A Multimodel Transfer-Learning-Based Car Price Prediction Model with an Automatic Fuzzy Logic Parameter Optimizer

Cars are regarded as an indispensable means of transportation in Taiwan.Several studies have indicated that the automotive industry has witnessed remarkable advances and that the market of used cars has rapidly expanded.In this study,a price prediction system for used BMW cars was developed.Nine parameters of used cars,including their model,registration year,and transmission style,were analyzed.The data obtained were then divided into three subsets.The first subset was used to compare the results of each algorithm.The predicted values produced by the two algorithms with the most satisfactory results were used as the input of a fully connected neural network.The second subset was used with an optimization algorithm to modify the number of hidden layers in a fully connected neural network and modify the low,medium,and high parameters of the membership function(MF)to achieve model optimization.Finally,the third subset was used for the validation set during the prediction process.These three subsets were divided using k-fold cross-validation to avoid overfitting and selection bias.In conclusion,in this study,a model combining two optimal algorithms(i.e.,random forest and k-nearest neighbors)with several optimization algorithms(i.e.,gray wolf optimizer,multilayer perceptron,and MF)was successfully established.The prediction results obtained indicated a mean square error of 0.0978,a root-mean-square error of 0.3128,a mean absolute error of 0.1903,and a coefficient of determination of 0.9249.

Data and Ensemble Machine Learning Fusion Based Intelligent Software Defect Prediction System

The software engineering field has long focused on creating high-quality software despite limited resources.Detecting defects before the testing stage of software development can enable quality assurance engineers to con-centrate on problematic modules rather than all the modules.This approach can enhance the quality of the final product while lowering development costs.Identifying defective modules early on can allow for early corrections and ensure the timely delivery of a high-quality product that satisfies customers and instills greater confidence in the development team.This process is known as software defect prediction,and it can improve end-product quality while reducing the cost of testing and maintenance.This study proposes a software defect prediction system that utilizes data fusion,feature selection,and ensemble machine learning fusion techniques.A novel filter-based metric selection technique is proposed in the framework to select the optimum features.A three-step nested approach is presented for predicting defective modules to achieve high accuracy.In the first step,three supervised machine learning techniques,including Decision Tree,Support Vector Machines,and Naïve Bayes,are used to detect faulty modules.The second step involves integrating the predictive accuracy of these classification techniques through three ensemble machine-learning methods:Bagging,Voting,and Stacking.Finally,in the third step,a fuzzy logic technique is employed to integrate the predictive accuracy of the ensemble machine learning techniques.The experiments are performed on a fused software defect dataset to ensure that the developed fused ensemble model can perform effectively on diverse datasets.Five NASA datasets are integrated to create the fused dataset:MW1,PC1,PC3,PC4,and CM1.According to the results,the proposed system exhibited superior performance to other advanced techniques for predicting software defects,achieving a remarkable accuracy rate of 92.08%.

Breakdown Voltage Prediction by Utilizing the Behavior of Natural Ester for Transformer Applications

This research investigates the dielectric performance of Natural Ester(NE)using the Partial Differential Equation(PDE)tool and analyzes dielectric performance using fuzzy logic.NE nowadays is found to replace Mineral Oil(MO)due to its extensive dielectric properties.Here,the heat-tolerant Natural Esters Olive oil(NE1),Sunflower oil(NE2),and Ricebran oil(NE3)are subjected to High Voltage AC(HVAC)under different electrodes configurations.The break-down voltage and leakage current of NE1,NE2,and NE3 under Point-Point(P-P),Sphere-Sphere(S-S),Plane-Plane(PL-PL),and Rod-Rod(R-R)are measured,and survival probability is presented.The electricfield distribution is analyzed using the Partial Differential Equation(PDE)tool.NE shows better HVAC with stand capacity under all the electrodes configuration,especially in the S-S shape geometry.The exponential function is developed for the oils under different elec-trode geometry;NE shows a higher survival probability.Likewise,the most influ-ential dielectric properties such as breakdown voltage,kinematic viscosity,and water content are used to develop a Mamdani-based control system model that combines two variables to produce the surface model.The surface model indi-cates that the NE subjected for investigation is less susceptible to moisture effect and higher kinematic viscosity.Based on the surface models of PDE and fuzzy,it is concluded that NE possesses a high survival rate since its breakdown voltage does not react to changes in water content.Hence the application of NE in the transformer application is highly safe and possesses extended life.

