A broad-deep fusion network-based fuzzy emotional intention inference model for teaching validity evaluation

Environmental comprehensive assessment of agricultural systems at the farm level using fuzzy logic: A case study in cane farms in Iran

In this paper, a new kind of fuzzy regression model suggested by the author in papers [1] and [2] will be restated more clearly. Then a consistency between the new fuzzy regression modeling and fuzzy inference modeling will be presented, showing its added advantage for application problems. Based on the consistency, a method for combining fuzzy regression and fuzzy inference will be introduced in this paper. This will serve as the starting point for applying the two kinds of information resources, quantitative and qualitative information resources, simultaneously in practical prediction problems.

To construction effective simulation meta-models for complex physical simulation system, the “curse of dimension” and the “uncertain and imprecise information” problems have to be addressed firstly. Although simulation meta-models based on neural networks can obtain well performance, the fuzzy inference mechanism of domain expert for practical application problems cannot be simulated. Thus, some prediction results may be contradicted with that of the actual physical systems. Aim at these problems, a novelty selective ensemble (SEN) simulation meta-modeling approach based on multiple kernel feature extraction and fuzzy inference modeling is proposed in this paper. A new ensemble construction method based on multiple candidate kernel latent features is used to produce candidate training sub-samples. Candidate sub-models based on Mamdani's fuzzy inference are constructed with these training sub-samples. Brand and bound (BB) and adaptive weighting fusion (AWF) algorithms are used to select and combine the ensemble sub-models to obtain final SEN simulation meta-model. Simulate results based on synthetic data show that the proposed approach is effective.

The geometric accuracy and surface roughness are mainly affected by the flank wear at the minor cutting edge in finish machining. A genetic algorithm-based fuzzy estimator obtained by a fuzzy inference algorithm to evaluate the minor flank wear length in finish milling is introduced. The fuzzy inference rules are trained by genetic algorithms (GA) through practice. Fuzzy membership functions and rules are usually decided upon subjectively. In this paper, the performance of the fuzzy estimator may be improved if the fuzzy inference model is supplemented by a genetic-based learning mechanism. The features sensitive to minor flank wear are extracted from the dispersion analysis of a time series AR model of the feed directional acceleration of the spindle housing. Linguistic rules for fuzzy estimation are constructed using these features, and then fuzzy inferences are carried out with test data sets under various cutting conditions. The proposed system turns out to be effective for estimating minor flank wear length, and its mean error is less than 13%.

A new fuzzy inference model, SIRMs (Single Input Rule Modules) Connected Fuzzy Inference Model, is proposed for plural input fuzzy control. For each input item, an importance degree is defined and single input fuzzy rule module is constructed. The importance degrees control the roles of the input items in systems. The model output is obtained by the summation of the products of the importance degree and the fuzzy inference result of each SIRM. The proposed model needs both very few rules and parameters, and the rules can be designed much easier. The new model is first applied to typical secondorder lag systems. The simulation results show that the proposed model can largely improve the control performance compared with that of the conventional fuzzy inference model. The tuning algorithm is then given based on the gradient descent method and used to adjust the parameters of the proposed model for identifying 4-input 1-output nonlinear functions. The identification results indicate that the proposed model also has the ability to identify nonlinear systems.

In this paper, a new method is proposed for the remediation of contaminated groundwater. The method is based on fuzzy inference and risk evaluation. The effectiveness of the presented method is assessed in numerical simulations. The main results of the study are as follows: 1) A pump-and-treat control algorithm was proposed by using the fuzzy inference and the human health risk calculation model, 2) It was found that applying the proposed algorithm was likely to reduce the pumped quantity, and 3) It was found that the proposed model can be used to notify residents of the human health risk; the model is based on the ASTM RBCA model for residents. A new fuzzy control system for contaminated groundwater can be used as a useful model for characterising the effects of contaminants on human health and providing helpful information on the human risk assessment of the contaminated groundwater site.

Rate of penetration (ROP) prediction is crucial for the drilling optimization and cost-savings. In this paper, a novel drilling ROP prediction method is proposed and the prediction model can be divided into two stages (data pre-processing and T-S fuzzy inference modeling). In the first stage, four data pre-processing techniques (Reduction, re-sampling, wavelet filtering, and normalization) are used step by step to improve the quality of drilling data. In the second stage, T-S fuzzy inference method is introduced to establish the ROP prediction model. The experiment is executed by using the data from actual drilling process and the results demonstrate the effectiveness of proposed method in prediction accuracy compared with two conventional methods (response surface method and support vector regression).

