Fuzzy pattern recognition model of geological sweetspot for coalbed methane development

From the perspective of geological zone selection for coalbed methane(CBM) development, the evaluation parameters(covering geological conditions and production conditions) of geological sweetspot for CBM development are determined, and the evaluation index system of geological sweetspot for CBM development is established. On this basis, the fuzzy pattern recognition(FPR) model of geological sweetspot for CBM development is built. The model is applied to evaluate four units of No.3 Coal Seam in the Fanzhuang Block, southern Qinshui Basin, China. The evaluation results are consistent with the actual development effect and the existing research results, which verifies the rationality and reliability of the FPR model. The research shows that the proposed FPR model of geological sweetspot for CBM development does not involve parameter weighting which leads to uncertainties in the results of the conventional models such as analytic hierarchy process and multi-level fuzzy synthesis judgment, and features a simple computation without the construction of multi-level judgment matrix. The FPR model provides reliable results to support the efficient development of CBM.

Group Decision Making Based Fuzzy Pattern Recognition Model for Lectotype Optimization of Offshore Platforms 1

This paper develops a fuzzy pattern recognition model for group decision making to solve the problem of lectotype optimization of offshore platforms. The lack of data and the inexact or incomplete information for criteria are the main cause of uncertainty in the evaluation process, therefore it is necessary to integrate the judgments from different decision makers with different experience, knowledge and preference. This paper first uses a complementary principle based pairwise comparison method to obtain the subjective weight of the criteria from each decision maker. A fuzzy pattern recognition model is then developed to integrate the judgments from all the decision makers and the information from the criteria, under the supervision of the subjective weights. Finally a case study is given to show the efficiency and robustness of the proposed model.

Fuzzy Jamming Pattern Recognition Based on Statistic Parameters of Signal’s PSD

In order to recognize the jamming pattern in anti-jamming, a novel fuzzy jamming recognition method based on statistic parameters of received signal’s power spectral density (PSD) is proposed. It exploits PSD’s shape factor and skewness of received signal as classified characters of jamming pattern. After the mean center and variance of each jamming pattern are calculated by using some jamming samples, an exponential fuzzy membership function is used to calculate the membership value of the recognized sample. Finally, the jamming pattern of received signal is recognized by the maximum membership principle. The simulation results show that the proposed algorithm can recognize common eight jamming patterns accurately.

Investigation and Application of Automatic Fingerprint Identification Based on Fuzzy Pattern Recognition

Fingerprint image is a typical non-restraint image that has some uncertainty, which makes it difficult to perform identification using classical approach. Therefore, fuzzy pattern recognition is applied to match individual query by searching the entire template database. The fuzzy maximum subordinate principle is used to solve shift matching. Through experimenting and analyzing, the approximate principle fuzzy method is employed by selecting fuzzy characteristics and determining the similarity function to achieve the further accuracy. Theoretical and experimental results show this approach is effective and reasonable.

QUALITY ASSESSMENT OF GINSENG BY CHEMICAL FUZZY PATTERN RECOGNITION

A new method of assessing ginseng and screening its bioactive constituents by fuzzy pattern recognition has been proposed.

Water quality assessment for Ulansuhai Lake using fuzzy clustering and pattern recognition

Water quality assessment of lakes is important to determine functional zones of water use.Considering the fuzziness during the partitioning process for lake water quality in an arid area,a multiplex model of fuzzy clustering with pattern recognition was developed by integrating transitive closure method,ISODATA algorithm in fuzzy clustering and fuzzy pattern recognition.The model was applied to partition the Ulansuhai Lake,a typical shallow lake in arid climate zone in the west part of Inner Mongolia,China and grade the condition of water quality divisions.The results showed that the partition well matched the real conditions of the lake,and the method has been proved accurate in the application.

