Perceptron training algorithms designed using discrete-time control Liapunov functions

Perceptrons, proposed in the seminal paper McCulloch–Pitts of 1943, have remained of interest to neural network community because of their simplicity and usefulness in classifying linearly separable data and can be viewed as implementing iterative procedures for “solving” linear inequalities. Gradient descent and conjugate gradient methods, normally used for linear equalities, can be used to solve linear inequalities by simple modifications that have been proposed in the literature but not been analyzed completely. This paper applies a recently proposed control-inspired approach to the design of iterative steepest descent and conjugate gradient algorithms for perceptron training in batch mode, by regarding certain parameters of the training/algorithm as controls and then using a control Liapunov technique to choose appropriate values of these parameters.     

Predictive models for emission of hydrogen powered car using various artificial intelligent tools

This paper investigates the use of artificial intelligent models as virtual sensors to predict relevant emissions such as carbon dioxide, carbon monoxide, unburnt hydrocarbons and oxides of nitrogen for a hydrogen powered car. The virtual sensors are developed by means of application of various Artificial Intelligent (AI) models namely; AI software built at the University of Tasmania, back-propagation neural networks with Levenberg–Marquardt algorithm, and adaptive neuro-fuzzy inference systems. These predictions are based on the study of qualitative and quantitative effects of engine process parameters such as mass airflow, engine speed, air-to-fuel ratio, exhaust gas temperature and engine power on the harmful exhaust gas emissions. All AI models show good predictive capability in estimating the emissions. However, excellent accuracy is achieved when using back-propagation neural networks with Levenberg–Marquardt algorithm in estimating emissions for various hydrogen engine operating conditions with the predicted values less than 6% of percentage average root mean square error.     

一种新的非线性共轭梯度法及收敛性

在LS方法基础上,提出了一种新的求解无约束最优化问题的共轭梯度法.新方法通过一个新的公式计算参数,克服了LS方法的数值效果不稳定和收敛性弱的缺点,并且在强Wolfe线搜索下证明了该方法具有充分下降性和全局收敛性.大量的数值试验表明新方法是稳定的、有效的.     

Descent three-term conjugate gradient methods based on secant conditions for unconstrained optimization

The conjugate gradient method is an effective method for large-scale unconstrained optimization problems. Recent research has proposed conjugate gradient methods based on secant conditions to establish fast convergence of the methods. However, these methods do not always generate a descent search direction. In contrast, Y. Narushima, H. Yabe, and J.A. Ford [A three-term conjugate gradient method with sufficient descent property for unconstrained optimization, SIAM J. Optim. 21 (2011), pp. 212–230] proposed a three-term conjugate gradient method which always satisfies the sufficient descent condition. This paper makes use of both ideas to propose descent three-term conjugate gradient methods based on particular secant conditions, and then shows their global convergence properties. Finally, numerical results are given.     

采煤机无线监测装置压电换能元件参数匹配分析

考虑到压电自供电装置中压电振子的几何参数直接影响其共振频率,从而影响压电自供电装置的发电量,因此需要根据环境激励频率设定最佳的压电振子的几何参数,对基于压电自供电的采煤机状态无线监测装置进行研究,通过理论分析、仿真以及实验方法对对不同悬臂梁结构在不同外界激励作用下的发电量进行研究.研究结果表明:悬臂梁自由端的质量块质量以及悬臂梁长度越大,悬臂梁压电结构的固有频率越低;随着悬臂梁厚度的增加,悬臂梁压电结构的固有频率逐渐增大.以此为依据对采煤机不同监测位置的装置选取最佳匹配的压电结构几何参数,并通过采煤机工作状态监测实验验证了本文研究的压电自供电无线监测装置的可行性.     

基于压电自供电的采煤机状态无线监测装置

考虑到压电自供电装置中压电振子的几何参数直接影响其共振频率,从而影响压电自供电装置的发电量,因此需要根据环境激励频率设定最佳的压电振子的几何参数,本文对基于压电自供电的采煤机状态无线监测装置进行研究,通过理论分析、仿真以及实验方法对对不同悬臂梁结构在不同外界激励作用下的发电量进行研究。研究结果表明：悬臂梁自由端质量块质量以及悬臂梁长度越大,悬臂梁压电结构的固有频率越低;随着悬臂梁厚度的增加,悬臂梁压电结构的固有频率逐渐增大。以此为依据对采煤机不同监测位置的装置选取最佳匹配的压电结构几何参数,并通过采煤机工作状态监测实验验证了本文研究的压电自供电无线监测装置的可行性。     

自供电磁阻车辆检测节点设计

针对无线车辆信息采集系统寿命受制于节点功耗的问题,设计了一种基于各向异性磁阻传感器的自供电车辆检测节点。节点采用电源动态管理,设计了快速信号采集电路,结合低功耗芯片的选用以及执行睡眠等方法来控制节点功耗。为减少通信次数和保证可靠通信,针对应用定制了时分多址加跳频的无线通信协议。节点采用超级电容加充电锂电池的存储结构收集太阳能,通过软硬件协同设计实现了简单高效的能量管理方法,具有供电源自动切换和充电控制功能。实验测试的结果表明该节点能够胜任车流量检测应用,自供电能够满足节点可持续工作的需要。     

XCSG在多机器人强化学习中的应用

XCS分类器在解决机器人强化学习方面已显示出较强的能力,但在多机器人领域仅局限于MDP环境,只能解决环境空间较小的学习问题。提出了XCSG来解决多机器人的强化学习问题。XCSG建立低维的逼近函数,梯度下降技术利用在线知识建立稳定的逼近函数,使Q-表格一直保持在稳定低维状态。逼近函数Q不仅所需的存储空间更小,而且允许机器人在线对已获得的知识进行归纳一般化。仿真实验表明,XCSG算法很好地解决了多机器人学习空间大、学习速度慢、学习效果不确定等问题。     

Adaptive soft k-nearest-neighbour classifiers

A novel classifier is introduced to overcome the limitations of the k-NN classification systems. It estimates the posterior class probabilities using a local Parzen window estimation with the k-nearest-neighbour prototypes (in the Euclidean sense) to the pattern to classify. A learning algorithm is also presented to reduce the number of data points to store. Experimental results in two hand-written classification problems demonstrate the potential of the proposed classification system.     

基于TSK模糊控制系统的网络异常检测

在网络异常检测中,为了提高对异常状态的检测率,降低对正常状态的误判率,该文提出利用TSK模糊控制系统进行网络异常检测的新方法。在对TSK模糊控制系统的训练中采取梯度下降算法,充分发挥梯度下降局部细致搜索优势。实验数据采用KDDCUP99数据集,实验结果表明,基于梯度下降的模糊控制系统提高了异常检测的准确性。