Fuzzy Reasoning Model Research Articles

A knowledge factor space model on multi-expert systems for oil-gas reservoir protection

In this paper, a new knowledge expression method, factor state space, is used in multi–expert system for oil–gas reservoir protection. Firstly, by analysing relationship between object and factor, factors expression pattern of knowledge based on factor state space is presented. Secondly, formal description of analysis factor neural is proposed and Mamdani fuzzy reasoning model based on analysis factor neuron is analysed to solve the reasoning problem of the expert system. For illustration, an example is utilised to verify the effectiveness of the method. The application of the expert system shows that factor state space theory is valid in knowledge expression and reasoning model. As a result, our proposed method based on factor state space can effectively improve the accuracy of inference.

A fuzzy expert system for automatic seismic signal classification

Automatic classification of seismic events is of great importance due to the large amount of data received continuously. Seismic analysts classify events by visual inspection and calculation of event signal characteristics. This process is subjective and demands hard work as well as a significant amount of time and considerable experience. A reliable automatic classification task considerably reduces the effort required and makes classification faster and more objective. The aim of this study is to develop a fuzzy rule based expert classification system that is able to imitate human reasoning and incorporate the analyst’s knowledge of seismic event classification. The fundamental idea behind using this approach was motivated by the way in which human analysts classify seismic events based on a set of experiential rules. Additionally, this approach was chosen due to its interpretability and adjustability, as well as its ability to manage the complexity of real data. Relevant discriminant features are extracted from event signal. Using these features, the classification system was built based on the vote by multiple rule fuzzy reasoning method with three types of rules. Comparison of this method with the single winner classical fuzzy reasoning model was carried out. Classification results on real seismic data showed the robustness of the classifier and its capability to operate in on-line classification.

Image Forgery Detection Based on Semantics

The development of powerful image editing software has made it easy to create visually convincing digital image forgeries. Recently some research works based on general low-level visual features in the field of digital image forensics have been conducted to address this problem. However, there has been little work by analyzing the high-level semantic content of the image. This paper discusses the forgery detection problem of digital image from the point of high-level semantic, and proposes the framework of image forgery detection based on semantics. The framework consists of three components including image recognition, semantic logic reasoning engine and generation of semantic rule. A new fuzzy logic semantic reasoning model for image forgery detection is proposed in this paper. Our reasoning model based on fuzzy logic can give the deterministic and uncertainty reasoning results. Based on the proposed framework of image forgery detection, the prototype system of image forgery detection based on semantics is completed using MATLAB, MySQL database software and ontology software protege and has the ability to detect abnormal splicing from the foreground and background, avoiding detection using the traditional methods based on lowlevel image features. Experimental results show that the framework and logic semantic reasoning model for image forgery detection are effective.

Interval type-2 fuzzy reasoning model of rehabilitation training for stroke

This paper deals with the problem of the uncertainty of stroke patient. Using the rehabilitant robot can achieve the rehabilitation training for stroke patients. However, the process of rehabilitation training has the uncertainty for patients' condition. So the interval type-2 fuzzy reasoning (IT2FR) model of rehabilitation training for stroke was proposed, which is used a novel type reduction method. Experimental results show that the model can make a rehabilitation training program effectively and improve rehabilitation efficiency.

Fault Section Estimation for Power Systems Based on Adaptive Fuzzy Petri Nets

AbstractDue to the advantages of Fuzzy reasoning Petri-nets(FPN)on uncertain and incomplete information processing. It is a promising technique to solve the complex power system fault-section estimation problem. Therefore, we propose a novel estimation method based on Adaptive Fuzzy Petri Nets (AFPN), in this algorithm, the AFPN is used to build a dynamic fault diagnosis fuzzy reasoning model, where the weights in fuzzy reasoning are decided by the incomplete and uncertain alarm information of protective relays and circuit breakers. The validity and feasibility of this method is illustrated by simulation examples. Results show that the fault section can be diagnosed correctly through fuzzy reasoning models for ten cases, and the AFPN not only takes the descriptive advantages of fuzzy Petri net, but also has learning ability as neural network..