A Fuzzy Neural Network for Fault Pattern Recognition

This paper combines fuzzy set theory with ART neural net-work , and demonstrates some important properties of the fuzzy ART neural net-work algorithm. The results from application on a ball bearing diagnosis indicate that a fuzzy ART neural net-work has an effect of fast stable recognition for fuzzy patterns.

Fuzzy Condition Pattern Recognition and Fault Diagnosis for Spacecraft

This paper introduces concepts of symptom vector and fuzzy symptom vector forspacecraft condition recognition and fault diagnosis,defines an operator and suggests a fuzzy pat-tern recognition method of fault diagnosis for spacecraft.This method is verified by examples andresults are checked from an expert system.

Air Target Fuzzy Pattern Recognition Threat-Judgment Model

Threat-judgment is a complicated fuzzy inference problem. Up to now no relevant unified theory and measur-ing standard have been developed. It is very difficult to establish a threat-judgment model with high reliability in the airdefense system for the naval warships. Air target threat level judgment is an important component in naval warship com-bat command decision-making systems. According to the threat level judgment of air targets during the air defense of sin-gle naval warship, a fuzzy pattern recognition model for judging the threat from air targets is established. Then an algo-rithm for identifying the parameters in the model is presented. The model has an adaptive feature and can dynamicallyupdate its parameters according to the state change of the attacking targets and the environment. The method presentedhere can be used for the air defense system threat judgment in the naval warships.

Fuzzy Pattern Recognition in Atlas and Images of the Unevenness of Carbide in Tool Steel

Fuzzy pattern recognition has been employed to identify some atlas and images of the unevenness of carbide in tool steel. Three models have been constructed. These models were based on fuzzy mathematics theory, as well as fuzzy pattern recognition method. Distribution rule of the unevenness of eutectic carbide in ledeburite steel is proposed in these models respectively.

Body Fuzzy Pattern Recognition in Woman Basic Block Design

Basic block is the foundation of clothing construction design because it is the media between body and clothes and the fitness of clothes should be based on the accuracy of basic block. That needs us to recognize body not to record it. This paper reports the Algorithm of woman body fuzzy pattern recognition. It is organized in three sections:(i) extracting woman body feature; (ii) establishing membership functions of feature indexes;(iii) presenting an Algorithm for woman body fuzzy pattern recognition by example.

Fabric Wrinkle Grade Assessment Based on Fuzzy Pattern Recognition

The basic principle of fuzzy pattern recognition is brief introduced firstly in this paper, which mainly includes fuzzy rules and fuzzy inference system. Then, the algorithm procedure of fuzzy pattern recognition is proposed. Finally, the application of Mamdani fuzzy model is introduced to evaluate fabric wrinkle grade in detail, and used the correlation coefficient between subject and object evaluation to verify the reliability of fuzzy pattern recognition. It shows the method of fuzzy pattern recognition needs not a large number of testing data and the accuracy of evaluation is up to 97.38%.

Fuzzy Pattern Recognition System for Detection of Alga Distribution

To realize the on-line measurement and make analysis on the density of algae and their cluster distribution, the fluorescent detection and fuzzy pattern recognition techniques are used. The principle of fluorescent fiber-optic detection is given as well as the method of fuzzy feature extraction using a class of neural network.

Fault Pattern Recognition based on Kernel Method and Fuzzy C-means

A method about fault identification is proposed to solve the relationship among fault features of large rotating machinery, which is extremely complicated and nonlinear. This paper studies the rotor test-rig and the clustering of data sets and fault pattern recognitions. The present method firstly maps the data from their original space to a high dimensional Kernel space which makes the highly nonlinear data in low-dimensional space become linearly separable in Kernel space. It highlights the differences among the features of the data set. Then fuzzy C-means （FCM） is conducted in the Kernel space. Each data is assigned to the nearest class by computing the distance to the clustering center. Finally, test set is used to judge the results. The convergence rate and clustering accuracy are better than traditional FCM. The study shows that the method is effective for the accuracy of pattern recognition on rotating machinery.

