Neural Fuzzy Inference System Model Research Articles

Employing Adaptive Neural Fuzzy Inference System Model Via Meta-heuristic Algorithms for Predicting Maximum Dry Density

Employing Adaptive Neural Fuzzy Inference System Model Via Meta-heuristic Algorithms for Predicting Maximum Dry Density

Estimation of optimal process parameters for minimum wear in the application of SiC/RHA reinforced Al7075 hybrid composites using ANN, ANFIS, and GA

Increasing demand for high-performance materials has led to the exploration of composite materials for enhanced mechanical properties. In this study, a composite of silicon carbide particulate and rice husk ash (RHA) in varying proportions was utilized to reinforce an aluminum alloy (Al7075) hybrid composite fabricated through the stir casting technique. Microstructure examination via an optical microscope ensured the homogeneous distribution of reinforced particles. Wear was evaluated using a pin-on-disc apparatus, considering material factors (% of SiC and % of RHA) and mechanical wear factors (load applied, speed of rotation, and sliding distance). Experimental data were used to develop artificial neural network and adaptive neural fuzzy inference system models, which demonstrated high predictive accuracy. An objective function, formulated to minimize wear via regression analysis, guided the application of a genetic algorithm to determine optimal process parameters. The optimal combination, resulting in a minimum wear of 34.5 µm, comprised 12% SiC, 7% RHA, a sliding speed of 1.9 m/s, an applied load of 11.5 N, and a sliding distance of 715 mm. This study concludes with recommendations for further research and implications for composite material design and optimization.

Employing adaptive neural fuzzy inference system model via meta-heuristic algorithms for predicting undrained shear strength

Employing adaptive neural fuzzy inference system model via meta-heuristic algorithms for predicting undrained shear strength

An Adaptive Neural Fuzzy Inference System model for freeway travel time estimation based on existing detector facilities

While volume-counting detectors are currently installed in many freeways and have the capabilities of collecting high-resolution traffic information, travel time estimations are usually only based on spot speeds collected by the detectors, which could only produce rough estimates of travel time during peak hours when roadways are congested. In addition to spot speeds, these detectors provide directional volume counts and occupancy, which are useful for a better travel time estimation. This research used data from volume counting detectors and made Adaptive Neural Fuzzy Inference System (ANFIS) to automatically estimate real-time travel times. ANFIS’s ability to learn traffic patterns allows accurate and reliable real-time travel time estimation even with missing or corrupted data. ANFIS also proves to be a powerful tool for estimating future travel times on freeways. For easier use by practitioners and researchers, the method used in this study was implemented into a software package, named FTTE (Freeway Travel Time Estimator). Finally, a case study was conducted using the new approach and the results were compared for both congested and uncongested traffic conditions.

An Adapted Neural-Fuzzy Inference System Model Using Preprocessed Balance Data to Improve the Predictive Accuracy of Warfarin Maintenance Dosing in Patients After Heart Valve Replacement.

Tailoring warfarin use poses a challenge for physicians and pharmacists due to its narrow therapeutic window and substantial inter-individual variability. This study aimed to create an adapted neural-fuzzy inference system (ANFIS) model using preprocessed balance data to improve the predictive accuracy of warfarin maintenance dosing in Chinese patients undergoing heart valve replacement (HVR). This retrospective study enrolled patients who underwent HVR between June 1, 2012, and June 1, 2016, from 35 centers in China. The primary outcomes were the mean difference between predicted warfarin dose by ANFIS models and actual dose and the models' predictive accuracy, including the ideal predicted percentage, the mean absolute error (MAE), and the mean squared error (MSE). The eligible cases were divided into training, internal validation, and external validation groups. We explored input variables by univariate analysis of a general linear model and created two ANFIS models using imbalanced and balanced training sets. We finally compared the primary outcomes between the imbalanced and balanced ANFIS models in both internal and external validation sets. Stratified analyses were conducted across warfarin doses (low, medium, and high doses). A total of 15,108 patients were included and grouped as follows: 12,086 in the imbalanced training set; 2820 in the balanced training set; 1511 in the internal validation set; and 1511 in the external validation set. Eight variables were explored as predictors related to warfarin maintenance doses, and imbalanced and balanced ANFIS models with multi-fuzzy rules were developed. The results showed a low mean difference between predicted and actual doses (< 0.3 mg/d for each model) and an accurate prediction property in both the imbalanced model (ideal prediction percentage, 74.39-78.16%; MAE, 0.37 mg/daily; MSE, 0.39 mg/daily) and the balanced model (ideal prediction percentage, 73.46-75.31%; MAE, 0.42 mg/daily; MSE, 0.43 mg/daily). Compared to the imbalanced model, the balanced model had a significantly higher prediction accuracy in the low-dose (14.46% vs. 3.01%; P < 0.001) and the high-dose warfarin groups (34.71% vs. 23.14%; P = 0.047). The results from the external validation cohort confirmed this finding. The ANFIS model can accurately predict the warfarin maintenance dose in patients after HVR. Through data preprocessing, the balanced model contributed to improved prediction ability in the low- and high-dose warfarin groups.

