DESIGN, FABRICATION, AND PREDICTIVE MODEL OF A 1-DOF TRANSLATIONAL FLEXIBLE BEARING FOR HIGH PRECISION MECHANISM

2015 ◽  
Vol 39 (3) ◽  
pp. 419-429 ◽  
Author(s):  
Thanh-Phong Dao ◽  
Shyh-Chour Huang
Keyword(s):  
Neural Network ◽  
Stress Concentration ◽  
Linear Regression ◽  
High Precision ◽  
Rbf Neural Network ◽  
Basic Function ◽  
Practical Applications ◽  
Design Variables ◽  
The Relationship ◽  
Predictive Efficiency

Flexible bearing is significantly associated with high precision manipulators, actuators, and positioning stages. In this paper, a flexible bearing is designed for such applications. The life of a flexible bearing is very sensitively influenced by the stress concentration. The Taguchi method is applied to find the best combination of design variables to reduce the stress concentration. Multivariable linear regression (MLR) is established to model the relationship between the design variables and the stress response. In addition, to enhance the predictive efficiency for predicting, a radial basic function (RBF) neural network is used for this relationship. The effectiveness of all models is compared using statistical methods. It is evident that the relationship derived from RBF neural network is more accurate than that derived from MLR models. The confirmation experiments are conducted to verify the predicted results. The combined methodology in this paper is likely be used for various practical applications.

scholarly journals Adaptive Robust Controller Design-Based RBF Neural Network for Aerial Robot Arm Model

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 831
Author(s):  
Izzat Al-Darraji ◽  
Dimitrios Piromalis ◽  
Ayad A. Kakei ◽  
Fazal Qudus Khan ◽  
Milos Stojemnovic ◽  
...  
Keyword(s):  
Neural Network ◽  
Controller Design ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Robot Arm ◽  
Robust Controller ◽  
Practical Applications ◽  
Control Loops ◽  
Modeling Errors ◽  
Aerial Robot

Aerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can handle vary, depending on the nature of the task. The high uncertainties due to modeling errors and an unknown payload are inversely proportional to the stability of ARAs. To address the issue of stability, a new adaptive robust controller, based on the Radial Basis Function (RBF) neural network, is proposed. A three-tier approach is also followed. Firstly, a detailed new model for the ARA is derived using the Lagrange–d'Alembert principle. Secondly, an adaptive robust controller, based on a sliding mode, is designed to manipulate the problem of uncertainties, including modeling errors. Last, a higher stability controller, based on the RBF neural network, is implemented with the adaptive robust controller to stabilize the ARAs, avoiding modeling errors and unknown payload issues. The novelty of the proposed design is that it takes into account high nonlinearities, coupling control loops, high modeling errors, and disturbances due to payloads and environmental conditions. The model was evaluated by the simulation of a case study that includes the two proposed controllers and ARA trajectory tracking. The simulation results show the validation and notability of the presented control algorithm.

scholarly journals Study on process of chemical fiber filament automatic doffing system based on simulation platform and machine learning

2021 ◽  
Vol 16 ◽  
pp. 155892502110548
Author(s):  
Hongxin Zhu ◽  
Kun Zou ◽  
Wenlan Bao
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Production Line ◽  
Simulation Platform ◽  
Chemical Fiber ◽  
Production Lines ◽  
The Neural Network ◽  
Spss Software ◽  
The Relationship

In recent years, a large number of automatic equipment has been introduced into the chemical fiber filament doffing production line, but the related research on the fully automatic production line technology is not yet mature. At present, it is difficult to collect data due to test costs and confidentiality. This paper proposes to develop a simulation platform for a chemical fiber filament doffing production line, which enables us to effectively obtain data and quantitatively study the relationship between the number of manual interventions and other process parameters of the production line. Considering that the parameter research is a multi-factor problem, an orthogonal test was designed by using SPSS software and was carried out by using a simulation platform. The multiple linear regression (MLR) and the neural network optimized by genetic algorithm were adopted to fit the relationship between the number of manual interventions and other parameters of the production line. The SPSS software was applied to obtain the standardized coefficients of the multiple linear regression fitting and the neural network mean impact value (MIV) algorithm was applied to obtain the magnitude and direction of the impact of different parameters on the number of manual interventions. The above results provide important reference for the design of similar new production lines and for the improvement of old production lines.

