Fuzzy Process Model Research Articles

The construction of a new theoretical system of university education development based on the principle of multi-optimization of fuzzy ICDS

Abstract The purpose of exploring the new theoretical system of college education development is to improve college students’ quality in all aspects. This paper constructs a new theoretical system of college education development based on the new theory of college education development--”three comprehensive education” theory. The fuzzy credibility measure and credibility distribution are used to explain the fuzzy processing theory, and the uncertainty index of the fuzzy processing model is optimized based on the cross-rhombus search algorithm to improve the search speed of the fuzzy processing model. Data experimental analysis is conducted for the fuzzy ICDS multi-optimization principle. Regarding search speed, the average search speed improvement of the algorithm in this paper relative to the CD algorithm saves about 1.37 to 4.69 points per block. It improves classification accuracy by 12.02% and 7.65% compared to the best fuzzy and GFCM algorithms, respectively. This shows that using the fuzzy ICDS multi-optimization principle can help analyze the effectiveness of the “three holistic education” theory in constructing the education development system and provide a reference for planning and implementing the new education development system.

Supply chain operation evaluation and management decision by fuzzy cognitive map model

AbstractThe components of the logistics and supply chain are analysed to meet the needs of supply chain management and decision support, aiming at the complexity and uncertainty of the supply chain system. First, the characteristics of logistics demand and logistics service types of some enterprises are analysed. The factors affecting supplier selection are obtained through the questionnaire survey of some enterprises. Second, the mutual feedback relationship between the influencing factors of logistics service demand is analysed, and a fuzzy cognitive graph model of the selection process of enterprise logistics service providers is established. Finally, the fuzzy cognitive map (FCM) model is used to conduct an empirical study on the choice of logistics service providers. The results show that the ranking results of the three candidate logistics service companies based on FCM are WL1 (0.7233) > WL3 (0.6823) > WL2 (0.6764). When each node of the concept does not affect each other, the ranking result of the three candidate logistics service companies based on analytic hierarchy process (AHP) is WL1 (0.7265) > WL2 (0.6949) > WL3 (0.6920). The final evaluation result will be affected by the mutual influence relationship between the concept nodes. Overall, the lowest evaluation score is WL2, and the highest is WL1. The most suitable logistics service provider for the enterprise is WL1. The FCM model selected by the manufacturing enterprise logistics service provider is reasonable and feasible. The mutual feedback between evaluation factors affects the selection of logistics service providers in a company. The FCM can get a more accurate evaluation weight value and can realize the optimal choice of logistics suppliers. It provides reference and decision support for other enterprises.

Evaluating Pallet Investment Strategy Using Fuzzy Analytic Network Process: A Case in Chinese Chain Supermarkets

Presently in Chinese chain supermarkets, many enterprises have built automatic equipment and information facilities in the logistics center of their supply chain systems. Modern logistics technology and equipment largely depend on the resource integration of each role in the chain (such as suppliers, manufacturers, wholesalers, and retailers), especially logistics facilities and equipment resources, to realize the circulation of products. The pallet, which is an indispensable basic tool for a supply chain system in the process of product circulation, is most often used in the handling, stacking, storage, and transportation of products. The process of building automation and informationization in the supply chain system of Chinese supermarket chains requires the solving of the problems of cost and sharing pallets in logistics operations. Large-scale enterprises often spend millions of dollars on investment, the failure of which can cause significant harm to the enterprise. Therefore, the authors of this paper adopted the fuzzy analytic network process (FANP), combining fuzzy and ANP models to evaluate our studied case. We utilized an actual case as the research object to resolve the important decisions regarding pallet resource sharing investment in the supply chain system. Importantly, it is expected that the proposed method can provide an important reference standard or a new idea for decision makers in the chain supermarket industry or related industries.

