scholarly journals Structure Analysis and Decoupling Research of a Novel Flexible Tactile Sensor Array

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Feilu Wang ◽  
Yang Song ◽  
Zhenya Zhang ◽  
Wanli Chen
Keyword(s):  
Sensor Array ◽  
Back Propagation ◽  
Three Dimensional ◽  
Structural Complexity ◽  
Tactile Sensor ◽  
Back Propagation Neural Network ◽  
Sensor Model ◽  
Sensor Structure ◽  
Hidden Layer ◽  
Decoupling Algorithm

This paper presents a novel flexible tactile sensor structure and proposes an efficient decoupling algorithm for the tactile sensor. Firstly, structure of the sensor model is introduced, and the sensing mechanism of the sensor array based on force-sensitive conductive rubber is analyzed. Then the mapping relation between the resistances of conductive pillars and the three-dimensional force is deduced. After that, the force applied on the tactile sensor is decoupled from the resistance information by the improved Back Propagation Neural Network (BPNN) algorithm with the number of hidden layer nodes optimized. The flexible tactile sensor model achieves the decomposition of the three-dimensional information from the structure with its unique design, avoids the direct interference between electrodes of the sensor array, reduces the structural complexity and the nonlinear degree, improves the decoupling accuracy, and accelerates the decoupling rate.

Prediction of Yarn Quality Based on BP Neural Network

2011 ◽  
Vol 331 ◽  
pp. 449-453
Author(s):  
Jing Yuan ◽  
Ying Lin Li ◽  
Su Ying Chen
Keyword(s):  
Neural Network ◽  
Mathematical Models ◽  
Network Architecture ◽  
Raw Materials ◽  
Nonlinear Approximation ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Yarn Quality ◽  
Spinning Process ◽  
Hidden Layer

As the quality of yarn and the fiber indicators are nonlinear relationship, the traditional mathematical models or empirical formula has been unable to accurately resolve the problem. In view of artificial neural networks do not need to build accurate mathematical models, applicable to solving the problem of yarn quality prediction. In this paper, good nonlinear approximation ability of BP (Back Propagation) neural network be used, the use of neural network toolbox of MATLAB functions for modeling, good results was obtained. Prediction model set a hidden layer, using three-tier network architecture, and take the input layer 4 nodes, hidden layer 8 nodes and output layer 2 nodes. According to forecast results, can ensure the yarn quality effectively, use of raw materials rationally, to achieve optimal distribution of cotton. Meanwhile, the spinning process design can also be provided validation, for the development of new products to provide a theoretical basis.

Forecasting Stock Price Based on Back Propagation Neural Network

2014 ◽  
Vol 1006-1007 ◽  
pp. 1031-1034
Author(s):  
Li Zhang ◽  
Qing Yang Xu ◽  
Chao Chen ◽  
Zeng Jun Bao
Keyword(s):  
Neural Network ◽  
Stock Price ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Training Method ◽  
Price Forecast ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Hidden Layer ◽  
And Training

The stock market is a nonlinear dynamics system with enormous information, which is difficult to predict effectively by traditional methods. The model of stock price forecast based on BP Neutral-Network is put forward in this article. The paper try to find the way how to predictive the stock price. Exhaustive method is used for the hidden layer neurons and training method determination. Finally the experiment results show that the algorithm get better performance in stock price prediction.

Prediction method for offset compensation on three-dimensional mandrel with spatial irregular shape

2019 ◽  
pp. 152808371985876 ◽  
Author(s):  
Meng Zhuo ◽  
Yao Lingling ◽  
Bu Jianqiu ◽  
Sun Yize
Keyword(s):  
Arbitrary Shape ◽  
Back Propagation ◽  
Three Dimensional ◽  
Prediction Method ◽  
Single Layer ◽  
Back Propagation Neural Network ◽  
Precise Control ◽  
Finite Element Software ◽  
Offset Compensation ◽  
Braiding Angle

In this paper, trajectory control of arbitrary shape mandrel in three-dimensional circular braiding is studied. To obtain accurate trajectory, offset of mandrel is predicted and compensated for trajectory of mandrel. Firstly, the equation of the force of all yarns on three-dimensional mandrel is given. Then offset of mandrel in single layer braiding machine is analyzed via finite element software. Learning these data via back propagation neural network algorithm, offset of mandrel at each moment is derived. The trajectory generation of three-dimensional mandrel based on offset compensation by roll pitch yaw transformation is given. Lastly, braiding angle for the mandrel is analyzed theoretically. In the practical engineering, this method is proven to effectively reduce the error of braiding angle and helpful for the precise control of the trajectory of arbitrary shape mandrel.