A Fuzzy Logic Approach to Predict Tensile Strength in TIG Mild Steel Welds

Welding defects influence the desired properties of welded joints giving fabrication experts a common problem of not being able to produce weld structures with optimal strength and quality. In this study, the fuzzy logic system was employed to predict welding tensile strength. 30 sets of welding experiments were conducted and tensile strength data was collected which were converted from crisp variables into fuzzy sets. The result showed that the fuzzy logic tool is a highly effective tool for predicting tensile strength present in TIG mild steel weld having a coefficient of determination value of 99%.

Research on Mixed Logic Dynamic Modeling and Finite Control Set Model Predictive Control of Multi-Inverter Parallel System

Parallel connection of multiple inverters is an important means to solve the expansion,reserve and protection of distributed power generation,such as photovoltaics.In view of the shortcomings of traditional droop control methods such as weak anti-interference ability,low tracking accuracy of inverter output voltage and serious circulation phenomenon,a finite control set model predictive control(FCS-MPC)strategy of microgrid multiinverter parallel system based on Mixed Logical Dynamical(MLD)modeling is proposed.Firstly,the MLD modeling method is introduced logical variables,combining discrete events and continuous events to form an overall differential equation,which makes the modeling more accurate.Then a predictive controller is designed based on the model,and constraints are added to the objective function,which can not only solve the real-time changes of the control system by online optimization,but also effectively obtain a higher tracking accuracy of the inverter output voltage and lower total harmonic distortion rate(Total Harmonics Distortion,THD);and suppress the circulating current between the inverters,to obtain a good dynamic response.Finally,the simulation is carried out onMATLAB/Simulink to verify the correctness of the model and the rationality of the proposed strategy.This paper aims to provide guidance for the design and optimal control of multi-inverter parallel systems.

Improvement of Rainfall Prediction Model by Using Fuzzy Logic

This paper presents the improvement of the fuzzy inference model for predicting rainfall. Fuzzy rule based system is used in this study to predict rainfall. Fuzzy inference is the actual procedure of mapping with a given set of input and output through a set of fuzzy systems. Two operations were performed on the fuzzy logic model;the fuzzification operation and defuzzification operation. This study is obtaining two input variables and one output variable. The input variables are temperature and wind speed at a particular time and output variable is the amount of predictable rainfall. Temperature, wind speed and rainfall have to construct eight equations for different categories and which are shows the diagram of the graph. Fuzzy levels and membership functions obtained after minimum composition of inference part of the fuzzifications done for temperature and wind speed are considered as they represent the environmental condition enhance a rainfall occurrence which is effect on agricultural production.

Performance analysis of CDMA power control system based on fuzzy prediction

Power control is of paramount importance in combating the near-far problem and co-channel interference in a CDMA cellular system. Due to fast fading and ambient interference in a wireless channel, conventional fixed-step power control schemes have difficulty in compensating for the fast fading channel dynamically and in a timely manner. To acquire flexible power regulation in order to maintain required transmission capacity under the given transmission quality requirement, we propose a hybrid power control scheme which makes full use of the simple fuzzy inference rule refined by an operator in the fuzzy control and prediction property from related previous results in Generalized Prediction Control (GPC). In implementation of this strategy, we classify the fading zone into three levels according to the signal-to-noise-rate (SNR) requirement. In each level the power compensation amount varies with fading gradient and the compensation scheme varies as well. The digital results show that adoption of the fuzzy-GPC power regulation scheme has acquired a reasonable performance improvement when compared with fixed-step and fuzzy schemes. According to theoretic analysis and simulation results, we can conclude that under a variational transmission environment, a flexible power regulation scheme such as fuzzy-GPC is easy to adapt to the environment and thus overcomes the near-far effect and multi-access interference effectively.

