神经模糊控制的纸浆专家系统设计

为了控制纸浆浓度的稳定性并提高纸浆的质量,对碎浆工序的时间进行智能模糊控制．结合神经网络;和模糊控制技术,利用经验数据和隶属函数生成相应控制规则．控制系统根据纸浆浓度和浓度变化率自动控制碎浆的时间．仿真实验表明,控制系统具有良好的记忆和联想功能且性能稳定．     

Time to seize the digital evolution: Adoption of blockchain in operations and supply chain management among Malaysian SMEs

This study aims to investigate the effects of relative advantage, complexity, upper management support, cost, market dynamics, competitive pressure and regulatory support on blockchain adoption for operations and supply chain management among Small-Medium Enterprises (SMEs) in Malaysia. Unlike existing studies that employed linear models with Technology Acceptance Model or United Theory of Acceptance and Use of Technology that ignores the organisational and environmental factors, we adopted the Technology, Organisation and Environment Framework that covers the technological dimensions of relative advantage and complexity, organisational dimensions of upper management support and cost and environmental dimensions of market dynamics, competitive pressure and regulatory support. Empirical data from 194 SMEs were investigated and ranked using a nonlinear non-compensatory PLS-ANN approach. Competitive pressure, complexity, cost and relative have significant effects on behavioural intention. Market dynamics, regulatory support and upper management support were insignificant predictors. SMEs often lack resources for technological investments but faces same requirements for streamlining business processes to optimise returns and blockchain presents a viable option for SMEs’ sustainability due to its features of immutability, transparency and security that have the potential to revolutionise businesses. This study contributes new knowledge to the literature on factors that affect blockchain adoption and justifications were discussed accordingly.     

基于人工神经网络的时间序列交通事故预测

分析了人工神经网络和时间序列的优越性和适用性，提出了基于神经网络基础之上的时间序列预测方法，从而实现了从线性预测到非线性预测的转变。     

基于人工神经网络的等离子熔积层形貌试验研究

1IntroductionPlasma deposition manufacturing(PDM)is a newlydeveloped direct metal fabrication process based onplasma transferred arc surfacing[1],as shown in Fig.1.Unlike most existing rapid prototyping techn-ologies,this technique is characterized by sup…     

基于ANN模型的北京市公交客运量预测研究

通过对运输部门服务区域内不同出行方式（包括乘用公共电车）或乘行方式分布比率的已有状态及其动向的分析,利用ANN模型预测未来某一时期的客运量,为城市公共交通规划提供决策依据。     

基于HJPS算法的神经网络的轧制力修正模型

传统的轧制力模型结构简单、精度较低,即使采用基于有限元的数值积分方式进行精化,出于计算效率的考虑因其有限区域的划分十分有限,因此对于轧制力计算的精度提高有限。直接采用神经网络对轧制力进行建模可以极大地提高模型精度,但是模型对新型材料的泛化能力较差。为此提出简单有限元轧制力模型,并在模型基础上使用HJPS优化算法的神经网络对轧制力进行修正,对该模型的仿真测试表明,该模型具有很强的泛化能力,收敛速度快、不易陷于局部优化,能够极大地提高轧制力模型的计算精度。     

Calculation method of ship collision force on bridge using artificial neural network

Ship collision on bridge is a dynamic process featured by high nonlinearity and instantaneity. Calculating ship-bridge collision force typically involves either the use of design-specification-stipulated equivalent static load, or the use of finite element method （FEM） which is more time-consuming and requires supercomputing resources. In this paper, we proposed an alternative approach that combines FEM with artificial neural network （ANN）. The radial basis function neural network （RBFNN） employed for calculating the impact force in consideration of ship-bridge collision mechanics. With ship velocity and mass as the input vectors and ship collision force as the output vector, the neural networks for different network parameters are trained by the learning samples obtained from finite element simulation results. The error analyses of the learning and testing samples show that the proposed RBFNN is accurate enough to calculate ship-bridge collision force. The input-output relationship obtained by the RBFNN is essentially consistent with the typical empirical formulae. Finally, a special toolbox is developed for calculation effi- ciency in application using MATLAB software.     

Evaluation of accelerated deterioration in NAPTF flexible test pavements

Previous research studies have successfully demonstrated the use of artificial neural network （ANN） models for predicting critical structural responses and layer moduli of highway flexible pavements. The primary objective of this study was to develop an ANN-based approach for backcalculation of pavement moduli based on heavy weight deflectometer （HWD） test data, especially in the analysis of airport flexible pavements subjected to new generation aircraft （NGA）. Two medium-strength subgrade flexible test sections, at the National Airport Pavement Test Facility （NAPTF）, were modeled using a finite element （FE） based pavement analysis program, which can consider the non-linear stress-dependent behavior of pavement geomaterials. A multi-layer, feed-forward network which uses an error-backpropagation algorithm was trained to approximate the HWD back- calculation function using the FE program generated synthetic database. At the NAPTF, test sections were subjected to Boeing 777 （B777） trafficking on one lane and Boeing 747 （B747） trafficking on the other lane using a test machine. To monitor the effect of traffic and climatic variations on pavement structural responses, HWD tests were conducted on the trafficked lanes and on the untrafficked centerline of test sections as trafficking progressed. The trained ANN models were successfully applied on the actual HWD test data acquired at the NAPTF to predict the asphalt concrete moduli and non-linear subgrade moduli of the medium-strength subgrade flexible test sections.     

An artificial neural network approach for prediction of long-term strength properties of steel fiber reinforced concrete containing fly ash

In this study, an artificial neural network （ANN） model for studying the strength properties of steel fiber reinforced concrete （SFRC） containing fly ash was devised. The mixtures were prepared with 0 wt%, 15 wt%, and 30 wt% of fly ash, at 0 vol.%, 0.5 vol.%, 1.0 vol.% and 1.5 vol.% of fiber, respectively. After being cured under the standard conditions for 7, 28, 90 and 365 d, the specimens of each mixture were tested to determine the corresponding compressive and flexural strengths. The pa- rameters such as the amounts of cement, fly ash replacement, sand, gravel, steel fiber, and the age of samples were selected as input variables, while the compressive and flexural strengths of the concrete were chosen as the output variables. The back propagation learning algorithm with three different variants, namely the Levenberg-Marquardt （LM）, scaled conjugate gradient （SCG） and Fletcher-Powell conjugate gradient （CGF） algorithms were used in the network so that the best approach can be found. The results obtained from the model and the experiments were compared, and it was found that the suitable algorithm is the LM algorithm. Furthermore, the analysis of variance （ANOVA） method was used to determine how importantly the experimental parameters affect the strength of these mixtures.     

我国受限技术的监测与评估理论方法研究

技术强国对华技术出口限制严重影响了我国经济、技术的自由和快速发展,然而我国对这些受限技术深入系统的研究很少。本文首先应用技术监测方法,进行了技术强国针对我国实施的技术出口限制内容动态监测研究。然后综合运用模糊评价法以及人工神经网络等理论方法,对我国受限技术与我国重点发展领域的对比、与技术强国之间的技术差距、我国发展的重要性等进行分析和研究,建立我国受限技术评估的理论方法。从而可以准确认识我国受限技术的本质特征,为我国受限技术的发展和决策提供科学的指导和帮助。