Real-Time Prediction of Urban Traffic Problems Based on Artificial Intelligence-Enhanced Mobile Ad Hoc Networks(MANETS)

Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial resources.This study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Networks(MANETs)based real-time prediction paradigm for urban traffic challenges.MANETs are wireless networks that are based on mobile devices and may self-organize.The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts.This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network(CSFPNN)technique to assess real-time data acquired from various sources within theMANETs.The framework uses the proposed approach to learn from the data and create predictionmodels to detect possible traffic problems and their severity in real time.Real-time traffic prediction allows for proactive actions like resource allocation,dynamic route advice,and traffic signal optimization to reduce congestion.The framework supports effective decision-making,decreases travel time,lowers fuel use,and enhances overall urban mobility by giving timely information to pedestrians,drivers,and urban planners.Extensive simulations and real-world datasets are used to test the proposed framework’s prediction accuracy,responsiveness,and scalability.Experimental results show that the suggested framework successfully anticipates urban traffic issues in real-time,enables proactive traffic management,and aids in creating smarter,more sustainable cities.

A fuzzy neural network evolved by particle swarm optimization

A cooperative system of a fuzzy logic model and a fuzzy neural network(CSFLMFNN)is proposed,in which a fuzzy logic model is acquired from domain experts and a fuzzy neural network is generated and prewired according to the model.Then PSO-CSFLMFNN is constructed by introducing particle swarm optimization(PSO)into the cooperative system instead of the commonly used evolutionary algorithms to evolve the prewired fuzzy neural network.The evolutionary fuzzy neural network implements accuracy fuzzy inference without rule matching.PSO-CSFLMFNN is applied to the intelligent fault diagnosis for a petrochemical engineering equipment,in which the cooperative system is proved to be effective.It is shown by the applied results that the performance of the evolutionary fuzzy neural network outperforms remarkably that of the one evolved by genetic algorithm in the convergence rate and the generalization precision.

Self-learning fuzzy neural network control for backside width of weld pool in pulsed GTAW with wire filler

The weld pool shape control by intelligent strategy was studied. In order to improve the ability of self-learning and self-adaptation of the ordinary fuzzy control, a self-learning fuzzy neural network controller (FNNC) for backside width of weld pool in pulsed gas tungsten arc welding (GTAW) with wire filler was designed. In FNNC, the fuzzy system was expressed by an equivalence neural network, the membership functions and inference rulers were decided through the learning of the neural network. Then, the FNNC control arithmetic was analyzed, simulating experiment was done, and the validating experiments on varied heat sink workpiece and varied gap workpiece were implemented. The maximum error between the real value and the given one was 0.39mm, the mean error was 0.014mm, and the root-mean-square was 0.14mm. The real backside width was maintained around the given value. The results show that the self-learning fuzzy neural network control strategy can achieve a perfect control effect under different set values and conditions, and is suitable for the welding process with the varied structure and coefficients of control model.

Intelligent vehicle lateral controller design based on genetic algorithmand T-S fuzzy-neural network

Non-linearity and parameter time-variety are inherent properties of lateral motions of a vehicle. How to effectively control intelligent vehicle (IV) lateral motions is a challenging task. Controller design can be regarded as a process of searching optimal structure from controller structure space and searching optimal parameters from parameter space. Based on this view, an intelligent vehicle lateral motions controller was designed. The controller structure was constructed by T-S fuzzy-neural network (FNN). Its parameters were searched and selected with genetic algorithm (GA). The simulation results indicate that the controller designed has strong robustness, high precision and good ride quality, and it can effectively resolve IV lateral motion non-linearity and time-variant parameters problem.

Study on Missile Intelligent Fault Diagnosis System Based on Fuzzy NN Expert System

In order to study intelligent fault diagnosis methods based on fuzzy neural network (NN) expert system and build up intelligent fault diagnosis for a type of missile weapon system, the concrete implementation of a fuzzy NN fault diagnosis expert system is given in this paper. Based on thorough research of knowledge presentation, the intelligent fault diagnosis system is implemented with artificial intelligence for a large-scale missile weapon equipment. The method is an effective way to perform fuzzy fault diagnosis. Moreover, it provides a new way of the fault diagnosis for large-scale missile weapon equipment.

