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A assessment method for ergonomic risk based on fennec fox optimization algorithm and generalized regression neural network

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DISPLAYS 2025年 86卷

作者： Pei, Huining Cao, Jingru Ding, Man Wang, Ziyu Chen, Yunfeng Hebei Univ Technol Sch Architecture & Art Design Tianjin 300131 Peoples R China

The Rapid Upper Limb Assessment method depends mainly on the subjective perception of the assessor, resulting in inconsistent results and a low sensitivity to changes in input variables. In this study, a new scoring system is developed using the Fennec Fox Optimization Algorithm and the generalized regression neural network approach to overcome the drawbacks of traditional method. First, the deep convolutional neural network was used to identify the keypoints of the human working posture in an image and calculate the joint angle. Second, the new model was used to improve the traditional method, and the prediction results for different postural risk scores were output. The proposed network was trained and tested, and the data were analyzed for comparison. Finally, the correlation between the top 15 predictions in the dataset and the scores was verified. The comparison results show that the proposed method performed better than the other methods in terms of the mean absolute error, mean square error, root-mean-square error, mean absolute percentage error, coefficient of determination, runtime, and spatial complexity. Additionally, the proposed method is more sensitive to small variations in inputs, reducing the likelihood of obtaining the same assessment scores for different postures. This increased sensitivity makes the scoring method more conservative, resulting in a more accurate risk assessment, minimizing potential oversights, and effectively reducing occupational risk. These results underscore the effectiveness of the proposed method in improving the traditional assessment.

关键词： Rapid Upper Limb Assessment Deep convolutional neural network Fennec Fox Optimization Algorithm generalized regression neural network Human posture assessment

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generalized regression neural network in monthly flow forecasting

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CIVIL ENGINEERING AND ENVIRONMENTAL SYSTEMS 2005年第2期22卷 71-84页

作者： Cigizoglu, HK Istanbul Tech Univ Fac Civil Engn Div Hydraul TR-34469 Istanbul Turkey

The majority of the artificial neural network (ANN) applications to water resources data involve the employment of the feed forward back propagation method (FFBP). In this study, an ANN algorithm, generalized regression neural network (GRNN), was employed in monthly mean flow forecasting. The performances of the GRNN and the FFBP methods were compared initially for forecasting of monthly mean river flows and training the neural networks using the observed data;then the forecasting study was carried out using the AR model-generated synthetic monthly mean flow series for training stage. The GRNN simulations did not face the frequently encountered local minima problem of the FFBP applications and did not generate forecasts that are physically implausible. It was seer, that FFBP forecasting performance was sensitive to the randomly assigned initial weights. This problem, however, did not occur in the GRNN simulations. The GRNN approach does not require an iterative training procedure. unlike the FFBP method. GRNN forecasting performance was found to be superior to the FFBP, statistical, and stochastic methods in terms of the selected performance criteria.

关键词： generalized regression neural network monthly flow forecasting synthetic flow series local minima

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generalized regression neural network modeling based on inverse Duhem operator and adaptive sliding mode control for hysteresis in piezoelectric actuators

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2022年第5期236卷 1029-1037页

作者： Xu, Suan Wu, Zeyu Wang, Jing Hong, Kaixing Hu, Kaiming China Jiliang Univ Coll Mech & Elect Engn Hangzhou 310018 Peoples R China

A dynamic generalized regression neural network model based on inverse Duhem operator is proposed to characterize the rate-dependent hysteresis in piezoelectric actuators. As hysteresis is multi-valued mapping, and traditional neural network can only model the system with one-to-one mapping. An inverse Duhem operator is proposed to extract the dynamic property of the hysteresis. Moreover, it can transform the multi-valued mapping of the hysteresis into a one-to-one mapping to suit the input of neural network. In order to compensate the effect of the hysteresis in piezoelectric actuator, the adaptive sliding mode controller with a feedforward hysteresis compensator is developed for the tracking control of the piezoelectric actuator. Experimental results demonstrate superior tracking performance, which validate the practicability and effectiveness of the presented approach.

关键词： Piezoelectric actuators rate-dependent hysteresis inverse Duhem operator generalized regression neural network adaptive sliding mode control

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generalized regression neural network in modelling river sediment yield

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ADVANCES IN ENGINEERING SOFTWARE 2006年第2期37卷 63-68页

作者： Cigizoglu, HK Alp, M Tech Univ Istanbul Fac Civil Engn Div Hydraul TR-34469 Istanbul Turkey State Hydraul Works Reg Directorate 14 TR-34696 Istanbul Turkey

The majority of the artificial neural network applications in water resources involve the employment of feed forward back propagation method (FFBP). In this study another ANN algorithm, generalized regression neural network, GRNN, was used in river suspended sediment estimation. generalized regression neural network does not require an iterative training procedure as in back propagation method. The GRNN simulations do not face the frequently encountered local minima problem in FFBP applications and GRNN does not generate estimates physically not plausible. The neural networks are trained using daily river flow and suspended sediment data belonging to Juniata Catchment in USA. The suspended sediment estimations provided by two ANN algorithms are compared with conventional sediment rating curve and multi linear regression method results. The mean squared error and the determination coefficient are used as comparison criteria. Also the estimated and observed sediment sums are examined in addition to two previously mentioned performance criteria. The ANN estimations are found significantly superior to conventional method results. (C) 2005 Elsevier Ltd. All rights reserved.