MANAGING TARGET COSTS AT THE DEVELOPMENT STAGE OF A PRODUCT

At the development stage of a product, target cost management has a significant effect on achievement and maintenance of product competitiveness in a given market and the desired target profit as the primary aim of production. Target cost management, i.e., target profit management, is performed by evaluating individual variations of a conceptual and preliminary design solution for a new product and by choosing the solution with the lowest development costs. The paper presents the use of Adaptive Neural Fuzzy Inference System model for managing development costs on the example of double-row angular contact ball bearings as an example of applying the above approach to market positioning in relation to competitors.

The implementation of univariable scheme-based air temperature for solar radiation prediction: New development of dynamic evolving neural-fuzzy inference system model

New development of dynamic evolving neural-fuzzy inference system model was proposed for modeling solar radiation based on univariable air temperature scheme. The proposed predictive model was validated against several robust heuristic regression models including multivariate adaptive regression spline, M5 model tree and least square support vector regression. Historical data of solar radiation and air temperature for two meteorological stations: Adana and Antakya located in Turkey, were investigated. The prediction results were evaluated based on several statistical metrics. The modeling is conducted based on different data division scenarios (training/testing) phases. The attained prediction results evidenced the potential of the dynamic evolving neural-fuzzy inference system over the comparable models and for all the investigated data division scenarios. In quantitative terms, dynamic evolving neural-fuzzy inference system was enhanced the solar radiation prediction capability over the multivariate adaptive regression spline, M5 model tree and least square support vector regression models (Adana-Antakya) by 20–42%, 29–47% and 19–43% based on the root mean square errors metric. The applied predictive models were compared with the field measured average monthly solar radiation values and it was found that the proposed model estimates accurately. However, the comparable models were exhibited a considerable overestimation of the monthly averaged solar radiation values and for both inspected stations specifically in the summer months (June, July and August).

Modeling and prediction of water quality parameters using a hybrid particle swarm optimization–neural fuzzy approach

This work focuses on the correlations among water quality parameters such as total hardness, total dissolved solids, electrical conductivity and total alkalinity with water parameters pH, temperature and the sum of mill equivalents of cations and anions in water reservoir in Kermanshah province (located in the middle of the western part of Iran). The data of water quality of monitoring sites were collected over year 2015. To predict and simulate water quality parameters, two data-driven models, i.e., adaptive neural fuzzy inference system and a hybrid adaptive neural fuzzy inference system structure trained by particle swarm optimization technique, were used. The main advantages of these methods are their high accuracy and very fast computational speed to predict unknown data. The results indicated that implementation of two models is highly satisfactory for predicting inorganic indicators of water quality. However, the flexibility of particle swarm optimization–adaptive neural fuzzy inference system method in modeling is better than adaptive neural fuzzy inference system approach. To prove this, the correlation coefficient, mean absolute error, root mean square error and t statistics were calculated as the error criterion. The overall (training and testing) mean relative error percentage, mean absolute error, root mean square error, correlation coefficient and t statistics obtained by the proposed particle swarm optimization–adaptive neural fuzzy inference system model are less than 3.50, 11.60, 18.90, 0.95 and 0.38%, respectively. The results provide a useful approach that uses water parameters to estimate water quality in water reservoir for water treatment and pollution management.

