This paper addresses the criterion of the robust controller design for the solution of a number of fuzzy c-means clustering algorithms, which are robust to plant parameter disturbances and controller gain variations. ...
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This paper addresses the criterion of the robust controller design for the solution of a number of fuzzy c-means clustering algorithms, which are robust to plant parameter disturbances and controller gain variations. The control and stability problems in the present nonlinear systems are studied based on a Takagi-Sugeno (T-S) fuzzy model. A lately and important proposed integral inequality is considered and selected according to the method of the free weight matrix, with these comparatively flexible stability criteria which are determined in the numerical form of linear matrix inequalities (LMIs). Under the condition of the premise in which the controller and the control system partake the same rules, the method does not inquire the same number of membership functions and mathematical rules. In addition, the improved control is used for large-scale nonlinear systems, where the stability criterion of the closed T-S fuzzy system is obtained through LMI and rearranged through the membership function for machine learning . The close-loop controller criteria are derived by using the Lyapunov energy functions to guarantee the stability of the system . Eventually, an instance is presented to reveal the efficacy of evolution.
Input variables selection(IVS) is proved to be pivotal in nonlinear dynamic system modeling. In order to optimize the model of the nonlinear dynamic system, a fuzzy modeling method for determining the premise structur...
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Input variables selection(IVS) is proved to be pivotal in nonlinear dynamic system modeling. In order to optimize the model of the nonlinear dynamic system, a fuzzy modeling method for determining the premise structure by selecting important inputs of the system is studied. Firstly, a simplified two stage fuzzycurves method is proposed, which is employed to sort all possible inputs by their relevance with outputs, select the important input variables of the system and identify the ***, in order to reduce the complexity of the model, the standard fuzzy c-means clustering algorithm and the recursive least squares algorithm are used to identify the premise parameters and conclusion parameters, respectively. Then, the effectiveness of IVS is verified by two well-known issues. Finally, the proposed identification method is applied to a realistic variable load pneumatic system. The simulation experiments indi cate that the IVS method in this paper has a positive influence on the approximation performance of the Takagi-Sugeno(T-S) fuzzy modeling.
Education greatly aids in the process of students' growth;therefore, education institutions try to provide high-quality education to their students. A possible remedy to provide high-quality education is by discov...
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Education greatly aids in the process of students' growth;therefore, education institutions try to provide high-quality education to their students. A possible remedy to provide high-quality education is by discovering knowledge from educational data. However, accurately evaluating students' performance is very challenging due to different sources and structures of educational data. In addition, different teaching strategies are required because students' learning ability are different. One way to discover the hidden knowledge from educational data is the use of clusteringalgorithms, which are capable of mining interesting patterns from educational data. Thus, this study presents a fuzzy c-means clustering algorithm using 2D and 3D clustering to evaluate students' performance based on their examination results (the examination grades from college of computer Science and Technology, Huaqiao University for students enrolled in year 2014). Based on the experimental results from 2D and 3D clustering for evaluating students' performance, the educators can better understand the students' performance so as to build a pedagogical basis for decisions. Students can also receive some recommendations from the mining results about their performance.
Gravity inversion requires much computation,and inversion results are often *** first problem is often due to the large number of grid *** detection method,i.e.,tilt angle method of analytical signal amplitude(TAS),he...
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Gravity inversion requires much computation,and inversion results are often *** first problem is often due to the large number of grid *** detection method,i.e.,tilt angle method of analytical signal amplitude(TAS),helps to identify the boundaries of underground geological anomalies at different depths,which can be used to optimize the grid and reduce the number of grid *** requirement of smooth inversion is that the boundaries of the meshing area should be continuous rather than *** this paper,the optimized meshing strategy is improved,and the optimized meshing region obtained by the TAS is changed to a regular region to facilitate the smooth *** the second problem,certain constraints can be used to improve the accuracy of *** results of analytic signal amplitude(ASA)are used to delineate the central distribution of geological *** propose a new method using the results of ASA to perform local constraints to reduce the non-uniqueness of *** guided fuzzyc-means(FcM)clusteringalgorithmcombined with priori petrophysical information is also used to reduce the non-uniqueness of gravity *** Open Acc technology is carried out to speed up the computation for parallelizing the serial program on *** general,the TAS is used to reduce the number of grid *** local weighting and priori petrophysical constraint are used in conjunction with the FcM algorithm during the inversion,which improves the accuracy of *** inversion is accelerated by the Open Acc technology on *** proposed method is validated using synthetic data,and the results show that the efficiency and accuracy of gravity inversion are greatly improved by using the proposed method.