关键词： suspended sediments feed forward back propagation method generalized regression neural network

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generalized regression neural network for Prediction of Peak Outflow from Dam Breach

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WATER RESOURCES MANAGEMENT 2017年第1期31卷 549-562页

作者： Sammen, Saad S. H. Mohamed, T. A. Ghazali, A. H. El-Shafie, A. H. Sidek, L. M. Univ Putra Malaysia Fac Engn Dept Civil Engn Serdang Selangor Malaysia Diyala Univ Coll Engn Dept Civil Engn Diyala Governorate Iraq Univ Malaya Fac Engn Dept Civil Engn Kuala Lumpur 50603 Malaysia Univ Tenaga Nas Coll Engn Dept Civil Engn Kajang Selangor Malaysia

Several techniques have been used for estimation of peak outflow from breach when dam failure occurs. This study proposes using a generalized regression artificial neural network (GRNN) model as a new technique for peak outflow from the dam breach estimation and compare the results of GRNN with the results of the existing methods. Six models have been built using different dam and reservoir characteristics, including depth, volume of water in the reservoir at the time of failure, the dam height and the storage capacity of the reservoir. To get the best results from GRNN model, optimized for smoothing control factor values has been done and found to be ranged from 0.03 to 0.10. Also, different scenarios for dividing data were considered for model training and testing. The recommended scenario used 90% and 10% of the total data for training and testing, respectively, and this scenario shows good performance for peak outflow prediction compared to other studied scenarios. GRNN models were assessed using three statistical indices: Mean Relative Error (MRE), Root Mean Square Error (RMSE) and Nash - Sutcliffe Efficiency (NSE). The results indicate that MRE could be reduced by using GRNN models from 20% to more than 85% compared with the existing empirical methods.

关键词： Damsafety Damfailure Breach outflow Peak outflow discharge generalized regression neural network

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generalized regression neural network Optimized by Genetic Algorithm for Solving Out-of-Sample Extension Problem in Supervised Manifold Learning

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neural PROCESSING LETTERS 2019年第3期50卷 2567-2593页

作者： Huang, Hong-Bing Xie, Zhi-Hong Guangxi Med Univ Sch Informat & Management Nanning 530021 Peoples R China Guangxi Presch Vocat Coll Dept Presch Educ Nanning 530022 Peoples R China

With the advent of big data, massive amounts of high-dimensional data have been accumulated in many fields. The assimilation and processing of such high-dimensional data can be particularly challenging. Manifold learning offers a means for effectively dealing with this challenge. However, the results of applying manifold learning to supervised classification have remained unsatisfactory. The out-of-sample extension problem is a critical issue that must be properly solved in this regard. Genetic algorithms (GAs) have excellent global search capabilities. This paper proposes a generalized regression neural network (GRNN) optimized by a GA for the solution of the out-of-sample extension problem. The prediction performance of a GRNN mainly depends on the appropriateness of the chosen smoothing factor. The essence of the GA optimization is the determination of the optimal smoothing factor of the GRNN, the optimized form of which is subsequently used to forecast the low-dimensional embeddings of the test samples. A GA can be used to obtain a better smoothing factor in a larger search space, resulting in enhanced prediction performance. Experiments were performed to enable a detailed analysis of the important parameters that affect the performance of the proposed algorithm. The results confirmed the effectiveness of the algorithm.

关键词： Manifold learning Dimensionality reduction Out-of-sample extension Genetic algorithm generalized regression neural network Optimization

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generalized regression neural network Based Approach as a New Tool for Predicting Total Dissolved Gas (TDG) Downstream of Spillways of Dams: a Case Study of Columbia River Basin Dams, USA

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ENVIRONMENTAL PROCESSES-AN INTERNATIONAL JOURNAL 2017年第1期4卷 235-253页

作者： Heddam, Salim Fac Sci Agron Dept Hydraul Div Univ 20 Aout 1955Route El HadaikBP 26 Skikda Algeria

A generalized regression neural network (GRNN) has been applied to estimate total dissolved gas uptake (Delta_TDG) that corresponds to the net difference between TDG at the tailwater and TDG in the forebay of dams, by using available measured data. To demonstrate the capability and robustness of the GRNN model, we used data for a period of 2 years: 2015 and 2016. For each year, we selected a 6-month period, from April to September, which corresponds to the spilling season. The data were available from two stations which operated simultaneously by the United States Geological Survey (USGS) and the U.S. Army Corps of Engineers (USACE): USGS ID 454249120423500 station at Columbia River, right bank, near Cliffs, Washington (John Day TailWater), and USGS ID 14105700 station at Columbia River at The Dalles, Oregon (The Dalles TailWater). For developing the models, we used six input variables measured at hourly time step: total dissolved gas measured in the forebay of the dam (TDG_F), water temperature (TE), barometric pressure (BP), spill from dam (SP), sensor depth (SD), and total flow (TF). The performances of the models were evaluated using the root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE) and correlation coefficient (R) statistics. The proposed GRNN model was compared to the multiple linear regression (MLR) with respect to their capability of modelling TDG, using several combinations of the input variables. According to the results obtained, it was found that: (i) the Delta_TDG could be successfully estimated using the GRNN model;and (ii) the GRNN M1 model, which uses all the six input variables is the best model among all others tested for the two stations.