Assessment of Structural Damage Condition Based on Fuzzy Pattern Recognition

This paper presents a new method of damage condition assessment that allows accommodating other types of uncertainties due to ambiguity, vagueness, and fuzziness that are statistically nondescribable. In this method, healthy observations are used to construct a fury set representing sound performance characteristics. Additionally, the bounds on the similarities among the structural damage states are prescribed by using the state similarity matrix. Thus, an optimal group fuzzy sets representing damage states such as little, moderate, and severe damage can be inferred as an inverse problem from healthy observations only. The optimal group of damage fuzzy sets is used to classify a set of observations at any unknown state of damage using the principles of fitzzy pattern recognition based on an approximate principle . This method can be embedded into the system of Structural Health Monitoring （SHM） to give advice about structural maintenance and life predictio comes from Reference [ 9 ] for damage pattern recognition is presented n. Finally, a case and discussed. The study, which compared result illustrates our method is more effective and general, so it is very practical in engineering.

A Pattern Classification Model for Vowel Data Using Fuzzy Nearest Neighbor

Classification of the patterns is a crucial structure of research and applications. Using fuzzy set theory, classifying the patterns has become of great interest because of its ability to understand the parameters. One of the problemsobserved in the fuzzification of an unknown pattern is that importance is givenonly to the known patterns but not to their features. In contrast, features of thepatterns play an essential role when their respective patterns overlap. In this paper,an optimal fuzzy nearest neighbor model has been introduced in which a fuzzifi-cation process has been carried out for the unknown pattern using k nearest neighbor. With the help of the fuzzification process, the membership matrix has beenformed. In this membership matrix, fuzzification has been carried out of the features of the unknown pattern. Classification results are verified on a completelyllabelled Telugu vowel data set, and the accuracy is compared with the differentmodels and the fuzzy k nearest neighbor algorithm. The proposed model gives84.86% accuracy on 50% training data set and 89.35% accuracy on 80% trainingdata set. The proposed classifier learns well enough with a small amount of training data, resulting in an efficient and faster approach.

Fuzzy Recognition Method for Fish Ontology Retrieving

This paper presents a new method based on ontology formation and fuzzy recognition of digital pictures.Ontology creation and document indexing are well-known bottlenecks for integrating semantic services and for the Semantic Web,and thus the new method will be able to make automatic creation of the fish geometric ontology and automatic indexing to existing Semantic Web.Fuzzy set and fuzzy recognition are used to decide wheter a new fish picture belongs to an existing training set,here with the carp as an example.Training samples are used to set up fuzzy set and membership functions.The existing way of fish ontology formation can be integrated with the new method and the existing work for fish web can be used.

A novel approach in ECG beat recognition using adaptive neural fuzzy filter

Accurate and computationally efficient means of electrocardiography (ECG) arrhythmia detec-tion has been the subject of considerable re-search efforts in recent years. Intelligent com-puting tools such as artificial neural network (ANN) and fuzzy logic approaches are demon-strated to be competent when applied individu-ally to a variety of problems. Recently, there has been a growing interest in combining both of these approaches, and as a result, adaptive neural fuzzy filters (ANFF) [1] have been evolved. This study presents a comparative study of the classification accuracy of ECG signals using (MLP) with back propagation training algorithm, and a new adaptive neural fuzzy filter architec-ture (ANFF) for early diagnosis of ECG ar-rhythmia. ANFF is inherently a feed forward multilayered connectionist network which can learn by itself according to numerical training data or expert knowledge represented by fuzzy if-then rules [1]. In this paper we used an adap-tive neural fuzzy filter as an ECG beat classifier. We combined 3 famous wavelet transforms and used them mid 4 the order AR model coefficient as features. Our results suggest that a new proposed classifier (ANFF) with these features can generalize better than ordinary MLP archi-tecture and also learn better and faster. The results of proposed method show high accu-racy in ECG beat classification (97.6%) with 100% specificity and high sensitivity.