A Modified Artificial Neural Fuzzy Inference System Model for Brain Tumor Segmentation

A Modified Artificial Neural Fuzzy Inference System Model for Brain Tumor Segmentation

Modelling CO&lt;sub&gt;2&lt;/sub&gt; and NO&lt;sub&gt;x&lt;/sub&gt; on signalized roundabout using modified adaptive neural fuzzy inference system model

Modelling CO&lt;sub&gt;2&lt;/sub&gt; and NO&lt;sub&gt;x&lt;/sub&gt; on signalized roundabout using modified adaptive neural fuzzy inference system model

Contribution to optimize decision parameters in activated-sludge process using ANFIS model

The monitoring of activated sludge processes is difficult because of their slow dynamics, the complexity of their behavior. To obtain the desired level of performance in an activated sludge system, a proper balance must be maintained between aeration (energy consumption) and the quantity of sludge recirculation. The objective of this study is to determine the necessary amount of recirculated sludge in activated sludge process in the aim to reduce the energy consumption with respect of environmental standards. To achieving this objective, a comparative study between three models was performed. In order to reduce the complexity of activated-sludge process, the first is a linear model based on principal component analysis method (PCA), the second is an Adaptive Neural Fuzzy Inference System model (ANFIS), while the third is based on coupling of those two approaches. The best values of validation criterion obtained with the last method show the high performance of the hybrid model proposed…

Adaptive Neural Fuzzy Inference System Models for Predicting the Shear Strength of Reinforced Concrete Deep Beams

A reinforced concrete member in which the total span or shear span is especially small in relation to its depth is called a deep beam. In this study, a new approach based on the Adaptive Neural Fuzzy Inference System (ANFIS) is used to predict the shear strength of reinforced concrete (RC) deep beams. A constitutive relationship was obtained correlating the ultimate load with seven mechanical and geometrical parameters. These parameters contain Web width, Effective depth, Shear span to depth ratio, Concrete compressive strength, Main reinforcement ratio, Horizontal shear reinforcement ratio and Vertical shear reinforcement ratio.The ANFIS model is developed based on 214 experimental database obtained from the literature. The data used in the present study, out of the total data, 80% was used for training the model and 20% for checking to validate the model. The results indicated that ANFIS is an effective method for predicting the shear strength of reinforced concrete (RC) deep beams and has better accuracy and simplicity compared to the empirical methods.

The Study of System Model Based on Fuzzy Inference and Neural Network

For the characteristics of fuzziness, indeterminacy etc. in nonlinear systems, this paper, combining fuzzy inference system with neural network, Adaptive Neural Fuzzy Inference System model had been provided in the paper, ANFIS method is based on Sugeno fuzzy model and has a structure similar to neural network that tunes the parameters of the fuzzy inference system with back propagation algorithm and least - square method and can produce fuzzy rules automatically. This solutes extraction of fuzzy rules and learning of parameters of membership functions play an essential role in the design. This paper gives the simulation example of modeling a typical system with ANFIS method and good result is obtained.

The Research of Neural-Fuzzy Inference System Model for Arm of Excavator Robot

To improve the trajectory planning control accuracy of the working device of excavator robot when working for excavation, reduce working device of excavator robot to be the two joints two dimension robot arm composed of arm and bucket when analyses. Creating inverse kinematics model, need to relate to terminal position and orientation space of bucket and joint space and cylinder space of working devices to plan trace, so that to control excavator robot in each space. Because of the geometry complexity of inverse kinematics, the nonlinear of electro-hydraulic system, the uncertainty and non-uniqueness for inverse mapping, lead to the difficulty of trajectory planning. To improve the precision for tracking desired trace, obtaining inverse kinematics mapping between terminal trace and joint angle, use two adaptive neural-fuzzy inference system(ANFIS) to learn inverse mapping relations between (x, y) two joint coordinate and joint angle, create ANFIS inverse mapping model. Select I/O curve data of inverse mapping to train ANFIS structure, obtain I/O mapping curve of fuzzy model, realize the aim of obtaining corresponding joint angle based on given desired excavation trace. Use the model to trace expected motion trace finally, indicate by simulation that trace precision can meet the actual demands.