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Kinetic model of vibration screening for granular materials based on biological neural network

Particuology 2024年第5期88卷 98-106页

作者： Zhan Zhao Yan Zhang Fang Qin Mingzhi Jin School of Agricultural Engineering Jiangsu UniversityZhenjiang212013China Shandong Academy of Agricultural Machinery Sciences Ji'nan250100China

The kinetic model is the theoretical basis for optimizing the structure and operation performance of vibration screening *** this paper,a biological neurodynamic equation and neural connections were established according to the motion and interaction properties of the material under vibration *** material feeding to the screen and the material passing through apertures were considered as excitatory and inhibitory inputs,respectively,and the generated stable neural activity landscape was used to describe the material distribution on the 2D screen *** dynamic process of material vibration screening was simulated using discrete element method(DEM).By comparing the similarity between the material distribution established using biological neural network(BNN)and that obtained using DEM simulation,the optimum coefficients of BNN model under a certain screening parameter were determined,that is,one relationship between the BNN model coefficients and the screening operation parameters was *** screening parameters were randomly selected,and the corresponding relationships were established as a ***,with straw/grain ratio,aperture diameter,inclination angle,vibration strength in normal and tangential directions as inputs,five independent adaptive neuro-fuzzy inference systems(ANFIS)were established to predict the optimum BNN model coefficients,*** training results indicated that ANFIS models had good stability and *** flexibility and adaptability of the proposed BNN method was demonstrated by modeling material distribution under complex feeding conditions such as multiple regions and non-uniform rate.

关键词： Kinetic model Material distribution Vibration screening Biological neural network DEM simulation adaptive neuro-fuzzy inference systems

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Fatigue Life Estimation of High Strength 2090-T83 Aluminum Alloy under Pure Torsion Loading Using Various Machine Learning Techniques

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Fluid Dynamics & Materials Processing 2023年第8期19卷 2083-2107页

作者： Mustafa Sami Abdullatef Faten NAlzubaidi Anees Al-Tamimi Yasser Ahmed Mahmood Electromechanical Engineering Department University of Technology-IraqBaghdadIraq

The ongoing effort to create methods for detecting and quantifying fatigue damage is motivated by the high levels of uncertainty in present fatigue-life prediction approaches and the frequently catastrophic nature of fatigue *** fatigue life of high strength aluminum alloy 2090-T83 is predicted in this study using a variety of artificial intelligence and machine learning techniques for constant amplitude and negative stress ratios(R?1).Artificial neural networks(ANN),adaptive neuro-fuzzy inference systems(ANFIS),support-vector machines(SVM),a random forest model(RF),and an extreme-gradient tree-boosting model(XGB)are trained using numerical and experimental input data obtained from fatigue tests based on a relatively low number of stress *** particular,the coefficients of the traditional force law formula are found using relevant numerical *** is shown that,in comparison to traditional approaches,the neural network and neuro-fuzzy models produce better results,with the neural network models trained using the boosting iterations technique providing the best *** strong models from weak models,XGB helps to predict fatigue life by reducing model partiality and variation in supervised *** neural models can be used to predict the fatigue life of alloys more accurately than neural networks and traditional methods.

关键词： Fatigue life high strength aluminum alloy 2090-T83 neuro-fuzzy tree boosting model neural networks adaptive neuro-fuzzy inference systems random forest support vector machines

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Evaluation of Innovation Performance in Construction with ANFIS

Evaluation of Innovation Performance in Construction with AN...

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2011 International Conference on Management and Service Science（ICMSS 2011）

作者： Thoedtida Thipparat Department of Civil Engineering Prince of Songkla University

ISBN: (纸本)9789810888688

This study aims to measure the innovation performance of consulting companies in Thailand construction *** variables include factors associated with quality objectives,engineering objectives,innovation objectives and technical information resource adaptive neuro-fuzzy inference systems （ANFIS） is used to develop an evaluation model by applying Sugeno *** decision makers can use the fuzzy neural network approach to adjust the resource allocation to meet the company’s objectives.

关键词： innovation quality adaptive neuro-fuzzy inference systems Sugeno ANFIS

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Application of four machine-learning methods to predict short-horizon wind energy

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Global Energy Interconnection 2023年第6期6卷 726-737页

作者： Doha Bouabdallaoui Touria Haidi Faissal Elmariami Mounir Derri El Mehdi Mellouli Laboratory LAGES Ecole Hassania des Travaux Publics(EHTP)Casablanca 20230Morocco Laboratory LESE Ecole Nationale Supérieure d’Electricitéet de Mécanique(ENSEM)Casablanca 20100Morocco Laboratory LESA School of Applied SciencesSidi Mohamed Ben Abdellah UniversityFez 30000Morocco

Renewable energy has garnered attention due to the need for sustainable energy *** power has emerged as an alternative that has contributed to the transition towards cleaner *** the importance of wind energy grows,it can be crucial to provide forecasts that optimize its performance *** intelligence(AI)methods have risen in prominence due to how well they can handle complicated systems while enhancing the accuracy of *** study explored the area of AI to predict wind-energy production at a wind farm in Yalova,Turkey,using four different AI approaches:support vector machines(SVMs),decision trees,adaptive neuro-fuzzy inference systems(ANFIS)and artificial neural networks(ANNs).Wind speed and direction were considered as essential input parameters,with wind energy as the target parameter,and models are thoroughly evaluated using metrics such as the mean absolute percentage error(MAPE),coefficient of determination(R~2),and mean absolute error(MAE).The findings accentuate the superior performance of the SVM,which delivered the lowest MAPE(2.42%),the highest R~2(0.95),and the lowest MAE(71.21%)compared with actual values,while ANFIS was less effective in this *** main aim of this comparative analysis was to rank the models to move to the next step in improving the least efficient methods by combining them with optimization algorithms,such as metaheuristic algorithms.

关键词： Wind Energy Prediction Support Vector Machines Decision Trees adaptive neuro-fuzzy inference systems Artificial Neural Networks

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