Causal Discovery of Flight Service Process Based on Event Sequence

The development of the civil aviation industry has continuously increased the requirements for the efficiency of airport ground support services. In the existing ground support research, there has not yet been a process model that directly obtains support from the ground support log to study the causal relationship between service nodes and flight delays. Most ground support studies mainly use machine learning methods to predict flight delays, and the flight support model they are based on is an ideal model. The study did not conduct an in-depth study of the causal mechanism behind the ground support link and did not reveal the true cause of flight delays. Therefore, there is a certain deviation in the prediction of flight delays by machine learning, and there is a certain deviation between the ideal model based on the research and the actual service process. Therefore, it is of practical significance to obtain the process model from the guarantee log and analyze its causality. However, the existing process causal factor discovery methods only do certain research when the assumption of causal sufficiency is established and does not consider the existence of latent variables. Therefore, this article proposes a framework to realize the discovery of process causal factors without assuming causal sufficiency. The optimized fuzzy mining process model is used as the service benchmark model, and the local causal discovery algorithm is used to discover the causal factors. Under this framework, this paper proposes a new Markov blanket discovery algorithm that does not assume causal sufficiency to discover causal factors and uses benchmark data sets for testing. Finally, the actual flight service data are used for causal discovery among flight service nodes. The local causal discovery algorithm proposed in this paper has a certain competitive advantage in accuracy, F1, and other aspects of the existing causal discovery algorithm. It avoids the occurrence of its dimensional disaster. Through the in-depth analysis of the flight safety reason node discovered by this method, it is found that the unreasonable scheduling of flight support personnel is an important reason for frequent flight delays at the airport.

Active vibration control of thin-walled milling based on ANFIS parameter optimization

In view of the time-varying dynamic characteristics of thin-walled milling, an optimization method of thin-walled milling control parameters based on the ANFIS system is proposed. Firstly, the control system strategy design and equipment selection construction were carried out, and the fuzzy inference model was built based on matlab platform; Subsequently, the fuzzy inference model of the thin-walled workpiece milling process was obtained by collecting fuzzy inference training data set of and the ANFIS system training. The proportional control coefficient of the controller was optimized based on the input and output relation of the model. Finally, the dynamic response and surface roughness of the controlled thin-walled panel milling process before and after optimization were obtained through cutting experiments, and the effectiveness of the ANFIS method to optimize the control parameters and the reliability of the fuzzy reasoning model were verified through analysis of the reasoning results. The results show that the ANFIS system can predict the vibration response performance of the target by setting the input and output reasonably, and optimize the related variable parameters according to the predicted results, so as to make the thin-wall milling system more stable.

A trust-based fuzzy neural network for smart data fusion in internet of things

Internet of Things (IoT) devices generates a vast amount of data from extensive applications. Maintaining the sensed data with low energy consumption, delay time, and adaptive coverage fraction rate proportionally influences the storage capacity. To maintain a trade-off between above-listed factors, we proposed an Elfes Sugeno Fuzzy and Trust-based Neural Networks (ESF-TNN) approach enables 3-algorithms. First, Elfes Probability Sensing (EPS) Model addresses the coverage fraction of each IoT sensor. Second, Sugeno Fuzzy Processing model regulates the energy consumption by proportionately distributing data to nodes without the defuzzification process. Third, Trust-based Neural Data Storage algorithm enriches an adequate data storage capacity by considering the average classification ratio while processing regenerated data packets to pertain each interaction information via Trust Mechanism. Simulation results show that our proposed method effectively covers the monitored area with 15 Joules of energy consumption and 1-ms delay time along with sufficient storage capacity.

Fuzzy modeling of multirate sampled nonlinear systems based on multi-model method

Based on the multi-model principle, the fuzzy identification for nonlinear systems with multirate sampled data is studied. Firstly, the nonlinear system with multirate sampled data can be shown as the nonlinear weighted combination of some linear models at multiple local working points. On this basis, the fuzzy model of the multirate sampled nonlinear system is built. The premise structure of the fuzzy model is confirmed by using fuzzy competitive learning, and the conclusion parameters of the fuzzy model are estimated by the random gradient descent algorithm. The convergence of the proposed identification algorithm is given by using the martingale theorem and lemmas. The fuzzy model of the PH neutralization process of acid-base titration for hair quality detection is constructed to demonstrate the effectiveness of the proposed method.