In conventional fuzzy c-means clustering algorithms, each data and each feature are treated equally, the clustering performance is sensitive to the noise points;in existing weighting clusteringalgorithms, few studies...
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In conventional fuzzy c-means clustering algorithms, each data and each feature are treated equally, the clustering performance is sensitive to the noise points;in existing weighting clusteringalgorithms, few studies have focus on data weighting and feature weighting simultaneously, besides, the same data in different clusters is treated equally. To address this issue, in this paper, taking the different data weights in different clusters and the different feature weights in different clusters into consideration, we present a new robust fuzzyc-meansclustering framework. For the first time, we propose a whole new idea that the same data in different clusters should have different importance, the different data in a cluster should have different importance, as well;By the new data weighting method, the proposed clusteringalgorithmcan weaken the impact of noise points on the formation of each clusteringcenter, which could enhance the robustness of clustering;to stimulate more data and more features to take part in the process of clustering and to avoid overfitting, we add l(2)-norm regularization of the data weights and l(2)-norm regularization of the feature weights to the objective function. Then, based on the presented objective function, we get the scientific update rules of the different data weights in different clusters, the different feature weights in different clusters, the membership degrees, and the cluster centers, during each iteration. To assess the performance of the new fuzzyc-means framework, experimental verifications on synthetic dataset and real-world datasets are conducted, experimental results have shown that the new algorithmcan achieve better clustering performances in comparison to other related clustering methods.
Purpose This paper aims to address the robust controller design problem for a class of fuzzy c-means clustering algorithm that is robust against both the plant parameter perturbations and controller gain variations. B...
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Purpose This paper aims to address the robust controller design problem for a class of fuzzy c-means clustering algorithm that is robust against both the plant parameter perturbations and controller gain variations. Based on Takagi-Sugeno (T-S) fuzzy model description, the stability and control problems of nonlinear systems are studied. Design/methodology/approach A recently proposed integral inequality is selected based on the free-weight matrix, and the less conservative stability criterion is given in the form of linear matrix inequalities (LMIs). Findings Under the premise that the controller and the system share the same, the method does not require the number of membership functions and rules. Originality/value The closed-loop controller criterion is derived by energy functions to guarantee the stability of systems. Finally, an example is given to demonstrate the results.
The present study reports an investigation on identification of priority areas of improvement for small passenger car segment in the Indian market. A total of 750 responses on importance and satisfaction rating of 16 ...
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The present study reports an investigation on identification of priority areas of improvement for small passenger car segment in the Indian market. A total of 750 responses on importance and satisfaction rating of 16 attributes in a 7-point Likert-type ordinal rating scale were collected from small car owners in Delhi using a paper and pencil instrument. Revised importance-performance analysis with fuzzy c-means clustering algorithm was used to identify the priority areas of improvement in small passenger cars. The analysis technique effectively identified the factor structure of consumer satisfaction and derived various management schemes for small passenger cars by analysing consumers' importance and satisfaction data. The priority areas of improvement were obtained by comparing the factor structure with the management scheme. The results indicate that safety, security and advanced vehicle technology options are the priority areas of improvement for small passenger cars. The study identified that the consumers are satisfied with the performance of purchase price, driving range, annual fuel cost, annual maintenance cost, top speed, seating comfort, appearance/style, emission, acceleration time and resale value in small cars, indicating the need to retain these attributes at their present levels to maintain competitive advantage. The engine power is identified under 'possible overkill', which indicates an opportunity for automobile manufacturers to possibly reduce the engine power to save some resources and reallocate the same for improvement of priority attributes. The findings of the present study provide valuable insight into the product specifications that the automobile manufacturers should focus on, to make new-generation small cars more attractive to Indian consumers.