A fuzzy decision tree method for fault classification in the steam generator of a pressurized water reactor

Anemia is considered one of the most common diseases that affect the human body. In case of prolonged existence, it can lead to the development of chronic diseases due to excessive load on the vital organs. Effective treatments are available with early detection of the disease and largely depend on its underlying cause. In terms of severity, anemia can be divided into mild, moderate, and severe according to the level of hemoglobin. The morphological classification provides for the division of anemia into microcytic, normocytic, and macrocytic depending on the average volume of red blood cells. According to the hemoglobin, content anemia can be hypochromic, normochromic and hyperchromic. The article explores scientific and technical information on classification and fuzzy logic as well as describes the development of a decision support system for anemia diagnosis using a fuzzy logic inference model. During the analysis of existing literature, it was found that the use of naive Bayesian methods is optimal for solving the problem of determining anemia. But there is a possibility of minor errors in measuring features, so it is necessary to use the theory of fuzzy sets. Modulation of fuzzy logic inference was implemented using the Mamdani algorithm. The developed information system has two levels. The first level determines the severity of anemia. The second level is responsible for belonging to one of the classes: microcytic hypochromic, normocytic normochromic, and macrocytic. The software was written in Java on the basis of its own library to implement a computational algorithm for morphological classification of anemia and classification by severity according to the indicators of hematological laboratory tests. The application of the proposed architectural design of the system saves time for decision-making and eliminates the need for additional manpower to solve the problems of information and analytical support for management decision-making in support of business processes of anemia monitoring

A neural fuzzy inference system and its application to product quality robust estimation

Adjusting the membership functions to satisfy one pattern may deteriorate the inference outcomes of the others. This incompatible issue can be retarded by the extension theory. A novel extension-based fuzzy modeling method, which differs from the traditional fuzzy inference, is proposed. Instead of directly applying the given data to building the fuzzy model, the given data are transformed to another domain by a sigmoidal function to obtain a better fuzzy model. We also define the extended correlation functions to relate the data with the fuzzy sets. During the refining process, the extended fuzzy model, which considers the positive and negative sets simultaneously, is adjusted by the gradient descent method. Simulation results from both single-input-single-output and double-input-single-output systems verified that better results than the conventional methods can be obtained.

An approach for environmental risk assessment of engineered nanomaterials using Analytical Hierarchy Process (AHP) and fuzzy inference rules

Estimating the intention of space objects plays an important role in aircraft design, aviation safety, military and other fields, and is an important reference basis for air situation analysis and command decision-making. This paper studies an intention estimation method based on fuzzy theory, combining probability to calculate the intention between two objects. This method takes a space object as the origin of coordinates, observes the target’s distance, speed, relative heading angle, altitude difference, steering trend and etc., then introduces the specific calculation methods of these parameters. Through calculation, values are input into the fuzzy inference model, and finally the action intention of the target is obtained through the fuzzy rule table and historical weighted probability. Verified by simulation experiment, the target intention inferred by this method is roughly the same as the actual behavior of the target, which proves that the method for identifying the target intention is effective.

Probing fractured reservoir of enhanced geothermal systems with fuzzy inversion model

The purpose of this paper is to study the issues raised by the implementation of a tool aimed at protecting air traffic sectors against overload in a large-scale air traffic system, acting on the basis of short term prediction. It shows how to overcome the computational complexity using a decentralised and co-ordinated system composed of a co-ordination level and a control level. The study points on the co-ordination level which decompose the large sector network into several smaller overlapping subnetworks that can be controlled independently. A modified interaction prediction method is developed using a fuzzy model. This model provides the interaction prediction of the control units on the basis of imprecise information and aggregated reasoning in order to decrease the multiple data transfer between the control and co-ordination levels. Time complexity of the fuzzy model inference is also studied, the antagonistic goals of reducing inference time of the fuzzy rule-base and increasing the accuracy of the interaction prediction is commented. Classical fuzzy inference models and rule interpolation techniques are then compared.KeywordsLarge-scale SystemCo-ordinationFuzzy ModelApproximate ReasoningNeural NetworkAir Traffic Flow Management.