A Fuzzy Optimal Control Inventory Model of Product–Process Innovation and Fuzzy Learning Effect in Finite Time Horizon

In this study, we have introduced a fuzzy dynamic optimal control model of process–product innovation with learning by doing. The firm’s cost functions of product and process innovation depend on investment in both the innovations and the gathering of knowledge of product and process innovation. In this model, the product price, and the investments of product and process innovation are control variables; the product quality, production cost, and the change rates of gathering knowledge accumulations of product and process innovation are state variables. To represent the model more realistically, we considered all the variables are fuzzy in nature. The main goal of this report is to probe the relationships between these variables and investigate the optimality criteria for the model. The model is formulated as a single objective profit maximization problem in the single period finite time horizon and solved numerically using Runge–Kutta forward–backward method of fourth order using MATLAB software. Further, some numerical experiments are performed and the graphical representation of the results are also depicted to illustrate the model. Also a sensitivity analysis is conducted to study the issue of varying the parameters and coefficients on the target function value.

A Safety Computer System Based on Multi-Sensor Data Processing.

The safety computer in the train control system is designed to be the double two-vote-two architecture. If safety-critical multi-input data are inconsistent, this may cause non-strict multi-sensor data problems in the output. These kinds of problems may directly affect the decision making of the safety computer and even pose a serious threat to the safe operation of the train. In this paper, non-strict multi-sensor data problems that exist in traditional safety computers are analyzed. The input data are classified based on data features and safety computer features. Then, the input data that cause non-strict multi-sensor data problems are modeled. Fuzzy theory is used in the safety computer to process multi-sensor data and to avoid the non-strict multi-sensor problems. The fuzzy processing model is added into the onboard double two-vote-two architecture safety computer platform. The fuzzy processing model can be divided into two parts: improved fuzzy decision tree and improved fuzzy weighted fusion. Finally, the model is verified based on two kinds of data. Verification results indicate that the fuzzy processing model can effectively reduce the non-strict identical problems and improve the system efficiency on the premise of ensuring the data reliability.

Fuzzy AHP model for challenges to thermal power plant establishment in India

Thermal power plant is a huge project in context of resource utilisation, time required for its completion and also large amount of funds are required for its establishment. So, delay in any activity especially activity of critical path would results in delay of whole project. Due to delay, the stress on the resources would also increase, which may further result in substandard quality of work. A fuzzy AHP process model has been used to prioritise the challenges to thermal power plant establishment through the inputs by experts from managers at the actual construction sites, designers, and consultants. The challenges identified in the present study can be categorised in three categories viz. high impact, medium impact and least impact challenges. This paper presented the ranking of 19 challenges faced in the establishment of a new thermal power plant in India.

Risk assessment of karst collapse using an integrated fuzzy analytic hierarchy process and grey relational analysis model

A karst collapse, as a natural hazard, is totally different to a normal collapse. In recent years, karst collapses have caused substantial economic losses and even threatened human safety. A risk assessment model for karst collapse was developed based on the fuzzy analytic hierarchy process (FAHP) and grey relational analysis (GRA), which is a simple and effective mathematical algorithm. An evaluation index played an important role in the process of completing the risk assessment model. In this study, the proposed model was applied to Jiaobai village in southwest China. First, the main controlling factors were summarized as an evaluation index of the model based on an investigation and statistical analysis of the natural formation law of karst collapse. Second, the FAHP was used to determine the relative weights and GRA was used to calculate the grey relational coefficient among the indices. Finally, the relational sequence of evaluation objects was established by calculating the grey weighted relational degree. According to the maximum relational rule, the greater the relational degree the better the relational degree with the hierarchy set. The results showed that the model accurately simulated the field condition. It is also demonstrated the contribution of various control factors to the process of karst collapse and the degree of collapse in the study area.