基于模糊神经网络的微电网荷储协调智能控制方法

针对传统比例-积分-微分(proportional integral derivative,PID)控制和模型论控制方法难以应对新型电力系统背景下微电网面临的运行场景复杂多变的问题,提出了基于模糊神经网络的微电网荷储协调智能控制方法。首先确定了微电网模糊控制输入及输出变量,以平抑净负荷波动及减少储能充放电频次为目的,将微电网控制经验总结成模糊规则表,采用神经网络深度学习算法修正模糊控制模型的隶属度函数中心、宽度和输出权重来提高模型的自适应能力,从而制定了可调控负荷和储能的功率控制系数;进而针对模糊神经网络控制输出的负荷调控需求量在各可调控负荷间分配的问题,提出了基于灵活性供给指标排序的负荷调控优先级选择方法,最终完成了微电网系统储能单元和可调控负荷控制策略的制定。某典型微电网系统算例仿真结果表明,所提方法制定的各可调控负荷与储能控制策略能在避免储能频繁和过度充放电的同时,在并网状态下有效减弱并网功率对上级电网造成的随机扰动,在孤岛状态下能够有效平抑系统功率波动,提升系统运行稳定性。

盾构掘进姿态控制技术研究现状与未来展望

为系统地分析我国盾构掘进姿态控制技术的研究进展,基于知网检索到的32篇相关文献,总结盾构掘进姿态的主要表征参数和影响因素,并以盾构液压推进系统为例论述其控制原理。同时,结合盾构姿态智能控制的部分案例,总结PID控制、自适应控制、模糊控制、基于神经网络的控制和基于智能算法的控制等技术的优劣势及应用场景。基于以上分析,对盾构姿态控制技术的发展方向进行展望。研究发现:1)盾构掘进姿态的影响因素主要包括几何参数、地层参数和盾构掘进参数。2)由于盾构推进系统需要同时完成盾构向前推进和姿态调整等复杂任务,因此该系统的参数对盾构姿态有着很大的影响,是姿态控制的关键因素之一。3)相较于传统PID控制方法,智能控制方法与PID控制的结合可以提高系统的响应速度、精度、适应能力和鲁棒性。4)未来研究可以围绕基于多源数据融合的控制算法、构建数据-机制混合驱动的控制技术以及加强控制技术在实际工程中的实用性等方面展开,实现更精准、更高效的盾构掘进姿态控制。

基于深度神经网络的电厂跑冒滴漏智能识别方法研究

电厂设备复杂,容易发生跑冒滴漏问题,人工巡检存在发现滞后、人为疏忽、不能实时传达异常情况等问题。基于深度学习卷积神经网络、迁移学习和小样本学习技术,设计电厂异常状态智能识别报警系统,利用深度学习模型检测监控系统捕获的现场图片,识别常见的设备跑冒滴漏现象,准确并且及时地发出警告,以此提高电厂的安全监管和对意外事故的应急能力。采用相对成熟的YOLOv5作为目标检测网络基础框架,针对跑冒滴漏数据稀少问题,对网络结构进行优化并采用迁移学习与小样本学习方法来提高网络识别精度。结果表明,基于深度学习卷积神经网络的电厂异常状态智能识别报警系统,能够保持电厂异常状态识别的准确性和实时性。该系统可以实现自主全天候智能检测,及时推送报警信息,减少利用人力关注监控设备排查异常状态可能发生的疏漏,降低电厂运行维护成本,提高电厂的安全监管与对意外事故的应急能力。

永磁同步电机变结构模糊神经网络控制策略

为改善永磁同步电机矢量控制技术在复杂工况下控制器参数不能做出实时调整导致控制性能差的问题,分析了模糊逻辑与神经网络控制原理,提出了一种基于高斯径向基神经网络与模糊控制的相结合的智能控制策略。以转速误差以及误差的变化率为依据构建增量补偿式二维变结构模糊神经网络PID控制器(deformable fuzzy neural network,DFNN)通过RBF神经网络参数辨识器获取永磁同步电机的雅可比信息矩阵(Jacobian matrix),通过变结构算法确定变结构模糊神经网络的结构信息。在MATLAB/Simulink中仿真结果表明,该控制系统提升了电机启动以及目标转速发生改变时的响应速度,同时降低了超调量,在负载转矩存在扰动时转速变化小,且能够快速回归至给定值,优化了矢量控制系统的性能。

