Journal of Intelligent & Fuzzy Systems - Volume 38, issue 4

Authors: Zhou, Jingyong | Guo, Yuan | Sun, Yu | Wu, Kai

Article Type: Research Article

Abstract: With the rapid development of database and Internet technologies, data collection and storage is possible. It is often impossible to correctly analyze the valuable information contained in the data, and it becomes more difficult to obtain valuable information. Therefore, it faces the status of “rich data and scarce knowledge”. Traditional information processing technology can no longer meet the needs of reality. There is an urgent need for more capable and effective information processing skills to help us analyze the information we need from big data and guide us to make the right decisions. Data mining technology is born in the …background. Data mining technology is one of the effective methods to solve rich data and improve lack of knowledge. It is also one of the main research topics in the field of information science. Related research and applications have greatly improved people’s decision-making ability. It has been recognized as one of the extremes of data research and has a very broad application prospect. Large databases often contain redundant and unnecessary attributes for many search rules, so the ability to remove duplicate attributes can greatly improve the clarity of potential system knowledge and reduce the time complexity of finding rules. At the same time, it enables efficient operation and improved adaptability. Because the structure of the neural network is variable, it has strong self-organization, self-learning, nonlinearity and high fault tolerance, but the ability to express and interpret knowledge is very poor. The network parameters lack physical meaning and learning. Therefore, it has become an inevitable trend to form a fuzzy neural network combining the characteristics of the two. Therefore, exploring the organic combination between rough sets and fuzzy neural networks is undoubtedly of great significance for data mining technology research. This paper proposes a data mining method based on the combination of rough set and fuzzy neural network technology. Using the approximate set to discover the rules of the database rules, the initial structure of the fuzzy neural network is determined, and the training data is used to train the network. Since the fuzzy neural network thus constructed has a good topology of data distribution features from the beginning, the network scale can be greatly reduced and the network training speed can be improved. Show more

Keywords: Data mining, rough set, fuzzy logic, fuzzy neural network

DOI: 10.3233/JIFS-179594

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3717-3725, 2020

Authors: Zhao, Wei | Luo, Zeju

Article Type: Research Article

Abstract: With the advent of Web 3.0 era, the number and complexity of Web pages in Bayesian networks have shown an explosive growth trend. Accompanying this is the geometric growth of information contained in Web pages. Web text data in Bayesian networks usually hide rich knowledge and rules of user value. However, due to the semi-structured, real-time and discrete characteristics of Web text data, it is difficult for users to obtain the knowledge they need directly from such complex data sets. The emergence of fuzzy mathematics provides a good research idea and method for solving such problems. It can use the …idea of fuzzy mathematics to analyze the practical problems in text data. Therefore, how to effectively mine the Web text data information and knowledge that users really care about from Bayesian network, and present it in a way that users can understand, it is a very popular research topic at present. In this paper, we select the text of Bayesian network: microblog data for experiments. User data model of microblog is established by using relevant knowledge of fuzzy theory. The concept of fuzzy measure is introduced to calculate the non-additive measure value under the interaction relationship between the detection indicators. Determine the membership function relationship between the detection user and the text data, calculate the integral values of Choquet integral, Sugeno integral and Wang integral of the membership function under the non-additive measure, the final valuable web text data is judged by integral value. On the basis of the above research contents, the research results of Web text mining technology and fuzzy arithmetic mathematics are combined, design and implement information acquisition and analysis for Bayesian network community. The recall rate obtained by the experimental method in this paper is as low as 4%, and tends to be more stable. Show more

Keywords: Fuzzy algorithm, Bayesian network, data mining, web text data mining

DOI: 10.3233/JIFS-179595

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3727-3735, 2020

Authors: Man, Na | Wang, Kechao | Liu, Lin

Article Type: Research Article

Abstract: A fuzzy system is a system that defines input, output, and state variables on a fuzzy set and is a generalization of a deterministic system. The fuzzy system begins at the macro level and covers the fuzzy features of human brain thinking. It has advantages in describing advanced knowledge. Fuzzy sets can mimic the comprehensive conclusions of people solving fuzzy information problems, which are difficult to solve by conventional mathematical methods, so computer applications can be extended to humanities, social sciences and complex systems. In this way, it can better solve nonlinear problems and is widely used in automatic control, …decision analysis, time series signal processing, economic information systems, medical diagnostic systems, and earthquake prediction systems. This paper aims to study the data mining algorithm of fuzzy systems based on fuzzy sets. By using the powerful fuzzy data modeling function of fuzzy theory, it combines with other intelligent processing methods, and makes practical use in industrial life. By comparing the application of fuzzy set data mining and algorithm, it is found that in the application state, the economic benefits of the factory are improved by 36% and the production efficiency is improved by 25% under the application of data mining and algorithm. The research data shows that the data mining and recommendation algorithms of fuzzy sets are beneficial to the development and operation of the factory. The research results show that compared with the conventional production and processing plan, the technology uses fuzzy set theory to transform the fuzzy attributes, which is more advantageous in scientific and technical systems and algorithms with its scientificity, accuracy, innovation and extensiveness. Show more

