%。实验结果表明,深、浅层特征及模型融合的方法是有效的。     

RBFNN结合自适应边界的机器人手臂轨迹跟踪控制系统设计

针对机器人手臂动态模型中存在动态不确定性问题,提出一种结合径向基函数神经网络(RBFNN)和自适应边界控制的机械臂轨迹跟踪方法。利用RBF神经网络在线学习系统中现有的结构化和非结构化不确定性,近似补偿未知动态部分;利用自适应边界来估计非结构化不确定性上的未知边界和神经网络重建误差;通过加权矩阵产生的李雅普诺夫函数证明了该系统具有渐进稳定性。利用三自由度机械臂进行实验,结果表明,相比其他几种较为先进的控制器,本文设计的控制器具有最优的控制精度。