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Decision making algorithm for an autonomous guide-robot using fuzzy logic

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2018年第4期9卷 1177-1189页

作者： Javier Rainer, J. Cobos-Guzman, Salvador Galan, Ramon Univ Int La Rioja UNIR Av GranVia Rey Juan Carlos I 41 Logrono 26002 La Rioja Spain Univ Politecn Madrid Ctr Automat & Robot CAR UPM CSIC C Jose Gutierrez Abascal 2 E-28006 Madrid Spain

This paper presents a novel method to generate optimal presentations for a Guide-Robot that explains the exhibition to different types of audience. The generation of automatic presentations are selected dynamically regarding different criteria and an intelligent algorithm is implemented based on fuzzy logic to decide which presentation is the optimal. Thus, the decision-making mechanism prioritizes values of the presentation by means of a quality index that the fuzzy logic algorithm generates. The learning phase is produced using feedback information from the public that can modify the previous quality criteria to evaluate if the task is good or bad. Thus, the robot can learn by means of the interaction of the public and with the combination of fuzzy logic for selecting the optimal time and presentation that require a specific guided visit. To ensure that the learning phase is working properly, the robot has been tested in museums where there are interactions between the public and the robot.

关键词： Decision making Fuzzy logic Learning intelligent algorithm Automatic presentations

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Fuzzy neural network control algorithm for asymmetric building structure with active tuned mass damper

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JOURNAL OF VIBRATION AND CONTROL 2020年第21-22期26卷 2037-2049页

作者： Yan, Xiao Xu, Zhao-Dong Shi, Qing-Xuan Xian Univ Architecture & Technol Sch Civil Engn Xian Peoples R China Minist Educ XAUAT Key Lab Struct Engn & Earthquake Resistance Xian Peoples R China Southeast Univ Sch Civil Engn Nanjing Peoples R China

Asymmetric structures experience torsional effects when subjected to seismic excitation. The resulting rotation will further aggravate the damage of the structure. A mathematical model is developed to study the translation and rotation response of the structure during seismic excitation. The motion equations of the structures which cover the translation and rotation are obtained by the theoretical derivations and calculations. Through the simulated computation, the translation and rotation response of the structure with the uncontrolled system, the tuned mass damper control system, and active tuned mass damper control system using linear quadratic regulator algorithm are compared to verify the effectiveness of the proposed active control system. In addition, the linear quadratic regulator and fuzzy neural network algorithm are used to the active tuned mass damper control system as a contrast group to study the response of the structure with different active control method. It can be concluded that the structure response has a significant reduction by using active tuned mass damper control system. Furthermore, it can be also found that fuzzy neural network algorithm can replace the linear quadratic regulator algorithm in an active control system. Because fuzzy neural network algorithm can control the process on an uncertain mathematical model, it has more potential in practical applications than the linear quadratic regulator control method.

关键词： Asymmetric structure torsion seismic response analysis intelligent algorithm active control

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Regression modeling and multi-objective optimization of rheological behavior of non-Newtonian hybrid antifreeze: Using different neural networks and evolutionary algorithms

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2024年 155卷

作者： Jin, Weihong Basem, Ali Baghoolizadeh, Mohammadreza Kamoon, Saeed S. Al-Yasiri, Mortatha Salahshour, Soheil Hekmatifar, Maboud Chongqing Coll Humanities Sci & Technol Sch Comp Engn Chongqing 401524 Hechuan Peoples R China Warith Al Anbiyaa Univ Fac Engn Karbala 56001 Iraq Shahrekord Univ Dept Mech Engn Shahrekord *** Iran Madenat Alelem Univ Coll Nucl Phys Baghdad 10006 Iraq Al Amarah Univ Coll Dept Chem Engn & Petr Ind Maysan Iraq Istanbul Okan Univ Fac Engn & Nat Sci Istanbul Turkiye Bahcesehir Univ Fac Engn & Nat Sci Istanbul Turkiye Lebanese Amer Univ Dept Comp Sci & Math Beirut Lebanon Islamic Azad Univ Dept Mech Engn Khomeinishahr Branch Khomeinishahr Iran

