生物质改性纳米TiO2的制备及其结构表征

采用溶胶-凝胶法制备了系列生物质改性复合纳米TiO2.以亚甲基蓝溶液为模拟污染物,考察了其可见光催化活性,并确定了最佳制备工艺.通过X射线光电子能谱(XPS)、X射线衍射(XRD)、场发射扫描电镜(FESEM)、紫外-可见漫反射光谱(UV-Vis-DRS)、荧光光谱(PL)等手段对催化剂样品进行了表征.实验结果表明,催化剂对亚甲基蓝的光催化降解适应一级反应动力学,复合TiO2和纯TiO2的反应速率常数分别为0.4990 h-1和0.0305 h-1,且复合催化剂实现了C、N、S、P、K等多元素的共掺杂.相比纯TiO2,复合TiO2的比表面积增大,结晶度升高,光生载流子复合率降低,吸收边带红移,禁带宽度窄化了0.09 eV.     

Pulse coding off-chip learning algorithm for memristive artificial neural network

Memristive neural network has attracted tremendous attention since the memristor array can perform parallel multiply-accumulate calculation (MAC) operations and memory-computation operations as compared with digital CMOS hardware systems. However, owing to the variability of the memristor, the implementation of high-precision neural network in memristive computation units is still difficult. Existing learning algorithms for memristive artificial neural network (ANN) is unable to achieve the performance comparable to high-precision by using CMOS-based system. Here, we propose an algorithm based on off-chip learning for memristive ANN in low precision. Training the ANN in the high-precision in digital CPUs and then quantifying the weight of the network to low precision, the quantified weights are mapped to the memristor arrays based on VTEAM model through using the pulse coding weight-mapping rule. In this work, we execute the inference of trained 5-layers convolution neural network on the memristor arrays and achieve an accuracy close to the inference in the case of high precision (64-bit). Compared with other algorithms-based off-chip learning, the algorithm proposed in the present study can easily implement the mapping process and less influence of the device variability. Our result provides an effective approach to implementing the ANN on the memristive hardware platform.     

Memristor-based multi-synaptic spiking neuron circuit for spiking neural network

Spiking neural networks (SNNs) are widely used in many fields because they work closer to biological neurons. However, due to its computational complexity, many SNNs implementations are limited to computer programs. First, this paper proposes a multi-synaptic circuit (MSC) based on memristor, which realizes the multi-synapse connection between neurons and the multi-delay transmission of pulse signals. The synapse circuit participates in the calculation of the network while transmitting the pulse signal, and completes the complex calculations on the software with hardware. Secondly, a new spiking neuron circuit based on the leaky integrate-and-fire (LIF) model is designed in this paper. The amplitude and width of the pulse emitted by the spiking neuron circuit can be adjusted as required. The combination of spiking neuron circuit and MSC forms the multi-synaptic spiking neuron (MSSN). The MSSN was simulated in PSPICE and the expected result was obtained, which verified the feasibility of the circuit. Finally, a small SNN was designed based on the mathematical model of MSSN. After the SNN is trained and optimized, it obtains a good accuracy in the classification of the IRIS-dataset, which verifies the practicability of the design in the network.     

A spintronic memristive circuit on the optimized RBF-MLP neural network

A radial basis function network (RBF) has excellent generalization ability and approximation accuracy when its parameters are set appropriately. However, when relying only on traditional methods, it is difficult to obtain optimal network parameters and construct a stable model as well. In view of this, a novel radial basis neural network (RBF-MLP) is proposed in this article. By connecting two networks to work cooperatively, the RBF's parameters can be adjusted adaptively by the structure of the multi-layer perceptron (MLP) to realize the effect of the backpropagation updating error. Furthermore, a genetic algorithm is used to optimize the network's hidden layer to confirm the optimal neurons (basis function) number automatically. In addition, a memristive circuit model is proposed to realize the neural network's operation based on the characteristics of spin memristors. It is verified that the network can adaptively construct a network model with outstanding robustness and can stably achieve 98.33% accuracy in the processing of the Modified National Institute of Standards and Technology (MNIST) dataset classification task. The experimental results show that the method has considerable application value.     

