深井硬岩岩爆倾向性的多指标自适应模式判别

采用多指标综合判别岩爆发生的倾向性时，往往产生各指标判别结果的不一致性。本文结合实例应用神经网络自适应模式识别方法可以很好地解决这种不一致性问题。实现了多指标综合判别岩爆发生的倾向性。     

运用神经网络方法评定软岩巷道顶板安全性

在综合考虑现有的几种软巷道围岩分级方案的基础上，确定用围岩水理性质，单轴抗压强度，围岩稳定性系数和开采深度作为软岩巷道顶板安全分级的指标，设计了实现安全分级的BP神经网络算法，并结合实例进行了计算。计算结果表明，本文所述的分级方法是可行的。     

Effect of chain length distribution on thermal characteristics of model polytetrahydrofuran (PTHF) networks

A series of model polytetrahydrofuran (PTHF) networks were synthesized via end-linking reactions of α, ω-allyl PTHF oligomers with a stoichiometric tetrafunctional crosslinker. The telechelic PTHF oligomers were synthesized by living cationic ring-opening polymerization of tetrahydrofuran followed by a termination reaction with allyl alcohol. Networks thus prepared have well-controlled architecture in terms of the inter-crosslink chain length (M_c) and chain length distribution: resulting in unimodal, bimodal and clustered structures. Unimodal network was prepared by using polymer chains of same molecular weight, bimodal networks were synthesized by using two groups of polymer chains with different average molecular weights, and the clusters are prepared by incorporating clusters of networks with small molecular weight chains in a network matrix made of longer chains. Thermal characteristics of these model networks were investigated as a function of crosslink density, as well as inhomogeneities of crosslink distribution using DSC. We demonstrate that glass transition temperature (T_g) and crystallization behavior (melting temperature and crystallinity) of the networks are both strongly influenced by crosslink density (M_c). By comparing the unimodal, bimodal and clustered networks with similar average M_c, the effects of inhomogeneities in the crosslink distribution on the thermal properties were also investigated. Results show that inhomogeneities have trivial influence on T_g, but strongly affects the crystallization behavior. Moreover, the effects of the content ratio and length ratio between long and short chains, and the effects of cluster size and size distribution on the thermal characteristics were also studied.     

基于WTD-AR谱和MEA-BPNN的轴承故障诊断方法

针对滚动轴承故障诊断模型在噪声干扰下鲁棒性能差的问题,提出一种基于小波阈值去噪（WTD）、AR谱和思维进化算法（MEA）优化反向传播神经网络（BPNN）的轴承故障诊断方法。以原始振动信号为输入,采用小波方法分解重构原始信号滤除高频噪声,然后采用Burg算法估计AR模型参数提取降噪信号功率谱特征,最后将特征向量与对应标签分别作为MEA-BPNN神经网络的输入、输出进行训练,最终实现诊断。将该方法与一些先进的人工神经网络诊断方法作比较,测试该诊断模型的性能。研究结果表明：WTD-AR谱-MEA-BPNN诊断模型能够有效降低轴承振动信号的噪声干扰,实现特征增强,分辨率更高;相较于传统神经网络训练速度更快,在更短时间内甄别故障类型且识别率高。     

密炼机液压上顶栓压力人工神经网络控制系统算法研究

阐述了BP神经网络控制算法原理;论证了人工神经网络相对于经典PID控制在上顶栓压力闭环控制上优越性;建立了系统的机电液耦合模型,通过MATLAB仿真实验,研究BP算法在该系统中的动态特性,为神经网络在该控制系统中的应用提供实验依据;最后仿真结果表明,引入BP算法可以使系统超调量明显下降,响应时间显著得到提高,静差得到很好的消除。     