Keywords: Data mining, recommendation algorithm, fuzzy system, fuzzy set

DOI: 10.3233/JIFS-179596

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3737-3745, 2020

Authors: Zhang, Jing

Article Type: Research Article

Abstract: In recent years, with the rapid increase in the number of electric vehicles in China, the accidents caused by electrical system failures are also increasing year by year. Therefore, it is necessary to carry out reliability analysis on the electrical systems of electric vehicles. Traditional reliability analysis cannot be quantitatively evaluated and it is not possible to accurately diagnose multiple fault conditions of the system. Aiming at this problem, this paper combines fuzzy mathematics theory with fault tree analysis, and establishes a multi-state fuzzy fault tree to analyze the reliability of pure electric bus high-voltage electrical system, including qualitative analysis …and quantitative analysis. The results show that the multi-state fuzzy fault tree reliability analysis method can accurately describe the various fault states of the high-voltage electric system of pure electric passenger car, and can quantitatively evaluate the reliability, which has great practical significance. Show more

Keywords: Polymorphic fuzzy fault tree, eliability analysis

DOI: 10.3233/JIFS-179597

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3747-3754, 2020

Authors: Chen, Ying | Qi, Pengyuan | Liu, Songqing

Article Type: Research Article

Abstract: In order to effectively avoid the violent vibration in the process of mechanical processing and to achieve high efficiency and high precision machining of mechanical parts, the improved algorithm of adaptive neuro-fuzzy inference system is used to study the optimization of parameters in the process of side milling of mechanical parts, and an adaptive network structure is formed. It has the learning ability of artificial neural network and the expression ability of “if-then” of fuzzy reasoning system, which is a new prediction and control method. The results validate the applicability of the stability. The machined surface topography is measured and …the effect of flutter on the surface topography is analyzed. The three-dimensional stability of milling provides a theoretical basis for the rational selection of milling parameters of mechanical parts, the realization of stable milling and the improvement of processing efficiency. Thus, the relationship between the radial depth of cut, the axial depth of cut and the spindle speed is established, and the contour of material removal rate is obtained. The corresponding spindle speed and radial shear depth are obtained when the material removal rate is maximum. The reasonable selection of machining parameters is carried out in the region near the maximum spindle speed with stability. Show more

Keywords: Adaptive neuro-fuzzy reasoning system, machining, parameter optimization, machining error

DOI: 10.3233/JIFS-179598

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3755-3764, 2020

Authors: Wang, Hanxu | Yao, Yubing

Article Type: Research Article

Abstract:  With the development and popularization of electronic computers and the Internet, the problem of language barriers has once again become prominent in the new era, and people are more in need of machine translation. However, there is currently no suitable method for effective semantic ordering of English machine translation. In order to better perform semantic ordering on English machine translation, the article combines fuzzy theory to construct an algorithm model, and analyzes the experimental results through evaluation indicators. The results show that with the increase of training concentration training examples, the semantic parser can learn more natural language sentence analysis …methods from the training examples, and the natural language sentences that can be correctly parsed gradually increase, so with the training examples increased recall rate and F value gradually increased. The experimental results also show that the use of higher precision syntax analyzers can effectively improve the performance of statistical machine translation systems, whether in phrase-based or machine-based translation methods. Show more

Keywords: Semantic ordering, machine translation, fuzzy theory, semantic parser, system performance

DOI: 10.3233/JIFS-179599

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3765-3772, 2020

Authors: Gao, Huanbing | Lu, Shouyin | Wang, Tao

Article Type: Research Article

Abstract: The fuzzy control algorithm is used to establish the relation between the functions of motion of 6-DOF (degree of freedom) industrial robots on the rotation angle of each joint. The algorithm optimizes the motion path of the robot, thereby accelerating the increase in industrial productivity and promoting the development of industrial production. During the motions, the 6-DOF industrial robots have weak avoidance ability toward the encountered obstacles, which is not conducive to the safe production and will reduce industrial efficiency. Therefore, by analyzing and summarizing the previous researches, the fuzzy control algorithm is used to construct and optimize the kinematics …model, thereby proposing a method of robot motion path planning. Also, based on the unstructured operating environment, a multi-functional motion navigation system for 6-DOF industrial robots is proposed. The experimental results show that the fuzzy control algorithm can optimize the robot motion path, shorten the time of motion, and make the robots reach the destinations smoothly. The algorithm can avoid safety accidents in industrial production effectively, reduce casualties, improve industrial productivity, and promote the optimized allocation of human resources. The motion system of 6-DOF industrial robot based on fuzzy control algorithm has excellent practicability, which can promote the development of industrial production effectively and be widely applied to industrial production. Show more

