Robot Grasp Pattern Recognition Based on Wavlet and BP Neural Network

2013 ◽  
Vol 331 ◽  
pp. 290-293 ◽  
Author(s):  
Jin Jun Chen ◽  
Ting Xiang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Tactile Sensor ◽  
Robot Hand ◽  
Logarithmic Function ◽  
Tangent Function ◽  
Spectrum Feature ◽  
Neural Network Structure ◽  
Hidden Layer ◽  
Proper Force

Tactile sensation is one of essential perceptions for a functional robot hand to monitor slip states, grasp objects with proper force, and distinguish different properties of objects etc. A practical tactile sensor based on acoustic-electric converting principle is introduced. The grasp signals of objects of three sort materials are collected by the tactile sensor. The power spectrum feature vectors of them are taken as learning sample book set. Transfer function of neurons in hidden layer is tangent function and that in output layer is logarithmic function. L-M algorithm is selected and convergence precision set is as 0.0001. The hidden layer nodes are taken by experiments as 13. When neural network structure is 8-13-4, BP neural network has the fastest convergence rate and short running time of milliseconds.

scholarly journals Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1094 ◽  
Author(s):  
Lanjun Wan ◽  
Hongyang Li ◽  
Yiwei Chen ◽  
Changyun Li
Keyword(s):  
Neural Network ◽  
Working Conditions ◽  
Bp Neural Network ◽  
Evidence Theory ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Fault Prediction ◽  
Fault Classification ◽  
Bearing Fault ◽  
Hidden Layer

To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster–Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster–Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.

Building Electronic Commerce Recommendation System Based on Ontology Learning and BP Neural Network

2013 ◽  
Vol 718-720 ◽  
pp. 1961-1966
Author(s):  
Hong Sheng Xu ◽  
Qing Tan
Keyword(s):  
Neural Network ◽  
Electronic Commerce ◽  
Bp Neural Network ◽  
Optimization Problem ◽  
Recommendation System ◽  
High Efficiency ◽  
Ontology Learning ◽  
The Neural Network ◽  
Network Process ◽  
Hidden Layer

Electronic commerce recommendation system can effectively retain user, prevent users from erosion, and improve e-commerce system sales. BP neural network using iterative operation, solving the weights of the neural network and close values to corresponding network process of learning and memory, to join the hidden layer nodes of the optimization problem of adjustable parameters increase. Ontology learning is the use of machine learning and statistical techniques, with automatic or semi-automatic way, from the existing data resources and obtaining desired body. The paper presents building electronic commerce recommendation system based on ontology learning and BP neural network. Experimental results show that the proposed algorithm has high efficiency.

Precision Model of Predicting FDM Rapid Prototype Based on BP Neural Network

2008 ◽  
Vol 392-394 ◽  
pp. 891-897
Author(s):  
G.Q. Shang ◽  
C.H. Sun ◽  
X.F. Chen ◽  
J.H. Du
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Fused Deposition Modeling ◽  
Rapid Prototype ◽  
Fused Deposition ◽  
Novel Approach ◽  
Deposition Modeling ◽  
Hidden Layer ◽  
Parts Manufacturing ◽  
Bp Neural Network Model

Fused deposition modeling (FDM) has been widely applied in complex parts manufacturing and rapid tooling and so on. The precision of prototype was affected by many factors during FDM, so it is difficult to depict the process using a precise mathematical model. A novel approach for establishing a BP neural network model to predict FDM prototype precision was proposed in this paper. Firstly, based on analyzing effect of each factor on prototyping precision, some key parameters were confirmed to be feature parameters of BP neural networks. Then, the dimensional numbers of input layer and middle hidden layer were confirmed according to practical conditions, and therefore the model structure was fixed. Finally, the structure was trained by a great lot of experimental data, a model of BP neural network to predict precision of FDM prototype was constituted. The results show that the error can be controlled within 10%, which possesses excellent capability of predicting precision.

Prediction Model of Flow-Induced Noise in Large-Scale Centrifugal Pumps Based on BP Neural Network

2017 ◽  
Author(s):  
Chang Guo ◽  
Ming Gao ◽  
Peixin Dong ◽  
Yuetao Shi ◽  
Fengzhong Sun
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Working Conditions ◽  
Bp Neural Network ◽  
Centrifugal Pumps ◽  
Output Layer ◽  
Sample Data ◽  
Input Layer ◽  
Flow Induced Noise ◽  
Hidden Layer

As one kind of serious environmental problems, flow-induced noise in centrifugal pumps pollutes the working circumstance and deteriorates the performance of pumps, meanwhile, it always changes drastically under various working conditions. Consequently, it is extremely significant to predict flow-induced noise of centrifugal pumps under various working conditions with a practical mathematical model. In this paper, a three-layer back propagation (BP) neural network model is established and the number of input, hidden and output layer node is set as 3, 6 and 1, respectively. To be specific, the flow rate, rotational speed and medium temperature are chosen as input layer, and the corresponding flow-induced noise evaluated by average of total sound pressure level (A_TSPL) as output layer. Furthermore, the tansig function is used to act as transfer function between the input layer and hidden layer, and the purelin function is used between hidden layer and output layer. The trainlm function based on Levenberg-Marquardt algorithm is selected as the training function. By using a large number of sample data, the training of the network model and prediction research are accomplished. The results indicate that good correlation is established among the sample data, and the predictive values show great consistence with simulation ones, of which the average relative error of A_TSPL in process of verification is 0.52%. The precision of the model can satisfy the requirement of relevant research and engineering application.