Three-dimensional MRI segmentation based on back-propagation neural network with robust supervised training

2000 ◽  
Author(s):  
Jorge U. Garcia ◽  
Leopoldo Gonzalez-Santos ◽  
Rafael Favila ◽  
Rafael Rojas ◽  
Fernando A. Barrios
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Three Dimensional ◽  
Back Propagation Neural Network ◽  
Mri Segmentation ◽  
Supervised Training

Flow Stress Prediction of High-Nb TiAl Alloys under High Temperature Deformation

2012 ◽  
Vol 510 ◽  
pp. 723-728 ◽  
Author(s):  
Liang Cheng ◽  
Hui Chang ◽  
Bin Tang ◽  
Hong Chao Kou ◽  
Jin Shan Li
Keyword(s):  
Neural Network ◽  
High Temperature ◽  
Flow Stress ◽  
Back Propagation ◽  
Three Dimensional ◽  
High Temperature Deformation ◽  
Temperature Deformation ◽  
Ann Model ◽  
The Neural Network ◽  
Hidden Layer

In this work, a back propagation artificial neural network (BP-ANN) model is conducted to predict the flow behaviors of high-Nb TiAl (TNB) alloys during high temperature deformation. The inputs of the neural network are deformation temperature, log strain rate and strain whereas flow stress is the output. There is a single hidden layer with 7 neutrons in the network, and the weights and bias of the network were optimized by Genetic Algorithm (GA). The comparison result suggests a very good correlation between experimental and predicted data. Besides, the non-experimental flow stress predicted by the network is shown to be in good agreement with the results calculated by three dimensional interpolation, which confirmed a good generalization capability of the proposed network.

scholarly journals Identification of the raw and processed Crataegi Fructus based on the electronic nose coupled with chemometric methods

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chenghao Fei ◽  
Chenchen Ren ◽  
Yulin Wang ◽  
Lin Li ◽  
Weidong Li ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Sensor Array ◽  
Prediction Models ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Stepwise Discriminant Analysis ◽  
Accuracy Rate ◽  
Different Types ◽  
Chemical Content ◽  
Electronic Nose System

AbstractCrataegi Fructus (CF) is widely used as a medicinal and edible material around the world. Currently, different types of processed CF products are commonly found in the market. Quality evaluation of them mainly relies on chemical content determination, which is time and money consuming. To rapidly and nondestructively discriminate different types of processed CF products, an electronic nose coupled with chemometrics was developed. The odour detection method of CF was first established by single-factor investigation. Then, the sensor array was optimised by a stepwise discriminant analysis (SDA) and analysis of variance (ANOVA). Based on the best-optimised sensor array, the digital and mode standard were established, realizing the odour quality control of samples. Meanwhile, mathematical prediction models including the discriminant formula and back-propagation neural network (BPNN) model exhibited good evaluation with a high accuracy rate. These results suggest that the developed electronic nose system could be an alternative way for evaluating the odour of different types of processed CF products.

scholarly journals Simulation design of an Intelligent system for Automotive transmission Gearbox Based on FPGA

2018 ◽  
Vol 6 (2) ◽  
pp. 395-411
Author(s):  
Azzad Bader SAEED
Keyword(s):  
Neural Network ◽  
Intelligent System ◽  
Back Propagation ◽  
Activation Function ◽  
Back Propagation Neural Network ◽  
Speed Ratio ◽  
Simulation Design ◽  
Automotive Transmission ◽  
Field Programmable ◽  
Hidden Layer