Enhanced Water Quality Control Based on Predictive Optimization for Smart Fish Farming

The requirement for high-quality seafood is a global challenge in today’s world due to climate change and natural resource limitations.Internet of Things(IoT)based Modern fish farming systems can significantly optimize seafood production by minimizing resource utilization and improving healthy fish production.This objective requires intensive monitoring,prediction,and control by optimizing leading factors that impact fish growth,including temperature,the potential of hydrogen(pH),water level,and feeding rate.This paper proposes the IoT based predictive optimization approach for efficient control and energy utilization in smart fish farming.The proposed fish farm control mechanism has a predictive optimization to deal with water quality control and efficient energy consumption problems.Fish farm indoor and outdoor values are applied to predict the water quality parameters,whereas a novel objective function is proposed to achieve an optimal fish growth environment based on predicted parameters.Fuzzy logic control is utilized to calculate control parameters for IoT actuators based on predictive optimal water quality parameters by minimizing energy consumption.To evaluate the efficiency of the proposed system,the overall approach has been deployed to the fish tank as a case study,and a number of experiments have been carried out.The results show that the predictive optimization module allowed the water quality parameters to be maintained at the optimal level with nearly 30%of energy efficiency at the maximum actuator control rate compared with other control levels.

应用混合逻辑动态模型预测控制器的磁轴承三电平调制策略

传统磁轴承三电平调制策略在数字控制系统中存在控制延迟问题,影响电流纹波抑制效果。针对上述问题,提出一种基于混合逻辑动态模型预测控制器的磁轴承三电平调制策略。基于混杂系统理论,建立混合逻辑动态模型,统一表征出驱动电路充电、放电和续流工作模态,实现三电平调制;结合模型预测控制理论,将所建立的混合逻辑动态模型作为预测模型,预测磁轴承的控制电流,实现对控制延迟的补偿,并将预测控制电流送入到代价函数中,得出驱动电路的最优控制信号;基于所提调制策略构建了磁轴承控制系统,并与传统调制策略进行对比。仿真结果表明:在轻载扰动工况下,所提调制策略相比于传统三电平滞环调制策略与传统三电平脉冲宽度调制(pulse width modulation,PWM)策略,电流纹波分别降低了49.90%和49.87%;在中载扰动工况下,所提调制策略相比于传统三电平滞环调制策略与传统三电平PWM策略,电流纹波分别降低了49.99%和49.84%;在重载扰动工况下,所提调制策略相比于传统三电平滞环调制策略与传统三电平PWM策略,电流纹波分别降低了50.08%和49.77%。在三电平调制机制的基础上,所提调制策略能够有效补偿一个采样周期的控制延迟,达到降低电流纹波的效果。

基于改进北方苍鹰优化随机配置网络的网络流量预测模型

网络流量预测作为一种关键技术,能帮助实现网络资源的合理分配、优化网络性能以及提供高效的网络服务。随着网络环境的演变和发展,网络流量的多样性和复杂性增加,为了提高网络流量的预测精度,提出了一种基于改进北方苍鹰优化随机配置网络(CNGO-SCN)的网络流量预测模型。随机配置网络作为一种具有监督机制的增量式模型,在解决大规模数据回归和预测问题方面具有良好的优势。但是,一些超参数的选择影响了随机配置网络的准确性。针对这一问题,利用北方苍鹰算法对影响随机配置网络性能的正则化参数和比例因子进行优化,得到最佳数值。而北方苍鹰算法由于初始种群的随机分布导致种群个体质量不佳,因此引入混沌逻辑映射提升初始解的质量。将优化后的模型应用于英国学术网、欧洲某城市核心网网络流量数据集和合作企业搭建的网络协同制造云平台交换机接口的真实流量数据集,并与多种神经网络模型进行对比,以验证所提模型的网络流量预测能力。实验结果表明,该模型对比其他神经网络模型具有更高的预测精度,在实际应用场景中处理复杂数据时具备更加优秀的预测能力,该模型的预测误差下降了0.9%~99.7%。