Fuzzy Reasoning Model and Calculating Method for the Sellers' Credit of C2C Based on the Evaluating Indexes of Taobao Net

Aiming at the limitation to the credit evaluating system of C2C, a multi-variables fuzzy reasoning model is constructed and the calculating scheme is designed to evaluate the sellers credit of C2C referring to the evaluating indexes of Taobao net. The score of credit indexes is defined by the form of continuous interval in the model based on the interdependent relationship between the global assessment and the secondary evaluation indexes. Since the effect of the subjective factors on credit rating is efficiently eliminating in the algorithm and the credit description is more close to the transaction fact, the rating results of credit got by fuzzy calculation is more reliable than the ones obtained by traditional evaluation method. To verify the effectiveness of the fuzzy method, an applied example is given under the background of Taobao transaction.

Image Classification Based on Universal Combination Operation Model

The relationship of mutual information and generalized correlative coefficient is studied, a fuzzy reasoning model based on universal combination operation model is proposed. According to position relation with different parts of medical image and morph feature of the pathological region, a method is proposed based on feature extraction of tree structure considering the adjacent parts of tree structure guided medical prior knowledge. The experimental results show that the proposed algorithm can obtain a higher diagnostic accuracy.

A Fuzzy Reasoning Model for Action and Change in Timed Domains

This paper proposes a fuzzy approach for reasoning about action and change in timed domains. In our method, actions and world states are modeled as fuzzy sets over time axis. Thus, their temporal relations and time constraints can be modeled as fuzzy rules. So, our method handles well the issue that action happens at an approximate time and then the states also change at an approximate time, which has not been solved well in the existing work. Finally, our method is used to solve the classic problem of rail-road crossing control in a fuzzy environment. The theoretical and simulation analysis shows that the controller using our method works well.

Irrationality Re-Examined: A Few Comments on the Conjunction Fallacy

In this paper, I argue that the probability model used to infer irrationality for the subjects in the famous Linda problem is not appropriate, and I suggest different approaches based on fuzzy reasoning models. My line of argument is two-fold: 1) If the term “probability” is understood properly (mathematically), then the experimenters used the wrong model. 2) If the term “probability” is understood casually (non- mathematically), then alternative models perhaps should be used to justify the subjects’ responses. The objective is to experiment with new ways of looking at irrationality and raise a discussion regarding the relation between irrationality, reasoning errors and logical models that are used as frameworks to study irrationality.

NON-INVASIVE NOCTURNAL HYPOGLYCEMIA DETECTION FOR INSULIN-DEPENDENT DIABETES MELLITUS USING GENETIC FUZZY LOGIC METHOD

Hypoglycemia, or low blood glucose, is the most common complication experienced by Type 1 diabetes mellitus (T1DM) patients. It is dangerous and can result in unconsciousness, seizures and even death. The most common physiological parameter to be effected from hypoglycemic reaction are heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal. Based on physiological parameters, a genetic algorithm based fuzzy reasoning model is developed to recognize the presence of hypoglycemia. To optimize the parameters of the fuzzy model in the membership functions and fuzzy rules, a genetic algorithm is used. A validation strategy based adjustable fitness is introduced in order to prevent the phenomenon of overtraining (overfitting). For this study, 15 children with 569 sampling data points with Type 1 diabetes volunteered for an overnight study. The effectiveness of the proposed algorithm is found to be satisfactory by giving better sensitivity and specificity compared with other existing methods for hypoglycemia detection.

Fuzzy reasoning model based on universal combination operation model

The universal combination operation model is a comprehensive decision model of continuous-valued logic. It overcomes limitations of the scope of operations of reasoning operators in the current comprehensive decision-making system. This article discusses relationship of mutual information and general correlation coefficient and gives the corresponding rules of them, the optimal matching operator is selected to complete fuzzy decision according to mutual information between candidate attributes. The relationship of mutual information between attributes and generalized correlative coefficient provides the principle to select the matching operator. According to the results of experiment, it is more reasonable to enhance classified precision effectively. There is a certain application value of the article's method.

A Fuzzy Case-Based Reasoning Model to Forecast Prices of Aquatic Products

Accurate prediction of aquatic product prices can improve the quality of business strategy of aquatic product market. Case-based reasoning (CBR) systems have long been intensively used in several areas of artificial intelligence. But it is difficult to cluster similar cases from case bases as there are uncertainties in knowledge representation, attribute description and similarity measures in CBR. To increase the efficiency and reliability of CBR, fuzzy theories have been combined with CBR. In this paper, fuzzy case-based reasoning (FCBR) has been developed to forecast the price of aquatic products.