一种改进的WOx忆阻器模型及其突触特性分析

忆阻器被定义为第四种基本电子元器件, 其模型的研究呈现多样性. 目前, 忆阻器模型与忆阻器实际特性的切合程度引起了研究者的广泛关注. 通过改变离子扩散项, 提出了一种新的WO_x忆阻器模型, 更好地匹配了忆阻器的实际行为特性. 首先, 新的模型不仅能够描述忆阻器的一般特性, 而且能够俘获记忆丢失行为. 另外, 将新的忆阻器作为神经突触, 分析了脉冲速率依赖可塑性、短期可塑性、长期可塑性, 并发现了与生物系统中极为相似的“经验学习”现象. 最后, 考虑到温度与离子扩散系数的关系, 探讨了温度对突触权值弛豫过程的影响. 实验表明, 新忆阻器模型比原来的模型更切合实际, 且更适合作为突触而应用到神经形态系统之中.     

基于Julia分形的多涡卷忆阻混沌系统

忆阻器作为一种非线性电子元件,能用作混沌系统中的非线性项,从而提高系统的复杂度.分形与混沌是密切相连的,分别对两者的研究都已成熟,却鲜有将分形过程应用到混沌系统中,以产生丰富的混沌吸引子.为了探索将分形与混沌系统相结合的可能性,本文首先提出了一个新的忆阻混沌系统,并从对称性、耗散性、平衡点稳定性、功率谱、Lyapunov指数和分数维等方面探讨了系统的动力学特性;紧接着,把经典的Julia分形过程应用到该忆阻混沌系统中,产生了新的混沌吸引子,并将几种由Julia分形衍生的变形Julia分形过程应用于文中提出的忆阻混沌系统,获得了丰富的混沌吸引子;最后,讨论了分形过程中的复常数对系统的影响.从仿真结果可以看出,分形过程与混沌系统的结合能产生丰富的多涡卷混沌吸引子.这不仅为产生多涡卷混沌吸引子提供了一种新方法,还弥补了使用功能函数方法造成混沌系统不光滑的不足.     

磁控二氧化钛忆阻混沌系统及现场可编程逻辑门阵列硬件实现

忆阻器作为混沌系统的非线性部分,能够提高混沌系统的信号随机性和复杂度,减小系统的物理尺寸.本文将磁控二氧化钛忆阻器应用到一个新的三维自治混沌系统中,通过理论推导和数值仿真,从平衡点的稳定性、Lyapunov指数谱、庞加莱截面和功率谱等方面研究了该系统的动力学特性,并详细讨论了不同参数变化对系统相图和平衡点稳定性的影响.有趣的是,在改变参数的情况下,系统的吸引子会产生翻转、混沌程度加剧和混叠的现象,说明该忆阻混沌系统具有丰富的动力学行为.此外,本文将改进的牛顿迭代法运用于现场可编程逻辑门阵列技术中,巧妙设计出一种只迭代3次就能达到所需精度的开方运算器,从而硬件实现了该忆阻混沌系统.这突破了以往忆阻器混沌系统只能在计算机模拟平台仿真的瓶颈,为进一步研究忆阻混沌系统及其在保密通信、信息处理中的应用提供了参考.     

两类纳米级非线性忆阻器模型及串并联研究

忆阻器是一种新型的非线性动态可变电阻器,其阻值的变化依赖于通过它的电荷量或磁通量.作为第四种基本电路元器件,忆阻器在非易失性存储器、非线性电路及系统、神经形态系统等领域中有巨大的应用潜能.忆阻器串并联组合电路具有比单个忆阻器更为丰富的器件特性,引起了研究者越来越多的关注.本文推导了带有窗函数的闭合形式的电荷及磁通量控制的忆阻器非线性模型,能够有效地模拟忆阻器边缘附近的非线性离子迁移现象,同时保证忆阻器的边界条件.进一步,分别从忆阻器的器件参数和激励阈值两个角度,对忆阻器串并联电路进行了全面的理论推导和数值分析.为了更加直观地观察忆阻器串并联特性,设计了一种基于Matlab的忆阻器串并联图形用户界面,能够清晰地展示两种分类方式下忆阻系统的器件特性,可为忆阻器组合电路的后续研究提供良好的理论参考和实验依据.