The Evaluation Model of the Hydropower Project Financing Risk Based on AHP-RS and RBF Neural Network

2011 ◽  
Vol 474-476 ◽  
pp. 2243-2246 ◽  
Author(s):  
Hui Zhao ◽  
Li Ming Chen
Keyword(s):  
Neural Network ◽  
Rough Set Theory ◽  
Rbf Neural Network ◽  
Evaluation Model ◽  
Indicator System ◽  
Basic Function ◽  
Hydropower Project ◽  
Project Financing ◽  
Analytic Hierarchy ◽  
Financing Risk

A evaluation model based on the integration of analytic hierarchy process (AHP)-rough set theory (RS) and radial basic function (RBF) neural network is put forward for grasping the hydropower project financing risk. Firstly, the evaluation indicator system is constructed by AHP, then the evaluation indicators are discretized by RS neural network. And then, RBF neural network is used to evaluate the hydropower project financing risk. In order to grasp this evaluation model better, finally, the paper provides an example to demonstrate the application of this evaluation model.

Learning Transformations with Complex-Valued Neurocomputing

2012 ◽  
Vol 3 (2) ◽  
pp. 81-116
Author(s):  
Tohru Nitta
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Affine Transformations ◽  
Generalization Ability ◽  
Practical Applications ◽  
Optical Flows ◽  
Fractal Images ◽  
Inherent Nature ◽  
The Relationship ◽  
Complex Valued

The ability of the 1-n-1 complex-valued neural network to learn 2D affine transformations has been applied to the estimation of optical flows and the generation of fractal images. The complex-valued neural network has the adaptability and the generalization ability as inherent nature. This is the most different point between the ability of the 1-n-1 complex-valued neural network to learn 2D affine transformations and the standard techniques for 2D affine transformations such as the Fourier descriptor. It is important to clarify the properties of complex-valued neural networks in order to accelerate its practical applications more and more. In this paper, first, the generalization ability of the 1-n-1 complex-valued neural network which has learned complicated rotations on a 2D plane is examined experimentally and analytically. Next, the behavior of the 1-n-1 complex-valued neural network that has learned a transformation on the Steiner circles is demonstrated, and the relationship the values of the complex-valued weights after training and a linear transformation related to the Steiner circles is clarified via computer simulations. Furthermore, the relationship the weight values of the 1-n-1 complex-valued neural network learned 2D affine transformations and the learning patterns used is elucidated. These research results make it possible to solve complicated problems more simply and efficiently with 1-n-1 complex-valued neural networks. As a matter of fact, an application of the 1-n-1 type complex-valued neural network to an associative memory is presented.

Four-channel sEMG for Elbow Load Recognition

2021 ◽  
Author(s):  
Jiabin Cai ◽  
Junjun Song ◽  
Yuanqiang Long
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Wavelet Packet ◽  
Control Mode ◽  
Support Vector ◽  
Semg Signal ◽  
Joint Force ◽  
Emg Signal ◽  
The Relationship ◽  
Rehabilitation Exercise

Abstract In order to help patients after surgery to carry out reasonable rehabilitation training, avoid joint adhesions and movement disorders, the relationship between surface electromyograph (sEMG) signal changes and the size of the patient ' s joint force in the process of rehabilitation exercise was studied, hoping to use the relationship between them to redesign the control mode of the rehabilitation robot, and a method was proposed to identify the size of the elbow load based on wavelet packet. Firstly, s EMG signals of human elbow joint during stretching and bending under different loads were collected by 4-channel surface electromyography. Then, the wavelet packet decomposition method was used to obtain the feature vector composed of energy(E), variance(VAR) and mean absolute value(MAV) of wavelet packet coefficient. Finally, the improved support vector machine ( ISVM), BP neural network and RBF neural network were used for pattern recognition of three different forces. The experimental results show that the change of sEMG signal is indeed related to the size of joint force. It is feasible to identify the load of s EMG signal.

scholarly journals The relationships between gross motor coordination and sport-specific skills in adolescent non-athletes

2017 ◽  
Vol 18 (4) ◽  
Author(s):  
Daniel V Chagas ◽  
John Ozmun ◽  
Luiz Alberto Batista
Keyword(s):  
Linear Regression ◽  
Motor Performance ◽  
Motor Coordination ◽  
Linear Regression Analysis ◽  
Sports Performance ◽  
Regression Analyses ◽  
Young Athletes ◽  
Gross Motor ◽  
Practical Applications ◽  
The Relationship