延迟时间未知的时延系统Fuzzy-Gray预测控制

提出了一种带智能积分的参数自调整Fuzzy -Gray预测控制算法。该算法综合Fuzzy控制、Gray预测的长处 ,同时利用神经网络辨识延迟系统的延迟时间来在线调整灰色预测控制器的参数。仿真结果表明这种控制策略具有很好的控制效果 ,它是大延迟控制中克服延迟时间变化的很有希望的方法 ,并能较好地兼顾系统的动、静态特性 ,超调小、响应快 。

应用于水下防御的小目标动态多属性威胁评估

面向近海水下防御的信息融合,提出一种危险小目标的动态多属性威胁评估方法。通过对典型目标的特性分析,构建了威胁评估指标体系。利用K均值聚类为各规范化指标添加威胁度标签,从而生成指标值-威胁度样本。基于此,将指标威胁隶属度函数的确定和计算转化为径向基函数神经网络的训练和预测,连接权值及偏置等网络参数利用量子粒子群优化算法求解,目标属性及时间序列的权重分别由熵权法和正态分布累积函数计算,应用逼近理想解排序法得到目标威胁度。算例分析中,通过专家评分和对比实验验证了所提方法的有效性和可靠性。

基于FNN的城市景观照明智能节能控制方法仿真

为了降低景观照明用电能耗,提升节能效果,提出基于FNN的城市景观照明智能节能控制方法。分析城市景观照明需求,依据分析结果建立景观照明区域划分模型,利用上述模型将城市景观照明区域划分为开启区域和关闭区域,并在开关区域之间建立缓冲区域,规避城市景观照明控制时出现的延迟误差问题;采集景观照明设备运行状态数据,通过FNN网络对其实施训练学习,获取完整的设备状态数据集;基于获取的数据集通过模糊神经网络设计节能控制器,并利用以上节能控制器实现城市景观照明智能节能控制。实验结果表明,使用该方法对景观照明开展智能节能控制时,调光时长、照明时间以及用电能耗均得到了良好控制,说明其能够满足照明节能需求。

选煤场浓缩池溢流浓度控制方法研究

浓缩池溢流浓度的监测与控制方法是选煤厂实现智能化选煤的关键之一,针对仅采用传感器的溢流浓度监测与控制方式会导致絮凝剂调节滞后的问题。搭建一套浓缩池自动加药系统,该系统采用BP神经网络与模糊自抗扰控制器(Active Disturbance Rejection Control,ADRC)相结合的监测与控制方法,实现浓缩池溢流浓度的稳定控制,避免因絮凝剂添加量问题直接影响浓缩反应,进而影响絮凝沉降效果。通过仿真对比,该方法比采用PID控制的自动加药系统稳定性更高,可有效应对絮凝剂调节的滞后问题,为选煤厂浓缩池溢流浓度控制提出了新的方案。

智能重介系统在大海则选煤厂的研究及应用

针对常规设计中重介系统存在的分选密度波动大、需根据煤质化验结果进行人工调节等问题,通过对分选流程的深度剖析,研发了基于神经网路、模糊控制技术的智能重介控制系统,借助全自动采制样测灰仪,实现了分选密度自稳定、自调节,全过程无人操作和智能化运行;同时,对磁选和喷水环节进行智能控制,减少介质损耗,为重介分选提供良好的分选环境。

基于人工智能的智能垃圾分类系统设计与实现

随着环境问题的加剧,垃圾分类受到广泛关注。文中设计了一种基于人工智能的智能垃圾分类系统,包括垃圾识别和分类两部分。系统利用卷积神经网络模型实现垃圾准确识别,通过模糊逻辑理论进行高效分类。测试显示,相比传统方式,本系统大幅提高了垃圾分类准确率,且运行稳定,减少了人力投入。此外,系统利用人工智能技术,可以对未分类垃圾进行自我学习,纠正分类错误,以提高垃圾分类的准确性。该系统的设计与实现,不仅有助于提高我国垃圾分类的普及率和准确率,也为我国环保事业提供了新的解决方案。

Model of Land Suitability Evaluation Based on Computational Intelligence

A novel model of land suitability evaluation is built based on computational intelligence (CI). A fuzzy neural network (FNN) is constructed by the integration of fuzzy logic and artificial neural network (ANN). The structure and process of this network is clear. Fuzzy rules (knowledge) are expressed in the model explicitly, and can be self-adjusted by learning from samples. Genetic algorithm (GA) is employed as the learning algorithm to train the network, and makes the training of the model efficient. This model is a self-learning and self-adaptive system with a rule set revised by training.