Uncertain information in the securities market exhibits fuzziness. In this article, expected returns and liquidity are considered as trapezoidal fuzzy numbers. The possibility mean and mean absolute deviation of expec...
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Uncertain information in the securities market exhibits fuzziness. In this article, expected returns and liquidity are considered as trapezoidal fuzzy numbers. The possibility mean and mean absolute deviation of expected returns represent the returns and risks of securities assets, while the possibility mean of expected turnover represents the liquidity of securities assets. Taking into account practical constraints such as cardinality and transaction costs, this article establishes a fuzzy portfolio model with cardinality constraints and solves it using the differential evolution algorithm. Finally, using fuzzyc -meansclusteringalgorithm, 12 stocks are selected as empirical samples to provide numerical calculation examples. At the same time, fuzzyc -meansclusteringalgorithm is used to cluster the stock yield data and analyse the stock data comprehensively and accurately, which provides a reference for establishing an effective portfolio.
Stud shear connectors are popular connectors used in steel-concrete composite structures. Assessing the condition and damage process of studs embedded in concrete is difficult. This paper proposes to assess the condit...
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Stud shear connectors are popular connectors used in steel-concrete composite structures. Assessing the condition and damage process of studs embedded in concrete is difficult. This paper proposes to assess the condition and damage process of stud shear connectors using an acoustic emission (AE) technique. Nine steel-concrete push-out specimens with stud shear connectors were tested, and AE sensors were used to monitor the damage process and explore the damage mechanism. The test results showed that the specimens failed due to stud fracture, accompanied by concrete cracks. The damage process included three primary stages featuring different characteristics of AE energy. An unsupervised machine learning algorithmcalled fuzzyc-meansclustering was used to analyze the AE signals, and AE events were classified into three clusters, and various damage modes (i.e., concrete cracks and steel-concrete interface debonding, stud fracture, and concrete crushing) were identified based on the parameter characteristics and positions of AE events.
Diabetes is a metabolic sickness that remains to be a major universal problem because it influences the health of the entire population. Over the years several researchers have attempted to build a model for predictin...
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ISBN:
(数字)9781665408370
ISBN:
(纸本)9781665408387;9781665408370
Diabetes is a metabolic sickness that remains to be a major universal problem because it influences the health of the entire population. Over the years several researchers have attempted to build a model for predicting diabetes accurately. However, this field study remains a challenge because of the unavailability of dataset and prediction models which forces the researchers to utilize ML (Machine Learning) algorithm and Big data analytics. The primary objective of the study was to identify how ML and Big data analytics can be adopted in diabetes. The evaluation of outcomes demonstrates that the proposed ML-based technique might achieve 8.6% of accuracy. The authors performed a review of literature on the ML models and came out with the suggestion of an Automatic Intelligent method for the prediction of diabetes based on the findings. The authors explored ML approaches and proposed and developed an intelligent ML-based architecture for diabetes prediction. In this work, the authors used fuzzy mean, SVM learning models for the prediction of diabetes. A firefly algorithm optimized classifies was utilized for selecting the best features. It is proposed in the work, that an exclusive automatic intelligent optimize diabetic prediction model is built by utilizing ML approaches. The framework was created following a thorough analysis of existing prediction approaches in the literature and assessing their relevance to diabetes. The authors presented the training methods, model evaluation strategies, and the challenges allied to the prediction of diabetes also the remedies they provide by using the framework. Health professionals, students, researchers, and stakeholders working in diabetes forecast research development may benefit from the findings of the study. The proposed work achieves an accuracy of 86 percent with low error rates.
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