In this paper, an artificial  intelligent system has been designed, realized, and downloaded into  FPGA (Field Programmable Gate Array), which is used to control five speed ratio steps ( 1,2,3,4,5) of an electrically controlled type of  automotive transmission gearbox of a vehicle, the first speed ratio step (1) is characterized by the  highest torque, a lowest velocity, while, the  fifth step is characterized by the lowest torque, and highest velocity.The Back-propagation neural network has been used as the intelligent system for the proposed system. The proposed neural network is composed from   eight neurons in the input layer, five neurons in the hidden layer, and five neurons in the output layer. For real downloading into the FPGA, Satlins and Satlin linear activation function has been used for the hidden and output layers respectively. The training function Trainlm ( Levenberg-Marqurdt training) has been used as a learning method for the proposed neural network, which it has a powerful algorithm. The proposed simulation system has been designed and downloaded into the FPGA using MATLAB and ISE Design Suit software packages.

scholarly journals Perancangan Model Jaringan Syaraf Tiruan Propagasi Balik Untuk Mengestimasi Curah Hujan

2012 ◽  
Vol 9 (2) ◽  
Author(s):  
Elohansen Padang
Keyword(s):  
Neural Network ◽  
Gradient Descent ◽  
Back Propagation ◽  
Activation Function ◽  
Back Propagation Neural Network ◽  
Rainfall Data ◽  
Training Method ◽  
Output Layer ◽  
Input Layer ◽  
Hidden Layer

This research was conducted to investigate the ability of backpropagation artificial neural network in estimating rainfall. Neural network used consists of input layer, 2 hidden layers and output layer. Input layer consists of 12 neurons that represent each input; first hidden layer consists of 12 neurons with activation function tansig, while the second hidden layer consists of 24 neurons with activation function logsig. Output layer consists of 1 neuron with activation function purelin. Training method used is the method of gradient descent with momentum. Training method used is the method of gradient descent with momentum. Learning rate and momentum parameters defined respectively by 0.1 and 0.5. To evaluate the performance of the network model to recognize patterns of rainfall data is used in Biak city rainfall data from January 1997 - December 2008 (12 years). This data is divided into 2 parts, namely training and testing data using rainfall data from January 1997-December 2005 and data estimation using rainfall data from January 2006-December 2008. From the results of this study concluded that rainfall patterns Biak town can be recognized quite well by the model of back propagation neural network. The test results and estimates of the model results testing the value of R = 0.8119, R estimate = 0.53801, MAPE test = 0.1629, and MAPE estimate = 0.6813.

scholarly journals Layout Optimization of Two Autonomous Underwater Vehicles for Drag Reduction with a Combined CFD and Neural Network Method

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Wenlong Tian ◽  
Zhaoyong Mao ◽  
Fuliang Zhao ◽  
Zhicao Zhao
Keyword(s):  
Neural Network ◽  
Autonomous Underwater Vehicles ◽  
Back Propagation ◽  
Three Dimensional ◽  
Optimization Method ◽  
Back Propagation Neural Network ◽  
Underwater Vehicles ◽  
Minimum Drag ◽  
Computational Fluid Dynamics Cfd ◽  
The Relationship

This paper presents an optimization method for the design of the layout of an autonomous underwater vehicles (AUV) fleet to minimize the drag force. The layout of the AUV fleet is defined by two nondimensional parameters. Firstly, three-dimensional computational fluid dynamics (CFD) simulations are performed on the fleets with different layout parameters and detailed information on the hydrodynamic forces and flow structures around the AUVs is obtained. Then, based on the CFD data, a back-propagation neural network (BPNN) method is used to describe the relationship between the layout parameters and the drag of the fleet. Finally, a genetic algorithm (GA) is chosen to obtain the optimal layout parameters which correspond to the minimum drag. The optimization results show that (1) the total drag of the AUV fleet can be reduced by 12% when the follower AUV is located directly behind the leader AUV and (2) the drag of the follower AUV can be reduced by 66% when it is by the side of the leader AUV.

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