改进变值逻辑与线性预测在心音分类中的应用

心音对于评价心脏健康状况具有重要作用.文章介绍了一种新的基于变值逻辑与线性预测倒谱系数融合特征的先心病分类算法,有助于提取心音中的深度病理特征.算法首先对心音进行降噪、包络提取;然后进行变值逻辑运算、标记并转换为可分析的测度数据,并计算信号的线性预测倒谱系数进行特征融合;最后使用随机森林,XGBOOST和LIGHTGBM机器学习分类器进行先心病二分类.研究所用心音样本共4000例,测试结果对正常和异常心音分类的平均准确率为0.9138.算法无需对心音进行心动周期分割,大大简化了分析流程,可望用于先心病的筛查.

广西河池某矿水文地质特征分析及涌水量预测

以广西河池某矿区为研究对象,通过对矿区相关的水文地质条件进行分析,并采用水文地质比拟法、大井法和修正后大井法公式对矿区涌水量进行预测对比。结果表明:含水岩组之间水力联系密切,断层破碎带裂隙水发育明显,岩溶破碎带对矿床开采影响较大。矿区北、东以及西部为隔水边界,南部为透水边界。地下水径流赋存运移于岩溶裂隙、断层破碎带及岩溶管道中,汇流集中于北东、北西径流带进行排泄。水文地质比拟法的矿区平水期、枯水期及丰水期涌水量分别为18364.72 m^(3)/d、15 893.31 m^(3)/d、79 794.11 m^(3)/d;大井法用裘布依公式预测结平水期、枯水期及丰水期涌水量分别为23 108.90 m^(3)/d、21 044.15 m^(3)/d、27 418.04 m^(3)/d;修正后的大井法平水期、枯水期及丰水期涌水量分别为16 028.25 m^(3)/d、15 421.58 m^(3)/d、23 485.05 m^(3)/d。研究表明水文地质比拟法和修正后大井法公式预测结果更准确合理,符合预期结果。

基于全流域梯级水电站的智能化调度应用研究

为实现老挝南欧江全流域梯级水电站“度电必争、滴水归调”的智能化调度运行目标,基于数字化转型的管理理念,剖析梯级水电站统筹调度的难点,探索提高水文气象预测精度及水电站运行调度能力的实施路径与有效举措。通过水电站智能化调度提高电站经济效益、社会效益、生态效益,推动“双碳”目标的落实,为“一带一路”沿线国家流域水电开发、国内外各大流域梯级调控中心提供优化运行的指导与借鉴。

基于模糊逻辑的空调系统广义预测控制参数整定

针对多输入多输出(multiple-input-multiple-output,MIMO)变风量空调系统(variable air volume,VAV),本文提出了一种基于模糊逻辑和事件触发的广义预测控制(generalized predictive control,GPC)参数整定方法。针对多变量VAV空调系统,基于输出斜率等新型模糊目标参数及高斯双边形隶属度函数建立模糊预测模型,提高了隶属函数拟合度以更贴切地反映系统当前时刻状态。利用麻雀智能算法(sparrow search algorithm,SSA)构建隶属度函数,对GPC控制器中的柔化因子和加权系数进行在线分段整定,有效提升了系统性能。此外,在参数整定过程中,引入事件触发机制(event-triggered mechanism,ETM),在保证控制性能的同时,避免了不必要的控制器采样与更新,降低了系统在线计算量并减少了能源消耗。最后通过VAV空调系统仿真实验验证,证明了本文提出方法的可行性和有效性。

博士生教育科类结构演进逻辑与规模预测——基于中美比较的视角

博士研究生教育是国民教育体系的“金字塔尖”,其学科结构是人才培养的重要载体,其培养规模事关国家创新驱动战略。新中国成立以来,我国博士毕业生规模持续上升,学科结构趋向多元化。博士生教育学科结构的调整遵循着多重逻辑,即政府逻辑、市场逻辑及学术逻辑。从2021—2030年预测结果来看,中美两国科类结构分布不均衡。据此本研究提出精确定位人才培养目标、依据产业结构战略性调整动向、强化学科体系调整与学科体制创新的联动机制等建议。