The research used an artificial neural network (ANN) model to examine the rheological properties of hybrid nonNewtonian ferrofluids (HNFFs) composed of Fe-CuO, water, and ethylene glycol. The performance of neural network was optimized using seven regression methods (RMs), namely Group Method of Data Handling (GMDH), Decision Tree (D-Tree), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Extreme Learning Machine (ELM), Radial Basis Function (RBF), and Multiple Linear Regression (MLR). The findings highlighted GMDH method's superior performance when compared to neural networks. R and RMSE values attained by GMDH for the objective function (OF) mu nf were 0.99436 and 2.0135, respectively. For the torque function OF, the values were 0.97652 and 4.8952. Margin of difference (MOD) calculations across various algorithms, such as MLP, SVM, RBF, D-Tree, ELM, MLR, and GMDH-Algos revealed significant disparities, indicating GMDH's efficacy. Comparison of R, RMSD, and standard deviation values between GMDH and MLR algorithms further underscored performance discrepancies. Specific parameters for which NSGA II Algo was rated highest among evaluation indices were as follows: a crossover rate of 0.7, a mutation rate of 0.02, a population size of 50, and 500 generations. Post-optimization, optimal values for mu nf and torque (To) were determined as 6.595 and 3.543, respectively, with corresponding values for 9, T, and gamma obtained as 0.185, 49.372, and 3.163, respectively. This comprehensive analysis sheds light on the effectiveness of various regression methods in modeling the rheological behavior of hybrid non-Newtonian ferrofluids, contributing to advancements in fluid dynamics research.

关键词： Regressions Rheological behavior Non-Newtonian Ferrofluids intelligent algorithm ANN

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Reinforcement Learning-Based Routing Protocols for Vehicular Ad Hoc Networks: A Comparative Survey

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IEEE ACCESS 2021年 9卷 27552-27587页

作者： Nazib, Rezoan Ahmed Moh, Sangman Chosun Univ Dept Comp Engn Gwangju 61452 South Korea

Vehicular-ad hoc networks (VANETs) hold great importance because of their potentials in road safety improvement, traffic monitoring, and in-vehicle infotainment services. Due to high mobility, sparse connectivity, road-side obstacles, and shortage of roadside units, the links between the vehicles are subject to frequent disconnections;consequently, routing is crucial. Recently, to achieve more efficient routing, reinforcement learning (RL)-based routing algorithms have been investigated. RL represents a class of artificial intelligence that implements a learning procedure based on previous experiences and provides a better solution for future operations. RL algorithms are more favorable than other optimization techniques owing to their modest usage of memory and computational resources. Because a VANET deals with passenger safety, any kind of flaw is intolerable in VANET routing. Fortunately, RL-based algorithms have the potentials to optimize the different quality-of-service parameters of VANET routing such as bandwidth, end-to-end delay, throughput, control overhead, and packet delivery ratio. However, to the best of the authors' knowledge, surveys on RL-based routing protocols for VANETs have not been conducted. To fulfill this gap in the literature and to provide future research directions, it is necessary to aggregate the scattered works on this topic. This study presents a comparative investigation of RL-based routing protocols, by considering their working procedure, advantages, disadvantages, and applications. They are qualitatively compared in terms of key features, characteristics, optimization criteria, performance evaluation techniques, and implemented RL techniques. Lastly, open issues and research challenges are discussed to make RL-based VANET routing protocols more efficient in the future.

关键词： Vehicular ad hoc networks Routing Routing protocols Quality of service Reinforcement learning Optimization Safety Vehicular ad hoc network routing protocol reinforcement learning Q-learning intelligent algorithm quality-of-service routing intelligent transportation system

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Different states of multi-block based forecast engine for price and load prediction

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2019年 104卷 423-435页

作者： Gao, Wei Darvishan, Ayda Toghani, Mohammad Mohammadi, Mohsen Abedinia, Oveis Ghadimi, Noradin Yunnan Normal Univ Sch Informat Sci & Technol Kunming 650500 Yunnan Peoples R China Univ Houston Dept Ind Engn Houston TX 77004 USA Islamic Azad Univ Dept Mech Najafabad Branch Najafabad Iran Payame Noor Univ Dept Elect Engn Tehran Iran Budapest Univ Technol & Econ Budapest Hungary Islamic Azad Univ Ardabil Branch Young Researchers & Elite Club Ardebil Iran