Natural occurrence of nocturnal hypoglycemia detection using hybrid particle swarm optimized fuzzy reasoning model

Low blood glucose (hypoglycemia) is a common and serious side effect of insulin therapy in patients with diabetes. This paper will make a contribution to knowledge in the modeling and design of a non-invasive hypoglycemia monitor for patients with type 1 diabetes mellitus (T1DM) using a fuzzy-reasoning system. Based on the heart rate and the corrected QT interval of the electrocardiogram (ECG) signal, we have developed a hybrid particle-swarm-optimization-based fuzzy-reasoning model to recognize the presence of hypoglycemic episodes. To optimize the fuzzy rules and the fuzzy-membership functions, a hybrid particle-swarm-optimization with wavelet mutation operation is investigated. From our clinical study of 16 children with T1DM, natural occurrence of nocturnal-hypoglycemic episodes was associated with increased heart rates and increased corrected QT intervals. All the data sets were collected from the Government of Western Australia's Department of Health. All data were organized randomly into a training set (8 patients with 320 data points) and a testing set (another 8 patients with 269 data points). To prevent the phenomenon of overtraining, we separated the training set into 2 sets (4 patients in each set) and a fitness function was introduced for this training process. The testing performances of the proposed algorithm for detection of advanced hypoglycemic episodes (sensitivity=85.71% and specificity=79.84%) and hypoglycemic episodes (sensitivity=80.00% and specificity=55.14%) were given. We have investigated the detection for the natural occurrence of nocturnal hypoglycemic episodes in T1DM using a hybrid particle-swarm-optimization-based fuzzy-reasoning model with physiological parameters. In this study, no restricted environment (e.g. patient's dietary requirements) is required. Furthermore, the sampling time is between 5 and 10 min. To conclude, we have shown that the testing performances of the proposed algorithm for detection of advanced hypoglycemic and hypoglycemic episodes for T1DM patients are satisfactory.

Fuzzy lattice classifier and its application to bearing fault diagnosis

In this work, we present a novel classification scheme named fuzzy lattice classifier (FLC) based on the lattice framework and apply it to the bearing faults diagnosis problem. Different from the fuzzy lattice reasoning (FLR) model developed in literature, there is no need to tune any parameter and to compute the inclusion measure in the training procedure in our new FLC model. It can converge rapidly in a single pass through training patterns with a few induced rules. A series of experiments are conducted on five popular benchmark datasets and three bearing datasets to evaluate and compare the presented FLC with the FLR model as well as some other widely used classification methods. Experimental results indicate that the FLC yields a satisfactory classification performance with higher computation efficiency than other classifiers. It is very desirable to utilize the FLC scheme for on-line condition monitoring of bearings and other mechanical systems.

English

In this study, a model called “fuzzy reasoning model” was proposed for the case when the explanatory variables were crisp and the value of the binary response variable was reported as a number between zero and one. In this regard, the concept of possibilistic odds is also introduced. Then, the methodology and formulation of this model was used to explain the model parameters. Some goodness of fit criteria were proposed and a numerical example was given as an example.   Key words: Fuzzy number, defuzzification, centroid, clinical research.

Safety Following Space Modeling and Speed Control on Highway

Due to the rapid development of economics and society, China has witnessed increasing highway accidents, especially rear end collisions that mainly caused by over-speeding or too close following distance. Based on the vehicle dynamic analysis, therefore, a 3 step-based safety following space identification model according to Mazda vehicle is proposed to describe the brake performance and three driving states are taken into considering, including total following state, part following state, and three-kind extreme states. Then, a self-adaptive fuzzy reasoning model is used to control the traveling speed so as to adjust the traffic flow as smoothly as possible. The Matlab simulation results shows this proposed fuzzy control model helps minimize the occurrence probability of rear-end collision on highway and improve the overall traffic safety performance and management level.