多电机机器人低成本神经脉冲PID控制研究

针对传统连续PID控制器并行使用导致资源访问阻塞,在单一设备内并行运行难以实现实时控制的问题,提出了一种基于神经脉冲的PID控制器,并实现了多电机的控制。该控制器将驱动和传感器信息作为脉冲,通过专门电路实时处理脉冲,实现了大规模的并行信息处理。实验结果证明了实现基于尖峰的控制器的可行性,且硬件要求较低,允许在大量并行控制器中复制该控制器,从而实现机器人实时控制。     

Integration of Adaptive Neuro Fuzzy Inference Systems and principal component analysis for the control of tertiary scale formation on tinplate at a hot mill

Scale is highly detrimental to surface quality for tinplate products. There are a large number of process variables at a typical hot mill and principal component analysis is a well-known technique for reducing the number of process variables. This paper estimates the principal components associated with the hot mill process variables and puts these through an Adaptive Neuro Fuzzy Inference System (ANFIS) to find those hot mill running conditions that will minimise the amount of scale observed on the bottom of the rolled strip. It was found that the variation observed in all the hot mill process variables could be captured through the use of just six principal components, and that using just three of these in an ANFIS was sufficient to identify those operating conditions leading to coils being produced with a consistently low scale count. Specifically, it was found that the best operating conditions for the hot mill were when the first component was lower than −0.098 the second lower than 0.8058 and the third higher than −0.482. These ranges in turn corresponded to certain hot mill temperatures that depended to some extent on the base chemistry of the incoming slab.     

Automated classification of brain tumours from short echo time in vivo MRS data using Gaussian Decomposition and Bayesian Neural Networks

Neuro-oncologists must ultimately rely on their acquired knowledge and accumulated experience to undertake the sensitive task of brain tumour diagnosis. This task strongly depends on indirect, non-invasive measurements, which are the source of valuable data in the form of signals and images. Expert radiologists should benefit from their use as part of an at least partially automated computer-based medical decision support system. This paper focuses on Magnetic Resonance Spectroscopy signal analysis and illustrates a method that combines Gaussian Decomposition, dimensionality reduction by Moving Window with Variance Analysis and classification using adaptively regularized Artificial Neural Networks. The method yields encouraging results in the task of binary classification of human brain tumours, even for tumour types that have seldom been analyzed from this viewpoint.     

Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting

Load demand forecasting is a critical process in the planning of electric utilities. An ensemble method composed of Empirical Mode Decomposition (EMD) algorithm and deep learning approach is presented in this work. For this purpose, the load demand series were first decomposed into several intrinsic mode functions (IMFs). Then a Deep Belief Network (DBN) including two restricted Boltzmann machines (RBMs) was used to model each of the extracted IMFs, so that the tendencies of these IMFs can be accurately predicted. Finally, the prediction results of all IMFs can be combined by either unbiased or weighted summation to obtain an aggregated output for load demand. The electricity load demand data sets from Australian Energy Market Operator (AEMO) are used to test the effectiveness of the proposed EMD-based DBN approach. Simulation results demonstrated attractiveness of the proposed method compared with nine forecasting methods.     

Grey and black-box modelling based on neural networks and artificial immune systems applied to solid dissolution by rotating disc method

The dissolution rates of urea, sodium bicarbonate, and sodium carbonate in water and aqueous solutions were determined using the rotating disc technique. The experiments showed that the dissolution rate increases with increasing disc surface area, temperature, and rotating speed, while it decreases with the solute concentration increase in the dissolution medium. The comparison between experimental values for the dissolution rate and those calculated from Levich equation evidenced a satisfactory agreement in the case of the urea dissolution and poor compliance for the sodium bicarbonate and sodium carbonate dissolution. This poor results and the lack of a good model for making predictions in different situations determined the generation of empirical and semiempirical models (black and grey box approaches) which include neural networks developed with Clonal Selection algorithm (belonging to the Artificial Immune System class) and combination between neural network and phenomenological model. Satisfactory results were obtained with neural networks (black box models) and hybrid models (grey box models).