Employing Computational Intelligence to Generate More Intelligent and Energy Efficient Living Spaces

Our living environments are being gradually occupied with an abundant number of digital objects that have networking and computing capabilities. After these devices are plugged into a network, they initially advertise their presence and capabilities in the form of services so that they can be discovered and, if desired, exploited by the user or other networked devices. With the increasing number of these devices attached to the network, the complexity to configure and control them increases, which may lead to major processing and communication overhead. Hence, the devices are no longer expected to just act as primitive stand-alone appliances that only provide the facilities and services to the user they are designed for, but also offer complex services that emerge from unique combinations of devices. This creates the necessity for these devices to be equipped with some sort of intelligence and self-awareness to enable them to be self-configuring and self-programming. However, with this ＂smart evolution＂, the cognitive load to configure and control such spaces becomes immense. One way to relieve this load is by employing artificial intelligence （AI） techniques to create an intelligent ＂presence＂ where the system will be able to recognize the users and autonomously program the environment to be energy efficient and responsive to the user＇s needs and behaviours. These AI mechanisms should be embedded in the user＇s environments and should operate in a non-intrusive manner. This paper will show how computational intelligence （CI）, which is an emerging domain of AI, could be employed and embedded in our living spaces to help such environments to be more energy efficient, intelligent, adaptive and convenient to the users.

AN INTELLIGENT DECISION SUPPORT SYSTEM （IDSS） IN THE OPERATION PROCESS OF ELECTRIC FURNACE FOR CLEANING SLAG

In this paper, the structure and function of the IDSS in the operation process of electric furnace for cleaning slag are presented and the fuzzy neural network decision model (FNNDM) in the IDSS is specially suggested. The IDSS possesses selflearning and adaptive properties, and has been used for managing and analyzing the optimal operational conditions since June 1992. Electric energy consumption has been reduced remarkably and the coefficient of recovery of cobalt and nickel has been increased.

Intelligent Medical Diagnostic System for Hepatitis B

The hepatitis B virus is the most deadly virus,which significantly affects the human liver.The termination of the hepatitis B virus is mandatory and can be done by taking precautions as well as a suitable cure in its introductory stage;otherwise,it will become a severe problem and make a human liver suffer from the most dangerous diseases,such as liver cancer.In this paper,two medical diagnostic systems are developed for the diagnosis of this life-threatening virus.The methodologies used to develop thesemodels are fuzzy logic and the neuro-fuzzy technique.The diverse parameters that assist in the evaluation of performance are also determined by using the observed values from the proposed system for both developedmodels.The classification accuracy of a multilayered fuzzy inference system is 94%.The accuracy with which the developed medical diagnostic system by using Adaptive Network based Fuzzy Interference System(ANFIS)classifies the result corresponding to the given input is 95.55%.The comparison of both developed models on the basis of their performance parameters has been made.It is observed that the neuro-fuzzy technique-based diagnostic system has better accuracy in classifying the infected and non-infected patients as compared to the fuzzy diagnostic system.Furthermore,the performance evaluation concluded that the outcome given by the developed medical diagnostic system by using ANFIS is accurate and correct as compared to the developed fuzzy inference system and also can be used in hospitals for the diagnosis of Hepatitis B disease.In other words,the adaptive neuro-fuzzy inference system has more capability to classify the provided inputs adequately than the fuzzy inference system.

The Role of Computational Intelligence in Sensory Evaluation

Sensory evaluation is the evaluation of signals that a human receives via its senses of sight, smell, taste, touch and hearing. In today’s industrial companies, sensory evaluation is widely used in quality inspection of products, in marketing study and in many other fields such as risk evaluation, investment evaluation and safety evaluation. In practice, setting up a suitable mathematical formulation, an efficient working procedure and a pertinent computing method for sensory evaluation is quite difficult because of uncertainty and imprecision in sensory panels and their results involving linguistic expressions, non normalized data, data reliability, etc. At the present a prime problem of the practitioner is not the lack of useful methods but the lack of transparency in this area. In this tutorial lecture, we briefly describe some of the technology in the computational intelligence (CI) areas that has been developed for application to sensory evaluation and related fields. Moreover, we will illustrate the role of CI in sensory evaluation related applications from some recent publications.