Fuzzy Pattern Recognition Theory Research Articles

Research on the Cost Control Model of Power Gird Maintenance Based on Fuzzy Pattern Recognition Theory

The power grid lines and equipment maintenance of power enterprises is a complicated construction process, the cost of which is affected by meteorological and geographical factors, and the influence mode is uncertain. Using fuzzy clustering method and the threshold intervals of the objective function in clusters, this paper builds a predictive control model to control the project cost. This model uses relative fuzzy operator to build fuzzy matrix, construct correlation between factors, and describe the factors’ effect. Extracting the cluster’s eigenfunction, and defining the boundaries of various clusters, we determined the type of the predicted points and the range of the objective function. When the actual cost of the maintenance project is within the range calculated by the cost model, then it is normal. If the actual cost exceeds this range, then further analysis of all the aspects of the cost is needed to find out the reason.

Fuzzy pattern recognition technique for crack propagation on earplate connection of guyed mast under wind load

A 2-stage damage identification technique is presented for identifying crack lengths on cable-tower connections of guyed masts. It is based on fuzzy pattern recognition theory and uses the cable force and the strain at the key locations of the earplate connection as input parameters. The methodology is developed with a numerical model under stochastic wind excitation and verified by numerical simulation and experiment. First, the relationships of cable force, strain on the connection, and crack length are explored. The wind-induced responses of guyed mast are analyzed under different wind speed and directions to form a database of cable forces. The responses of a typical connection with different crack lengths are simulated to investigate the strain sensitivity to crack propagation. Its results are used to form a second-stage strain database. The first stage of the fuzzy identification consists in identifying the interval of cable forces on the basis of cable force measurements. Then, a subjection degree function is formed between the measured strain and the developed strain database to judge the damage state of the connection by identifying the existence of a crack and estimating its length. The results of a numerical case study and further experiment are presented and show the potential and practicability of the technique to estimate the right range of crack length on earplates. Therefore, the proposed technique is practical and feasible to predict the damage state of earplate and can provide valuable prewarning information for structural safety.

Application of Fuzzy Optimization Model Based on Entropy Weight in Typical Flood Hydrograph Selection

In China, design flood computation is generally used by the design flood hydrograph (DFH) method by amplifying the typical flood hydrograph (TFH). TFH selection plays an important role in design flood calculation because the selection result directly affects the final DFH. The traditional method of TFH selection is subjective and varies with designers. So the fuzziness of TFH selection should be considered; the theory of fuzzy pattern recognition is applied in TFH selection to explore a theoretical method. This paper establishes a fuzzy optimization model based on entropy weight to select TFH. The influence indexes reflecting the quality of TFH selection are determined by Pearson correlation test and Spearman’s rho test. A shape parameter, which quantitatively described the shape of the flood hydrograph, is proposed for the first time. The entropy weight method is used to determine the weights of influence indexes and is a better way to avoid subjective influence. The optimal TFH is obtained by the principle of minimum eigenvalue of grades through fuzzy optimization model calculation. The model is applied to Yuecheng Reservoir in the Zhanghe Basin. Results show that No. 520723 flood process is the optimal TFH. Compared with the traditional TFH (1956 TFH), the TFH selected with this model is relatively better to design flood computation.

Vehicle Model Recognition Based on Fuzzy Pattern Recognition Method

The method based on the theory of Fuzzy Pattern Recognition is divided into three parts. Firstly, use Hough transformation to extract the feature points of vehicles, and use the ratio between two absolute distance of adjacent feature points as the characteristic values of vehicles; secondly, use Fuzzy C-mean Classification to handle feature data of 75 car model, then establish a degree of membership matrix as the sample space; thirdly, consider the classification algorithm based on fuzzy approach degree and the credibility of the vehicle feature to propose a weighted close- degree recognition algorithm. This recognition method has a good effect.