关键词： Prediction Total dissolved gas Spillway generalized regression neural network Multiple linear regression

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generalized regression neural network for Predicting Traffic Flow

Generalized Regression Neural Network for Predicting Traffic...

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International Conference on Information and Communication Technology and Systems (ICTS)

作者： Buliali, Joko Lianto Hariadi, Victor Saikhu, Ahmad Mamase, Saprina Inst Teknol Sepuluh Nopember Dept Informat Kampus ITS Sukolilo 60111 Surabaya Indonesia Politekn Gorontalo Program Studi Tekn Informat Jl Muchlis Rahim Kab Bone Bolango Gorontalo Indonesia

ISBN: (纸本)9781509013814

Forecasting traffic flow is a popular research topic in Intelligent Transportation System. There have been several methods used for this forecasting, such as statistical methods, Bayesian network, neural network Model, Hybrid ARIMA and ANN. generalized regression neural network (GRNN) is an interesting model to be used in forecasting traffic flow, as it can predict data with dynamic change and non-linear in nature, which is generally found in traffic flow data. In this research, a GRNN model is set up to process traffic flow data, and comparing its results and the results from other predicting methods (ARIMA, Single Exponential Smoothing, and Moving Average). Leave One Out Cross Validation (LOOCV) is used in testing traffic flow data and Mean Absolute Percentage Error (MAPE) is used as the evaluation criterion in the testing. The results show that using GRNN method on the testing data can improve the accuracy of predictions by reducing the value of MAPE when three other predicting methods: ARIMA, Single Exponential Smoothing, and Moving Average.

关键词： forecasting generalized regression neural network Leave One Out Cross Validation

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generalized regression neural network BASED EFFICIENT MEMRISTOR MODELING 10

GENERALIZED REGRESSION NEURAL NETWORK BASED EFFICIENT MEMRIS...

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10th International Conference on Intelligent Systems and Control (ISCO)

作者： Cam, Zehra Gulru Cimen, Sibel Sedef, Herman Yildiz Tech Univ Dept Elect & Commun Engn Istanbul Turkey

ISBN: (纸本)9781467378079

With the recent advances in memristors as a potential building block for future hardware, it becomes an important and timely topic to study on memristor modelling. Memristor models are important for designers to exhibit memristor behavior since memristor is not yet available in market. An ideal memristor behavior has been remodel with generalized regression neural network (GRNN) and presented in this paper. Mathematical equations are used with a set of given memristor process parameters such as R-ON, R-OFF, thickness of TiO2, and instantaneous memristor behaviour is modelled. The behavior of this model is in agreement with the calculations of HP Lab's and Joglekar's SPICE model.

关键词： memristor modeling generalized regression neural network

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Short-term Load Forecasting of Regional Distribution network Based on generalized regression neural network Optimized by Grey Wolf Optimization Algorithm

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CSEE Journal of Power and Energy Systems 2021年第5期7卷 1093-1101页

作者： Leijiao Ge Yiming Xian Zhongguan Wang Bo Gao Fujian Chi Kuo Sun Key Laboratory of Smart Grid of Ministry of Education Tianjin UniversityTianjin 300072China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of TechnologyTianjin 300132China China Electric Power Research Institute Co.Ltd Beijing 100192China. State Grid Tianjin Electric Power Company Tianjin 300010China.

Short-term load forecasting of regional distribution network is the key to the economic operation of smart distribution systems,which not only requires high accuracy and fast calculation speed,but also has a diversity of influential factors and strong *** paper proposes a short-term load forecasting model for regional distribution network combining the maximum information coefficient,factor analysis,gray wolf optimization,and generalized regression neural network(MIC-FA-GWO-GRNN).To screen and decrease the dimension of the multiple-input features of the short-term load forecasting model,MIC is first used to quantify the non-linear correlation between the load and input features,and to eliminate the ineffective features,and then FA is used to reduce the dimension of the screened input features on the premise of preserving the main information of input *** that the high-precision short-term丨oad forecasting based on GWO-GRNN model is *** is used to regressively analyze the input features after screening and dimension reduction,and the parameter of GRNN is optimized by using the GWO,which has strong global searching ability and fast *** a case study of a regional distribution network in Tianjin,China verifies the accuracy and applicability of the proposed forecasting model.

关键词： Factor analysis generalized regression neural network gray wolf optimization maximum information coefficient short-term load forecasting

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