制冷站双目标权重自适应非线性预测控制

针对传统制冷站控制系统易产生振荡,且无法实现系统性能整体优化的问题,本文提出一种制冷站非线性预测控制策略,优化目标函数设计为满足建筑冷量需求的同时,尽可能提高系统整体能效.为解决上述两个优化目标之间的矛盾关系,本文采用模糊逻辑设计了优化目标权重自适应模块,实时求取权重因子最优解;针对非线性系统在线优化求解困难问题,本文提出了基于神经网络的非线性滚动优化算法,采用神经网络作为反馈优化控制器,并将系统优化目标函数作为在线寻优性能指标,结合Euler-Lagrange方法和随机梯度下降法对控制器权值和阈值进行在线寻优,算法计算量小,占用存储空间适中,便于采用低成本的现场控制器实现制冷站预测控制.仿真实验结果表明,本文所提出的预测控制策略与PID控制相比,在未加入优化目标函数权重自适应模块情况下,系统平均能效比提高约32.5%;进行优化目标函数权重自适应寻优后,系统平均能效提高约39.43%.

模糊逻辑在海洋天气预测中的应用

海洋环境有多变性、复杂性、突发性等几个特点,这些特点导致海洋天气难以准确预测,对海上设备防腐、海上施工及人民出行和生活造成很多不便。随着社会的发展,海上工业和渔业及人民生活对天气预报准确度有更高的要求。利用精密数据采集仪器,把海洋环境的一些参数进行记录、集成,随着时间的推移、相关数据的积累,建立最大隶属度型模糊逻辑可以帮助进行更为准确的预测,从而为施工、出行、生活等各方面在天气方面提供参考,减少风险,保障安全。

算法预测性警务的风险辨析及应对

随着算法时代的到来,在总结吸收问题导向警务和热点警务模式特性的基础上,通过对“不安全因素”的边际判断以及算法决策机制,算法预测性警务实现了对警情的预报预测预警。但算法预测性警务也存在风险,包括计算机学习的样本缺陷、技术上的利维坦化以及算法黑箱在内的技术风险,还包括算法在嵌入警察权的过程中可能带来的警察权扩张、“全景敞视主义”的自由困境、破坏警民关系等制度风险。为此,除了扩大数据池以及加强计算机学习模型的优化等技术升级保障外,更要做好个人信息保护与国家监管之间、算法预测的工具理性与伦理道德之间、算法技术垄断与公民参与之间三方面的平衡,以更好地应对算法预测性警务在未来可能面对的风险。

基于模糊逻辑的山区高速公路交通事故预测研究

为解决事故预测中变量及变量关系之间的模糊性,引入模糊逻辑方法,建立基于模糊逻辑的山区高速公路交通事故预测模型。收集云南省某典型山区高速公路的道路几何线形和交通事故等数据,按道路是否位于山阴面进行分类,提出能表征特殊节点和不良天气影响程度的显著变量;将变量划分为5个模糊集合,并采用三角形、梯形隶属函数将变量模糊化,训练得到693条有效模糊控制规则,按照模糊推理机制对模型进行推理,反模糊化后得到精确的事故预测结果。结果表明,模型的预测精度、平均绝对误差和平均绝对百分比误差分别为90.2,0.88次/km,11%,预测精度较高且适用于同类型的山区高速公路。因此,利用模糊逻辑对山区高速进行事故预测具有一定的可行性和可移植性。

Multi-model predictive control with local constraints based on model switching

Because model switching system is a typical form of Takagi-Sugeno（T-S） model which is an universal approximator of continuous nonlinear systems, we describe the model switching system as mixed logical dynamical （MLD） system and use it in model predictive control （MPC） in this paper. Considering that each local model is only valid in each local region,we add local constraints to local models. The stability of proposed multi-model predictive control （MMPC） algorithm is analyzed, and the performance of MMPC is also demonstrated on an inulti-multi-output（MIMO） simulated pH neutralization process.