End Effect Processing for Empirical Mode Decomposition Using Fuzzy Inductive Reasoning

This paper focuses on the end effect problem of the empirical mode decomposition (EMD) algorithm, which results in a serious distortion in the EMD sifting process. A new method based on fuzzy inductive reasoning (FIR) is proposed to overcome the end effect. Fuzzy inductive reasoning method has simple inferring rules and strong predictive capability. The fuzzy inductive reasoning based method uses the sequence near the end as the input signal of fuzzy inductive reasoning model. This predictive value can be obtained after fuzzification, qualitative modeling ,qualitative simulation and debluring. The simulation results have shown that the fuzzy inductive reasoning based method has equivalent performance to the neural network based method.

The Prediction Of Air Pollution By Using Neuro-fuzzy Gmdh

Air pollution is one of today modern life phenomena. It is resulted to create round-the-clock human begin activities. Control of environment pollution is complicated scientific process that enters policy, economy, technology and sociology. GMDH (Group Method of Data Handling) has been used for the identification of a mathematical model that has many input variables but limited data needs by using a hierarchical structure. This paper proposes a Neuro-fuzzy GMDH model, adopting Gaussian radial basis functions (GRBF) as partial descriptions of GMDH. GRBF is reinterpreted as both a simplified fuzzy reasoning model and as a three-layered neural network. In this paper, is used Neuro-fuzzy GMDH algorithm for predicting air pollution data and then were compared the results of predicting air pollution data by using Neuro-fuzzy GMDH and Multi Layer Perceptron (MLP).

퍼지 및 신경망이론을 이용한 도시부 신호교차로 교통사고예측모형 개발

본 연구는 신호교차로 교통사고예측모형 구축 과정 중 일반적으로 제한된 변수의 선정 및 모형의 구축에만 주로 초점이 맞추어진 기존 방법론의 문제점을 개선하고, 자료조사 및 수집 과정에서 발생하는 자료의 불확실한 상태를 인정하면서 자료의 불확실성을 최소화하여 이용할 수 있는 방법론을 개발하는데 연구의 주안점을 두었다. 퍼지추론이론과 신경망이론을 이용한 모형을 구축하였고, 마지막으로 구축된 퍼지추론이론 모형 및 신경망이론 모형과 기존 회귀모형인 포아송 회귀모형간의 통계적인 검증과 실제 Data를 이용한 모형의 적정성을 검토하였다. 모형의 통계적인 검증시 기존모형에 비해 퍼지추론모형과 신경망이론모형이 더 설명력이 높은 것으로 나타났고, 검증에서도 퍼지추론이론과 신경망이론이 적절한 것으로 나타났으며 기존모형보다 사고건수를 예측하는 설명력이 높은 것으로 입증되었다. 본 연구에서 개발된 모형은 계획 및 운영단계에서 신호교차로의 안전성을 측정하는데 활용될 수 있으며, 궁극적으로는 신호교차로에서 교통사고를 줄이는데 기여할 수 있을 것으로 판단된다. This study is to suggest a methodology to overcome the uncertainty and lack of reliability of data. The fuzzy reasoning model and the neural network model were developed in order to overcome the potential lack of reliability which may occur during the process of data collection. According to the result of comparison with the Poisson regression model, the suggested models showed better performance in the accuracy of the accident frequency prediction. It means that the more accurate accident frequency prediction model can be developed by the process of the uncertainty of raw data and the adjustment of errors in data by learning. Among the suggested models, the performance of the neural network model was better than that of the fuzzy reasoning model. The suggested models can evaluate the safety of signalized intersections in operation and/or planning, and ultimately contribute the reduction of accidents.

Research on Four Type-2 Fuzzy Reasoning Models

The previous discussion about type-2 fuzzy reasoning mechanisms just paid attentions to Mamdani models based on Mamdani implication operator. There is no doubt that the operator is justifiable, since such an operator possesses characteristic clipping effect, i.e. limiting the consequence membership function with the truth value of the antecedent. However, Mamdani implication operator does not uphold classical logic, i.e. it is in no agreement with the classic binary interpretation of implication. All of the Kleene-Dienes operator, Lukasiewicz operator, Zadeh operator and Reichenbach operator, which derive from equivalencies of the classic binary implication, have in common the feature of generalizing classic logic. This paper investigates type-2 fuzzy reasoning models and algorithms based on these four implication operators, respectively. In the view upholding classical logic, proposed models and algorithms seem to be more natural than of the Mamdani type.