This work proposes different prediction models based on multi-block forecast engine for load and price forecast in electricity market. Due to high correlation of load and price signals, the density of this reaction can affect the demand curve and shift it in market. Furthermore, to improve the operation and planning improvement in the power system, an accurate prediction model can play an important role. So, in this paper, a complex prediction approach is presented based on feature selection, and multi-stage forecast engine. The forecast engine is comprised of multi-block neural network (NN) and optimized by an intelligent algorithm to increase the training mechanism and forecasting abilities. Moreover, different models of multi-block forecast engine are presented in this paper to choose the effective model. In other words, different combinations of NN are tested in the same prediction condition to show their abilities. The proposed model is tested over real-world engineering test cases through comparison with other prediction methods. Obtained results demonstrate the validity of the proposed model.

关键词： Load and price Feature selection intelligent algorithm Multi-block forecast engine

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Design and analysis of a novel overload current estimation method for traditional miniature circuit breaker

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IET ELECTRIC POWER APPLICATIONS 2020年第10期14卷 1898-1905页

作者： Lv, Sen Zong, Ming Yu, Siyang Shenyang Univ Technol Sch Elect Engn Shenyang 110870 Peoples R China

The power grid is developing towards intelligence. Therefore, as important electrical equipment of the power grid, miniature circuit breaker (MCB) should need to meet the requirements of the smart grid for terminal apparatuses. However, because the bending process of bimetal widely used in MCB overload protection is dispersive, which is difficult to realise electronisation, it is urgent to study the intelligent algorithm of MCB to protect the power grid well. For this purpose, a current calculation method based on temperature rise caused by load current of traditional non-communicable MCB is proposed in this study. Compared with the present current calculation method, there is no iron core saturation problem even in the case of a huge load current. Also, in order to solve the problem of long time delay for stable temperature rise, a recursive algorithm that can rapidly calculate stable temperature rise is proposed. Furthermore, the current can be calculated rapidly. The influence of the initial temperature rise on the current calculation is also taken into account in the proposed algorithm. The simulation and experiment results show that the speed of stable temperature rise obtained by the recursive algorithm can meet the requirement of overload protection, and the error of calculating current is less than 5%.

关键词： smart power grids circuit breakers bending power system protection smart grid MCB overload protection intelligent algorithm power grid current calculation method noncommunicable MCB iron core saturation problem load current recursive algorithm miniature circuit breaker overload current estimation method electrical equipment terminal apparatuses bending process temperature rise

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Extremum Inference algorithm: A Clever Optimization algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2018年第2期102卷 1617-1632页

作者： Zhang, Hongjun Kang, Xingdang Zhang, Rui Yin, Chengxiang Wang, Han Army Engn Univ Nanjing Jiangsu Peoples R China

Most intelligent algorithms can't predict the existence and possible position of new better solution. This deficiency not only leads to the waste of computing resources, it also affects the efficiency to find the optimal solution. To fix this problem, this paper proposes a new algorithm named extremum inference algorithm. During the iterations, this algorithm uses high efficient algorithms to simultaneously searches for both local maxima and local minima, and then uses a simple principle to infer the existences and possible positions of unknown extremums. By doing this, the extremum inference algorithm can decide if there possibly exists new extremums and search for new extremums in the most possible regions. A strong version of extremum inference algorithm can even provide an approach to estimate searching accuracy, which is a measurement that can help to determine if the global optimal has been found. Our examples demonstrate that the extremum inference algorithm can efficiently solve multimodal problems which are harder to solve than other problems.