A Fault Detection Approach for Nonlinear Systems Based on Data-Driven Realizations of Fuzzy Kernel Representations

This paper is devoted to the data-driven fault detection of nonlinear systems. For our purpose, the definition of Takagi–Sugeno fuzzy data-driven forms of kernel representations for nonlinear systems is introduced first, which builds the basis of our work. The major contributions consist of two parts. In the first part, a data-driven method for fuzzy process modeling is proposed, and associated with it, some modeling issues are addressed with the aid of the so-called randomized algorithm technique in the probabilistic framework. It is followed by a data-driven realization of fuzzy kernel representation and its implementation in the fault detection system design. To link the data-driven methods to the well-established observer-based fault detection approaches, the recursive form of the fuzzy kernel representation is proposed. In the second part, the fuzzy-observer-based fault detection design scheme is investigated based on the recursive fuzzy kernel representation. The main results of our study are illustrated by an experimental study on the laboratory setup of a three-tank system.

Flood risk assessment model using the fuzzy analytic hierarchy process

Sustainable development and natural disasters are closely interlinked. The impact of catastrophic events on the environment is still very difficult to determine, and such losses are generally underestimated. Development is never neutral in relation to catastrophes: it creates, enhances or reduces the risk of their occurrence. Selection of appropriate methods and mathematical models for risk assessment in relation to the specific features and characteristics of the considered system and available information and resources, is a key parameter of reliability assessment. Numerous authors applied AHP methods with flood risk assessment, but very limited literature is avaliable on the use of fuzzy multiobjective analysis in flood studies. In the recent years, the fuzzy approach for flood risk assessments has gained greater importance. In this paper, we present the fuzzy analytic hierarchy process (FAHP) model for flood risk assessments. Two flood hazard indexes were defined, one based on natural factors and one based on anthropogenic factors. FAHP is applied to data sets to illustrate a model.

Applying 2-Tuple Linguistic Representation and a Mamdani Fuzzy Inference System to Fuzzy Time Series

This study applies 2-tuple linguistic representation and a Mamdani fuzzy inference system to fuzzy time series. Mamdani [12] proposed a max-min inference method and applied it to fuzzy logical control problem. The Mamdani fuzzy inference system is a useful tool in fuzzy modeling process, but it is rarely applied to fuzzy time series problems. By integrating 2-tuple linguistic representation, which was proposed by Herra and Martínez [16], the connection between fuzzy modeling inference and fuzzy time series can be established. With the 2-tuple concept, historical data can be fuzzified to a fuzzy set with triangular membership and the loss of information is reduced. The fuzzy if-then rule can be established by two consecutive data and a Mamdani fuzzy inference system can be applied in the inference process. Two examples are investigated. First, the proposed method is applied to forecast enrollments at the University of Alabama. Compared with six different methods, the average MAPE value shown by the proposed method is relatively small. Next, the proposed method is used to forecast the number of Taiwanese tourists traveling to Malaysia. A set of data from 1993 to 2006 is used to establish the forecasting model, and a set of data from 2007 to 2011 is used to test the model. Chen's method [4] is also presented for comparison with the proposed method. Chen's method fails forecast the number of tourists in 2009 and 2010. The proposed method succeeds in forecasting all the testing data from 2007 to 2011. In forecasting unknown future data, the proposed method provides more rules than other methods do.

Human Arm’s Muscle Force Estimation Model Based on Fuzzy Theory

Muscle force estimation, is important in order to understand human control strategies, rehabilitation, motion analysis and human-machinery interfacing. To estimate the force, bio-signals analysis is used. Bio-signals enable signal recognition and analysis of muscle action and it is used to estimate muscle force. In this work, a method to estimate human muscle force is proposed. This model, which is based on Fuzzy Logic Theorem, is fast and easy to implement. Fuzzy Model process a raw of rectified smoothed electromyography (RSEMG) signal and extract muscle force. Our model is a general one, it can use for any muscles. To prove that, we used biceps brachii muscle of the left and right arms and two different arm muscles as validation. Our results, demonstrate that the new model improves the accuracy of muscle force estimation. This model can efficiently extract muscle force features from (EMG) signals. Our results showed that algorithmic regression exceeds 99%. The mean square error (MSE) results for Fuzzy Model was very small (MSE equal to 1.11x10-08).