Keywords: Fuzzy control, motion path, robot

DOI: 10.3233/JIFS-179600

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3773-3782, 2020

Authors: Xu, Huanchun | Hou, Rui | Fan, Jinfeng | Zhou, Liang | Yue, Hongxuan | Wang, Liusheng | Liu, Jiayue

Article Type: Research Article

Abstract: The data of time series are massive in quantity and not conducive to subsequent processing. Therefore, the unordered time series fuzzy clustering algorithm of adaptive incremental learning has been utilized to explore the segmentation of time series in further. The research results show that the emergence of incremental learning technology can solve such problems. Also, it can continuously accumulate and increase the data, as well as improving the learning accuracy. Incremental learning technology correctly processes, retains, and utilizes the historical results, thereby reducing the training time of new samples by using historical results. Therefore, the clustering algorithm mostly clusters the …cluster-liked shape of discrete datasets and uses the hierarchical clustering algorithm, which is more suitable for measuring the similarity of time series, to replace the Euclidean distance for distance metric and hierarchical clustering. The distance matrix update method is improved to reduce the computational complexity, which proves that the algorithm has higher clustering validity and reduces the operating time of the algorithm. Show more

Keywords: Time series, incremental learning, fuzzy clustering

DOI: 10.3233/JIFS-179601

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3783-3791, 2020

Authors: Liu, Yunpeng | Dong, Xinling

Article Type: Research Article

Abstract: Network virtualization technology releases human resources to some extent through network and cloud computing technology, reducing the workload of staff. The application of network virtualization technology in cloud computing data centers is based on this condition to improve work quality and efficiency. The purpose of this paper is to use the fuzzy algorithm to realize network virtualization of cloud computing data center. In this paper, we study the adaptive fuzzy control in depth, and conduct the practical application based on the basic knowledge of adaptive fuzzy control we learned, achieved “learn to use”. Apply the design of adaptive fuzzy control …to the load balancing algorithm of the network virtual cloud computing data center, realized the load balancing algorithm of the network virtual cloud computing data center based on adaptive fuzzy control. According to the load balancing algorithm based on adaptive fuzzy control to achieve this algorithm by using Internet knowledge, and designed the load balancing system of the whole network virtual cloud computing data center. Test the whole load balancing system which has been achieved, and obtained the performance variance curve of the system under different algorithms. Then obtained advantages and disadvantages of the algorithm by analyzing the experimental data. The experimental results show that the proposed method can effectively improve the execution performance of communication-intensive applications and ensure the stable execution of the application. At the same time, the algorithm inherits the advantages of the general fuzzy control load balancing algorithm. The stability is strong and the variance curve does not appear pulsed fluctuation. There is also no divergence phenomenon with time increased. Show more

Keywords: Fuzzy Algorithm, Network Virtualization, Cloud Computing, Network Load

DOI: 10.3233/JIFS-179602

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3793-3801, 2020

Authors: Chen, Guobin | Chen, Zhongsheng

Article Type: Research Article

Abstract: With the rapid development of the economy, the demand for urban land resources is also growing. How to make more rational use of land resources and make more rational planning of cities become a major problem in current economic development. At present, the use of remote sensing images to classify urban land use areas has become a research hot spot. However, the traditional classification accuracy rate using the maximum likelihood classification method needs to be improved. How to improve the classification accuracy rate of urban land use area of remote sensing image has become the focus and key of the …research. Both rough sets and fuzzy sets are mathematical methods for dealing with uncertain problems. The rough fuzzy sets generated by the combination of the two can solve the problem of information loss due to the rough set discretization process. Based on the advantages of fuzzy rough sets, this paper applies fuzzy rough sets to the study of urban land use area classification of remote sensing images, so as to improve the accuracy of urban land use area classification of remote sensing images. Firstly, the spectral features and texture features of the remote sensing image are extracted after preprocessing the remote sensing image. Secondly, using the domain relationship fuzzy rough set reduces the extracted features. Finally, the support vector machine is used to classify the reduced feature set, and the classification of urban land use area is realized. In the simulation experiment, the classification accuracy is evaluated by the overall classification accuracy, Kappa coefficient, and single class classification success index. The evaluation data shows that the fuzzy rough set is applied to the remote sensing image urban land use area classification, which has a good application effect. Show more

Keywords: Urban land use area, remote sensing image, fuzzy rough set, support vector machine

DOI: 10.3233/JIFS-179603

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 3803-3812, 2020