Generalized Dynamic Fuzzy Neural Network Research Articles

Muscle Strength Enhancement and Metabolic Acceleration of College Students in Traditional Archery Programs Based on Functional Training

Abstract At the present stage, the evaluation of human motor function is mainly semi-quantitative, which only evaluates the overall motor function from the degree of joint mobility and muscle status, but the size of muscle strength cannot be obtained by direct measurement. In this paper, we take the muscle activation degree and joint angle of college students in traditional archery programs based on “functional training” as the experimental objects and establish a muscle strength prediction model based on a generalized dynamic fuzzy neural network (GD-FNN). By analyzing the relationship between the surface electromyographic signals and muscle strength under elbow flexion and extension, we selected suitable parameters as the sample data of the fuzzy neural network and proposed a learning algorithm based on the variable sliding window of the GD-FNN. The predicted muscle force was compared with the desired muscle force of the main flexion and extension muscles through the subjects’ elbow flexion exercise and extension exercise. The normalized root-mean-square error between the predicted and actual muscle strength of the algorithm in this paper is less than 0.2. Compared with the maximal strength test, the peak extensor moments (right) and peak extensor moments (left) of the college athletes before and after the functional training, the mean values increased by 30.6 N.m and 42.39 N.m, respectively. Compared with the metabolism of the students during the ordinary training of 2.74 met based on the “functional training” of traditional archery increased, students’ metabolism to 5.03 met. It shows that functional training is favorable to the muscle strength of traditional archery college students and has a positive effect on the metabolic capacity of the body.

Musculoskeletal Rehabilitation Status Monitoring Based on sEMG

The reduction and improper movements in people’s modern life will lead to physical discomfort, pain, and inflammation, which have generally affected the quality of people’s daily life and work efficiency. The pain caused by improper movements are called musculoskeletal pain, which can be relieved or eliminated with treatment. Musculoskeletal disorders are actually one of the most common medical conditions, which affects approximately one quarter of all adults in the world. Although surface electromyography (sEMG) is an acknowledged technology in musculoskeletal rehabilitation study, it is considerably significant to monitor the musculoskeletal rehabilitation status based on sEMG. In order to monitor the musculoskeletal rehabilitation status, we combine fuzzy theory with neural network. This article proposes variable size, sliding window-based, generalized, dynamic, fuzzy neural network (GD-FNN), musculoskeletal rehabilitation status monitoring, that is, the window length of sliding window of sample data changes with the size of sample period. Finally, this study made a simulation on subjects, and the experimental results show that the proposed variable size, sliding window-based GD-FNN, musculoskeletal rehabilitation status monitoring method not only has good monitoring effect but also put on a good performance in root-mean-squared error (RMSE) and mean absolute percentage error (MAPE).

Heading Control of Unmanned Marine Vehicles Based on an Improved Robust Adaptive Fuzzy Neural Network Control Algorithm

A robust adaptive fuzzy neural network control (RAFNNC) algorithm is proposed based on a generalized dynamic fuzzy neural network (GDFNN), proportion-integral-differential (PID), and improved bacterial foraging optimization (BFO) algorithm, for heading the control of the unmanned marine vehicle (UMV) in the presence of a complex environment disturbance. First, the inverse dynamic model of the motion control of UMV is established based on the GDFNN for the uncertain disturbance caused by the complex environment disturbance. Then, the adaptive rate of the fuzzy neural network is designed based on the error between the real UMV heading angle and designed reference heading angle, so as to further adjust the weight parameter of the GDFNN, and then, the output control value of the neural network is obtained. In order to further reduce the computation amount and computation time of the RAFNNC, the parameters of the PID control algorithm were optimized in advance by using the improved BFO algorithm. The fractal dimension step size and the intelligent probe operation are integrated into the BFO algorithm, in order to optimize the operation time and accuracy of the algorithm. Stability of the designed RAFNNC algorithm for the heading control of the UMV in the presence of complex marine environment disturbance is proved by the Lyapunov stability theory, and the effectiveness and accuracy of the control algorithm proposed are verified by semi-physical simulation experiment carried out in our laboratory.

RECONSIDERING THE APPLICATION SCOPE OF FUZZY NEURAL NETWORKS

In this paper, we first examine limitations of fuzzy neural networks. We find the following. (1) If training errors are the main concerns, Spline can perform better than the generalized dynamic fuzzy neural network (GD-FNN). (2) If the model is nonlinear with a disturbance term, the testing error of the GD-FNN is very large. If the model is chaotic with a disturbance term, both the training error and testing error of the GD-FNN are very large. (3) Using a sequential algorithm as in the GD-FNN, we would always be trapped at the local minima rather than the global minimum. In addition, we propose to use the characteristics among moments and fuzzy rules to identify the density function in advance.

Integrating spectral indices with environmental parameters for estimating heavy metal concentrations in rice using a dynamic fuzzy neural-network model

A generalized dynamic fuzzy neural network (GDFNN) was created to estimate heavy metal concentrations in rice by integrating spectral indices and environmental parameters. Hyperspectral data, environmental parameters, and heavy metal content were collected from field experiments with different levels of heavy metal pollution (Cu and Cd). Input variables used in the GDFNN model were derived from 10 variables acquired by gray relational analysis. The assessment models for Cd and Cu concentration employed five and six input variables, respectively. The results showed that the GDFNN for estimating Cu and Cd concentrations in rice performed well at prediction with a compact network structure using the training, validation, and testing sets (for Cu, fuzzy rules=9, R 2 greater than 0.75, and RMSE less than 2.5; for Cd, fuzzy rules=9, R 2 greater than 0.75, and RMSE less than 1.0). The final GDFNN model was then compared with a back-propagation (BP) neural network model, adaptive-network-based fuzzy interference systems (ANFIS), and a regression model. The accuracies of GDFNN model prediction were usually slightly better than those of the other three models. This demonstrates that the GDFNN model is more suitable for predicting heavy metal concentrations in rice.

Identification of nonwoven uniformity using generalized Gaussian density and fuzzy neural network

A joint method to identify nonwoven uniformity by combining wavelet transform, generalized Gaussian density (GGD) and generalized dynamic fuzzy (GDF) neural network is presented in this paper. Six hundred and twenty‐five nonwoven images of five different grades, 125 images of each grade, are decomposed at three different levels with coif4 wavelet base. Wavelet coefficients in each subband are independently modeled by GGD model, while the scale and shape parameters of that are extracted as input features of GDF neural network. For comparison, two energy‐based features are also extracted from wavelet coefficients directly, the number of which is the same as the scale and shape parameters estimated from GGD model with maximum likelihood (ML) estimator. Experimental results on the 625 nonwoven images indicate the GGD model parameters are more expressive and powerful in characterizing textures than the energy‐based ones. The proposed method has high identification accuracy, such as when the images are decomposed at Level 3 and described with GGD model parameters, the identification accuracies of five grades are all 100%. Additionally, to reduce the redundancy of the generated fuzzy rules, an effective complementary approach, fuzzy rule base reduction based on ‘CityBlock’ distance is proposed.