The Network Data Clustering Algorithm Based on Fuzzy Processing Model

Because the attributes of the network data become more and more complex, the useful clustering attributes are increasing greatly. The traditional network data clustering algorithm is sensitive with the initial center of the network data clustering which cause the operation efficiency of the algorithm is quite low. This paper proposes a kind of network data fast clustering algorithm based on fuzzy processing model. The algorithm solves the issue of fixed center in the traditional network data clustering algorithm. The experiment illustrates the algorithm has better clustering accuracy and the convergence speed is quickly.

A fuzzy model based adaptive PID controller design for nonlinear and uncertain processes

We develop a novel adaptive tuning method for classical proportional–integral–derivative (PID) controller to control nonlinear processes to adjust PID gains, a problem which is very difficult to overcome in the classical PID controllers. By incorporating classical PID control, which is well-known in industry, to the control of nonlinear processes, we introduce a method which can readily be used by the industry. In this method, controller design does not require a first principal model of the process which is usually very difficult to obtain. Instead, it depends on a fuzzy process model which is constructed from the measured input–output data of the process. A soft limiter is used to impose industrial limits on the control input. The performance of the system is successfully tested on the bioreactor, a highly nonlinear process involving instabilities. Several tests showed the method's success in tracking, robustness to noise, and adaptation properties. We as well compared our system's performance to those of a plant with altered parameters with measurement noise, and obtained less ringing and better tracking. To conclude, we present a novel adaptive control method that is built upon the well-known PID architecture that successfully controls highly nonlinear industrial processes, even under conditions such as strong parameter variations, noise, and instabilities.

Isolated Intersection Traffic Signal Timing Problem: A Fuzzy Control Approach and a Case Study

Considering the randomness of the traffic flow, a self-adaptive control method for isolated intersection signal timing is proposed, which imitates the traffic polices signal controlling behaviors. Firstly, we define the concepts of Busy Level (for the phase that has got the right of way) and Urgent Level (for the other phases that have not got the right of way). Based on the definitions, the signal control method is put forward, which can be summarized in brief that when Urgent Level is greater than Busy Level, the phase with the highest Urgent Level gains the right of way. Then, based on the fuzzy control theory, we elaborate the fuzzy modeling process of Busy Level and Urgent Level and put forward an optimization method of the membership function using the traversing method. The proposed method is a breakthrough comparing with the old methods that optimize the circle time, the split or the amount of the green time extension. The method has the charactaristics of no fixed circle time, no fixed phase sequence and is more effective to suit the fluctuant traffic flow. At last, the computational results of an simulation demostrates that the proposed method is far more effective than the pretimed signal timing methods.

Web Fuzzy Clustering Processing Model

In this paper, we combine the web mining and fuzzy clustering and give the concept of web fuzzy clustering processing model and its application. We also introduce the web fuzzy direct clustering method in brief. Web fuzzy clustering can be used in the web user clustering and web page clustering of web usage mining.

Development of an adaptive fuzzy logic-based inverse dynamic model for laser cladding process

The precision, performance, and robustness of model-based controllers depend, to a large extent, on the accuracy of the inverse dynamic model which is incorporated in the design of the controller. Due to complex nature of the laser cladding process and presence of time-varying uncertainties, derivation of an accurate mathematical inverse dynamic model of the process is very difficult, and involves many unknown parameters. The inverse dynamic model of the complex nonlinear laser cladding process, which is difficult to be described mathematically, can be described by a fuzzy logic-based inverse dynamic model constructed form input–output data. In this paper, the development of an adaptive fuzzy inverse dynamic model of the laser cladding process, using a systematic fuzzy modelling approach is presented. In a closed-loop laser cladding process, the scanning speed of the substrate is required to produce a clad with desired geometry and quality. In this paper, a fuzzy inverse dynamic model that describes the scanning speed as a function of the cladding parameters in particular the clad height is developed. The developed fuzzy model is validated by comparing the model output with experimental data. The results are very promising and show that fuzzy models can accurately describe the process dynamics.