Sunlight Greenhouse Temperature Prediction Model Based on Bayesian Regularization BP Neural Network

2015 ◽  
Vol 740 ◽  
pp. 871-874
Author(s):  
Hui Zhao ◽  
Li Rong Shi ◽  
Hong Jun Wang
Keyword(s):  
Neural Network ◽  
Network Structure ◽  
Bp Neural Network ◽  
Principal Component ◽  
Coupling Factor ◽  
Bayesian Regularization ◽  
The Neural Network ◽  
Neural Network Structure ◽  
Input Variables ◽  
Bp Neural Network Model

Directing against the problems of too large size of the neural network structure due to the existence of a complex relationship between the input coupling factor and too many input factors in establishing model for predicting temperature of sunlight greenhouse. This article chose the environmental factors that affect the sunlight greenhouse temperature as data sample. Through the principal component analysis of data samples, three main factors were extracted. These selected principal component values were taken as the input variables of BP neural network model. Use the Bayesian regularization algorithm to improve the BP neural network. The empirical results show that this method is utilized modify BP neural network, which can simplify network structure and smooth fitting curve, has good generalization capability.

scholarly journals Development of Customer Relationship Management System in E-Commerce Based on BP Neural Network

2012 ◽  
Vol 6-7 ◽  
pp. 995-999
Author(s):  
Mei Ling Zhou ◽  
Jing Jing Hao
Keyword(s):  
Neural Network ◽  
Customer Relationship Management ◽  
Bp Neural Network ◽  
Management System ◽  
Customer Value ◽  
Relationship Management ◽  
Customer Relationship ◽  
Feed Forward Network ◽  
Neural Network Algorithm ◽  
Hidden Layer

BP neural network can learn and store a lot of input - output mode mapping, without prior reveal the mathematical equations describe the mapping. The model based on BP neural network algorithm is constituted by an input layer, output layer and one hidden layer, three-layer feed forward network. CRM is to acquire, maintain and increase the methods and processes of profitable customers. The core of CRM is the customer value management, customer value; it is divided into the de facto value, potential value and model value. The paper presents development of customer relationship management system in e-commerce based on BP neural network. The experiment shows BP is superior to RFCA in CRM.

The Use of BP Neural Network in Evaluation about the Appearance of the Garments

2012 ◽  
Vol 433-440 ◽  
pp. 4320-4323 ◽  
Author(s):  
Jing Wang ◽  
Jin Ying Song ◽  
Ai Qing Tang
Keyword(s):  
Neural Network ◽  
Principal Components ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Original Data ◽  
Back Propagation Algorithm ◽  
Bp Network ◽  
Self Adaptation ◽  
Hidden Layer ◽  
Good Agreement

This article reports the use of BP neural network for evaluation of the appearance of garment after dry wash. The selected data about parameters of fabrics and interlinings are analyzed by principal analysis and eight principal components are obtained through this method. A BP neural network with a single hidden layer is constructed including eight input nodes, six hidden nodes and one output nodes. During training the network with a back-propagation algorithm, the eight principal components are used as input parameters, while the rate of the appearance of the garment are used as output parameters. The weight values are modified with momentum and learning rate self-adaptation to solve the two defects of the BP network. All original data are preprocessed and the learning process is successful in achieving a global error minimum. The rate of the appearance can be evaluated with this training network and there is a good agreement between the evaluated and tested values.

Study on the Grasping Ability and Optimization of Multi-Fingered Robot Hands under Different Postures

2014 ◽  
Vol 565 ◽  
pp. 247-252
Author(s):  
Hai Jun Mo ◽  
Jia Jun Zhou ◽  
Hua Rong Qiu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Joint Angle ◽  
Screw Theory ◽  
Robot Hand ◽  
Robot Hands ◽  
Grasp Force

A multi-fingered hand has been used in the explosive Disposal Robot to improve the disposal ability of explosive. Grasping ability of the multi-fingered hand is a problem with the change of grasping posture. This paper discusses grasping ability of the multi-fingered robot hand. Screw theory and BP neural network are used to optimize the joint angle of the finger. The most favorite grasping posture is calculated when the multi-fingered robot hand can withstand the largest external wrench. In order to guarantee the explosive not to be exploded under the exceeding grasp force, the weight of the explosive the multi-fingered hand can hold is also discussed in this paper. It is an important theoretical guidance for the multi-fingered robot hand handling of hazardous items.

RECOGNITION OF CONTACT STATE BY USING NEURAL NETWORK FOR MICROMACHINED ARRAY TYPE TACTILE SENSOR

2005 ◽  
Vol 02 (03) ◽  
pp. 181-190 ◽  
Author(s):  
SEIJI AOYAGI ◽  
TAKAAKI TANAKA ◽  
KENJI MAKIHIRA
Keyword(s):  
Neural Network ◽  
Rotation Angle ◽  
Simulated Data ◽  
Tactile Sensor ◽  
Training Data ◽  
Sensing Element ◽  
Array Type ◽  
Tactile Pattern ◽  
Hidden Layer ◽  
Contact Position

In this paper, a force sensing element having a pillar and a diaphragm is proposed and thereafter fabricated by micromachining. Piezo resistors are fabricated on a silicon diaphragm for detecting distortions caused by a force input to a pillar on the diaphragm. Since a practical arrayed sensor consisting of many of this element is still under development, the output of an assumed arrayed type tactile sensor is simulated by FEM (finite element method). Using simulated data, the possibility of tactile pattern recognition using a neural network (NN) is investigated. The learning method of NN, the number of units of the input layer and the hidden layer, as well as the number of training data are investigated for realizing high probability of recognition. The 14 subjects having different shape and size are recognized. This recognition succeeded even if the contact position and the rotation angle of these objects are changed.

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