Selective ensemble simulation meta-modeling approach based on multiple kernel features extraction and fuzzy inference

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

Purpose of research. The research presented in this article is aimed at improving the accuracy of determining the color shade. The developed fuzzy-logical color recognition system was used as the subject of the study. The efficiency indicator was the result of calculating the sensitivity area percentage and RMSE of the developed method.Methods. A method based on fuzzy logic has been developed and implemented, namely, on the structure of Mamdani's fuzzy inference, which consists of the following stages: fuzzification, fuzzy logical inference, defuzzification. Triangular membership functions were used at the fuzzification stage. As a compositional rule, 12 input variables were used, combined on the basis of Zadeh's compositional rule in 27. At the defuzzification stage, the area ratio method was used. The object of the study was the developed mathematical model for determining color.Results. A mathematical model has been developed, consisting of 4 steps, which guarantees a clear definition of 9 colors and their shades. Based on the estimation of the root of the mean square error, it was concluded that the proposed model is better than traditional options. It is expressed by the fact that the developed method reacts on the interval of the entire surface of output variables, while traditional methods have dead zones to changes in input variables.Conclusion. A fuzzy-logical color recognition system was developed. In the course of experimental studies, it was found that the RMSE and sensitivity indicators have better results in relation to other systems.

Modeling collinear data using double-layer GA-based selective ensemble kernel partial least squares algorithm

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%.

Type-2 fuzzy sets and systems can better handle uncertainties compared to its type-1 counterpart, and the widely applied Mamdani and TSK fuzzy inference approaches have been both extended to support interval type-2 fuzzy sets. Fuzzy interpolation enhances the conventional Mamdani and TKS fuzzy inference systems, which not only enables inferences when inputs are not covered by an incomplete or sparse rule base but also helps in system simplification for very complex problems. This paper extends the recently proposed fuzzy interpolation approach TSK+ to allow the utilization of interval type-2 TSK fuzzy rule bases. One illustrative case based on an example problem from the literature demonstrates the working of the proposed system, and the application on the cart centering problem reveals the power of the proposed system. The experimental investigation confirmed that the proposed approach is able to perform fuzzy inferences using either dense or sparse interval type-2 TSK rule bases with promising results generated.

The global population is at risk from both communicable and non-communicable deadly diseases, including cardiovascular disease. Early detection and prevention of cardiovascular disease require an accurate self-detection model. Therefore, this study introduces a novel fuzzy entropy DEMATEL inference system for accurate self-detection of cardiovascular disease. It combines fuzzy DEMATEL, entropy, and Mamdani fuzzy inference, utilizing innovative strategies like attribute reduction, entropy-based clustering, influential factor selection, and rule reduction. The system achieves high accuracy (98.69%) and sensitivity (98.62%), outperforming existing methods. Validation includes satisfactory factor analysis, performance measures and statistical analysis, demonstrating its effectiveness in addressing complexity and prioritizing factors.

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.

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 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).

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.

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

To enhance driving safety and comfort, this study aims to investigate the level of driving risk under different road conditions in urban areas. The you only look once version five-deep simple online and real time tracking (YOLOv5-DeepSORT) model is employed to collect traffic data of forward vehicles, and novel methodologies are proposed to calculate the relative orientation, distance, and velocity between the forward vehicles and the main vehicle. Additionally, this study introduces an innovative function to determine the proximity of the forward vehicles to the main vehicle’s lane, along with a straightforward model for vehicle lane change recognition. By utilizing the relative orientation, distance, and velocity between forward vehicles and the main vehicle, information on forward vehicles’ lane changes, and the main vehicle’s velocity as inputs, an urban road driving risk identification model based on Mamdani fuzzy inference is constructed. The model was validated with real vehicle experiments that were conducted under various road conditions in the city of Zhenjiang. The results demonstrate that the proposed driving risk identification model achieves an impressive accuracy rate of 92.44% and exhibits good identification performance.

Classifier ensemble generation and selection with multiple feature representations for classification applications in computer-aided detection and diagnosis on mammography

Different frequency spectral feature sub-sets of mill shell vibration and acoustical signals contain different information for modeling parameters of mill load. Selective ensemble modeling based on manipulate training samples can improve generalization performance of soft sensor model. Based on the former studies, we proposed a new dual layer selective ensemble learning strategy. At first, vibration and acoustical frequency spectral feature sub-sets are extracted and selected by the methods in literature [15]. Then, selective ensemble modeling method based on genetic algorithm and kernel partial least squares (GASEN-KPLS) is used to construct the first layer selective ensemble model for every feature sub-set. Finally, brand and band (BB) and adaptive weighting fusion (AWF) algorithm is use to select and combine the outputs of the first layer models to construct the second layer selective ensemble model. Results indicate that the proposed approach can perform reasonably well on estimate mill load parameters of a laboratory ball mill grinding process.