AbstractPurpose. While the usefulness of gross motor coordination score as predictor of sports performance in young athletes has been demonstrated, practical applications in the settings where the focus is not on elite performance is limited. Further, little is known about the extent to which gross motor coordination score is associated with sport-specific skills among adolescent nonathletes. The aim of this study was to analyse the relationship between the degree of gross motor coordination and execution in specific volleyball tests among adolescent non-athletes. Methods. The total of 34 students (27 females and 7 males) aged 13-14 years who regularly participated in volleyball during physical education classes were randomly recruited. Gross motor coordination was assessed with the Körperkoordinationstest für Kinder. Motor performance on volley-specific skills was indicated by two product-oriented tasks: volleyball under service and service reception. Correlation and linear regression analyses were applied to examine the associations between motor coordination scores and motor performance in volley-specific skills. Results. Motor coordination score was positively correlated with motor performance on specific skills (r = 0.503, p = 0.02). Linear regression analysis revealed that motor coordination score accounted for 23% of the variance in the motor performance on volleyball skills (R

Estimation of Vehicle Pre-Braking Speed

2012 ◽  
Vol 151 ◽  
pp. 165-169
Author(s):  
Wen Kung Tseng ◽  
S. X. Liao
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Rbf Neural Network ◽  
Training Data ◽  
Vehicle Speed ◽  
Training Time ◽  
Factors Affecting ◽  
Single Formula ◽  
The Relationship ◽  
Suitable System

An expert system has been proposed to estimate the relationship between the vehicle pre-braking speed and the length of the skid mark. Since the length of the skid mark varies with many factors, there is no a single formula or equation which can represent the relationship between the vehicle pre-braking speed and the length of the skid mark. Therefore in this paper an expert system is built to estimate the relationship between the vehicle pre-braking speed and the length of the skid mark. The radial basis function (RBF) neural network is used for the expert system due to its shorter training time and higher accuracy. There are many factors affecting the skid mark. In this paper we choose 7 factors, i.e. brand of vehicle, vehicle displacement, year of manufacture, vehicle weight, vehicles with and without ABS, roadway surface, and vehicle speed for the training in the RBF neural network. The total number of the training data for the RBF neural network is 2619. The results showed that high accuracy is obtained for estimating the relationship between the vehicle pre-braking speed and the length of the skid mark. Thus the expert system proposed in this paper is demonstrated to be a suitable system for estimating the relationship between the vehicle pre-braking speed and the length of the skid mark.

scholarly journals UAV adaptive trajectory tracking control under unknown time-varying disturbance and input saturation

2021 ◽  
Author(s):  
Yimin Zhou ◽  
Zengwu Tian
Keyword(s):  
Neural Network ◽  
High Precision ◽  
Trajectory Tracking ◽  
Control Algorithm ◽  
Rbf Neural Network ◽  
Input Saturation ◽  
Dynamic Surface Control ◽  
Time Varying ◽  
Dynamic Surface ◽  
Surface Control

Abstract In this paper, the flight control of the Unmanned aerial vehicle (UAV) is discussed with the proposed adaptive dynamic surface control method owing to its underactuated and non-linear characteristics. The proposed control algorithm is based on radial basis function (RBF) neural network and anti-saturation auxiliary system to realize high-precision trajectory tracking under time-varying disturbances and input saturation. First, the nonlinear dynamic model of the UAV with disturbances is established with the aid of rigid body motion theory. With the adoption of the dynamic surface control algorithm, the error surface and the Lyapunov function are defined to design the preliminary control law of the designed controller. Then the RBF neural network is introduced to estimate and compensate the disturbance. Further, an anti - saturation module is designed to tackle the problem of input saturation. By using the Lyapunov stability theory, it is proved that the stability and signal consistency of the closed-loop system are bounded, along with the constrained conditions of the control parameters. Simulation experiments have been performed and the results demonstrate that the proposed control algorithm has high-precision trajectory tracking ability and strong anti-disturbance capability under the input saturation constraint with high control performance.

Permanent Deformation Process of Asphalt Concrete Pavement Based on Neural Network Modeling

2013 ◽  
Vol 341-342 ◽  
pp. 1486-1490
Author(s):  
Fu Cheng Yin ◽  
Guang Chun Zhou
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Rbf Neural Network ◽  
Permanent Deformation ◽  
Basic Function ◽  
Neural Network Modeling ◽  
Output Data ◽  
Pavement Layers ◽  
Asphalt Concrete Pavement

This paper numerically simulates the deflection response of layers on the cross section of a medium-strength subgrade (MFC) flexible pavement under repeating load, by a radial basic function (RBF) neural network model. The RBF modeling focuses on the functional relationship between the local points in the top deflection curves of pavement layers. The input and output data of the RBF model utilizes the last deflection profiles on the tops of four layers in the test. The deflection curve of the pavement surface is set as the input data since its developing process can been watched and measured in the test. The deflection curves of the other three layers are as the output data, because their deflection process was invisible in the test. Thus, the deflection process of the pavement layers invisible in the test can be simulated by the trained RBF neural network model, which results in a further analysis based on the obtained simulation data.

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