关键词： Extremum inference algorithm Extremum inference and diffusion phenomenon intelligent algorithm Gradient descent

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Expected values of functions of fuzzy variables

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JOURNAL OF intelligent & FUZZY SYSTEMS 2006年第5期17卷 471-478页

作者： Zhu, Yuanguo Ji, Xiaoyu Nanjing Univ Sci & Technol Dept Appl Math Nanjing 210094 Peoples R China Tsing Hua Univ Dept Math Sci Beijing 100084 Peoples R China

Based on the expected value of a fuzzy variable, the expected value of function of a fuzzy variable is studied. A fuzzy expected value model is solved by an intelligent algorithm mixed integral sum approximation and g... 详细信息

关键词： fuzzy variable expected value fuzzy expected value model intelligent algorithm

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A novel structural damage detection strategy based on VMD-FastICA and ESSAWOA

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JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING 2023年第1期13卷 149-163页

作者： Fan, Qian Chen, Zhenjian Xia, Zhanghua Zhang, Wei Fuzhou Univ Coll Civil Engn Fuzhou 350108 Peoples R China Southeast Univ Sch Civil Engn Nanjing 211189 Peoples R China Fujian Acad Bldg Res Fuzhou 350025 Peoples R China

This paper proposes a novel two-stage structural damage detection strategy based on variational mode decomposition (VMD), fast independent component analysis (FastICA) and enhanced whale optimization algorithm integrated with Salp swarm algorithm (ESSAWOA). In the first stage, VMD and FastICA are utilized to decompose and process the initial response signals of the structure to detect the damage time preliminary. In the second stage, ESSAWOA algorithm is employed to identify the structural parameters (e.g., stiffness, mass or damping ratio) at different periods of time to determine the location and extent of the damage. To investigate the performance of the strategy, the simulation tests in six damage scenarios are carried out on a three-story numerical model. Then, the superiority of the parameter identification method based on ESSAWOA is verified on a seven-story simulation model. Finally, experimental verification on a laboratory seven-story steel frame is conducted to further validate the accuracy of the proposed strategy. The results in both numerical simulations and experimental validation prove that the two-stage strategy can effectively detect the time, location and extent of the damage in the frame structure. Furthermore, it has good applicability and robustness.

关键词： Structural damage detection Variational mode decomposition Independent component analysis intelligent algorithm Signal processing

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Comparing deep learning with several typical methods in prediction of assessing chlorophyll-a by remote sensing: a case study in Taihu Lake, China

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WATER SUPPLY 2021年第7期21卷 3710-3724页

作者： Zhao, Xiaolan Xu, Haoli Ding, Zhibin Wang, Daqing Deng, Zhengdong Wang, Yi Wu, Tingfong Li, Wei Lu, Zhao Wang, Guangyuan Army Engn Univ Def Engn Coll Nanjing 210007 Jiangsu Peoples R China Chinese Acad Sci Nanjing Inst Geog & Limnol Nanjing 210008 Jiangsu Peoples R China Jiangsu Geol Survey & Res Inst Nanjing 210018 Jiangsu Peoples R China

Chlorophyll-a (Chl-a) is an important index in water quality assessment by remote sensing technology. For the study of Chl-a value measurement in rivers or lakes, there were many classical methods, such as curve fitting, back propagation (BP) neural network and radial basis function (RBF) neural network, and all of them had some corresponding applications. With the rise of computer power and deep learning, this study intended to analyze the measurement of water quality and Chl-a in deep learning (DL) and to compare with several classical methods, so as to explore and develop better methods. Taking Taihu Lake of China as the case, this study adopted the measured data of Chl-a in Taihu Lake in 2017 and the data corresponding to the same time of Landsat8. In this study, the four methods were used to inverse the distribution of Chl-a value in Taihu Lake. From the results of inversion, power curve fitting model with the n-ary sumation Residual(2) of fitting of 90.469 and inverse curve fitting model with the one of 602156.608 had the better results than other curve fitting models, however, were not as accurate as the machine learning method from segmentation results images. The machine learning method had better accuracy than the curve fitting methods from segmentation results images. The mean squared error of testing of the three methods of machine learning (BP, RBF, DL) were respectively 1.436, 4.479, 4.356. Thus, the BP method and DL method had better results in this study.

关键词： comparative study intelligent algorithm inversion lake water water quality

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