An Analysis of Rationality of Landuse Based on the Fuzzy Patern Recognition Theory

The study on land use is one of the important content in global change research, and is also of the hot site in the geographical study. Taking rationality of land use of YULIN as the research object, this paper focuses on the method to classify rationality in the view of fuzzy pattern recognition theory. During the analysis, firstly, authors establish classification indexes system by choosing eight factors (cultivated land, woodland, meadow, etc) as the evaluate indexes, and then classification grade criterion determined by classifying eight factors into six grades. Moreover, values of 5,4,3,2,1 are evaluated to the eight indexes of classification grade criterion respectively. In order to evaluate rationality of land use, functions that determine the rationality subjection of fuzzy grade are established on the basis of gray system theory in this paper. In order to insure the oneness of each index of classification indexes system, the authors have transformed original data, and the ratio of land use is got from original data of each county's land use of YULING. By using the function established in this paper, fuzzy grades matrix of each classification index are calculated, and rationality grade of land use of each county's in YULIN is made out by using fuzzy synthetical measurement method. According to the calculation and analyses, rationality grades is made out respectively as following: YULIN, SHENMU, SHUIDE belongs to the grade of 5; HENGSHAN belongs to the grade of 4; FUGU, MIZHI, JIAXIAN, WUBAO, QINGJIAN, ZIZHOU belongs to the grade of 3; JINBIA belongs to the grade of 2; DINGBIAN belongs to the grade of 1, which is close to the fact of land use of YULIN area. This study shows: fuzzy synthetical measurement is a scientific and practical method to be used to classify the grade of land use rationality, which takes on characters of complexity, random city, uncertainty and other non line features.

Multi-Axle Moving Train Loads Identification by Using Fuzzy Pattern Recognition Technique

Identification of multi-axle moving loads on bridge is very important for bridge design, construction, and maintenance in engineering field. It is complicated and time consuming to identify the multi-axle moving train loads with general identification methods and far away from practical practice. Based on the theory of fuzzy pattern recognition, the fuzzy pattern recognition method for multi-axle moving train loads identification on bridge is presented in this paper. The multi-axle moving loads pattern library on a simply supported bridge is established with numerical methods. Effect of measurement noise on the proposed method is investigated in three situations. The results show that the proposed identification method has a certain resistance to measurement noise and can realize moving train loads identification with high accuracy.

Multi-objectives fuzzy optimization model for ship form demonstration based on information entropy

Selecting optimization ship form scheme is an important content in the process of concept design of ship. Multi-objective fuzzy decision-making model for ship form demonstration is set up according to the fuzzy pattern-recognition theory. Weight coefficients of each target of ship form scheme are determined by information entropy and individual subjective partiality. This model is used to select the optimal ship form scheme, the example shows that the model is exact and the result is credible. It can provide a reference for choosing the optimization scheme of ship form.

A multi-dimensional fuzzy decision support strategy

Based on the theory of fuzzy pattern recognition and fuzzy optimization, this paper presents a kind of multi-dimensional fuzzy decision support strategy for multi-objective and multi-layer fuzzy decision support systems. Compared with conventional strategies of fuzzy optimization that just considers the relative membership u j of decision j of down layer unit systems, this strategy also takes the relative bad membership u j b=1− u j as the input of up layer unit systems. Besides, unlike conventional strategies of fuzzy optimization, this strategy can provide multi-dimensional information to the decision-maker. With these advantages, this strategy founds a new theoretical basis for the multi-dimensional decision making of large systems.

Applications of Fuzzy Pattern Recognition to Function Mapping and Controller Design

ABSTRACTA novel mathematical tool to construct fuzzy mapping from multiple inputs to single output, for a given finite set of training data, is introduced. In contrast to available heuristic partition methods, the algorithm uses fuzzy pattern recognition theory to provide a systematic approach for the selection of discrete fuzzy bases functions from which suitable, continuous fuzzy bases functions may be obtained. Applications of the proposed method to system identification and controller design are presented.

Entropy, and the recognition of fuzzy letters

The behavior of information in a process of recognition is of much theoretical interest. In order to shed some light on this behavior a case study is presented. The process chosen was that of the recognition of several computer printed characters on a noisy page. The design of the experiment was made such that the amount of missing information in the data was exactly calculable. The recognition process is iterative and is found to always terminate. Another result is that information is gained, on the average, during the process of recognition. This was true for all different characters that participated in the analysis. A connection with the theory of fuzzy pattern recognition is very clear and is discussed in detail. A correlation was found to exist between either the final average entropy per symbol, or the drop in entropy during the iterative process, and the percentage of successful detection of the same specific symbol. This fact further connects missing information with the process of learning and recognizing fuzzy data.