scholarly journals Fruit recognition based on pulse coupled neural network and genetic Elman algorithm application in apple harvesting robot

2020 ◽  
Vol 17 (1) ◽  
pp. 172988141989747 ◽  
Author(s):  
Weikuan Jia ◽  
Shanhao Mou ◽  
Jing Wang ◽  
Xiaoyang Liu ◽  
Yuanjie Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Recognition Rate ◽  
Back Propagation ◽  
Structural Evolution ◽  
Operating Efficiency ◽  
Optimal Method ◽  
Elman Neural Network ◽  
Pulse Coupled Neural Network ◽  
Hidden Neurons ◽  
Harvesting Robot

In order to improve the harvesting efficiency of apple harvesting robot, this article presents an apple recognition method based on pulse coupled neural network and genetic Elman neural network (GA-Elman). Firstly, we use pulse coupled neural network to segment the captured 150 images, respectively, and extract six color features of R, G, B, H, S, and I and 10 shape features of circular variance, density, the ratio of perimeter square to area, and Hu invariant moments of segmented images, and these 16 features are considered as the inputs of Elman neural network. In order to overcome some defects of Elman neural network, such as, trapping local minimum easily and determining the number of hidden neurons difficultly; in this article, genetic algorithm is introduced to optimize it, and new optimization way is designed, that is, the connection weights and number of hidden neurons separate encoding and evolving simultaneously, in the process of structural evolution at the same time the learning of connection weights is completed, and then the operating efficiency and recognition precision of Elman model are improved. In order to get more precision neural network, and avoid the influence of fruit recognition caused by branches or leaves shadow, apple along with branches and leaves is allowed to train. The results of experiments show that compared with the traditional back-propagation, Elman neural network, and other two recognition algorithms of obscured fruit. the genetic Elman neural network algorithm is the optimal method which successful training rate can reach to 100%, recognition rate of overlapping fruit and obscured fruit can reach to 88.67% and 93.64%, respectively, and the total recognition rate reaches to 94.88%.

scholarly journals Recognition Method of Limb Motor Imagery EEG Signals Based on Integrated Back-propagation Neural Network

2015 ◽  
Vol 9 (1) ◽  
pp. 83-91 ◽  
Author(s):  
Mingyang Li ◽  
Wanzhong Chen ◽  
Bingyi Cui ◽  
Yantao Tian
Keyword(s):  
Neural Network ◽  
Motor Imagery ◽  
Hand Movement ◽  
Recognition Rate ◽  
Back Propagation ◽  
Operation Time ◽  
Back Propagation Neural Network ◽  
Classification Problems ◽  
Eeg Signals ◽  
Elman Neural Network

In this paper, in order to solve the existing problems of the low recognition rate and poor real-time performance in limb motor imagery, the integrated back-propagation neural network (IBPNN) was applied to the pattern recognition research of motor imagery EEG signals (imagining left-hand movement, imagining right-hand movement and imagining no movement). According to the motor imagery EEG data categories to be recognized, the IBPNN was designed to consist of 3 single three-layer back-propagation neural networks (BPNN), and every single neural network was dedicated to recognizing one kind of motor imagery. It simplified the complicated classification problems into three mutually independent two-class classifications by the IBPNN. The parallel computing characteristic of IBPNN not only improved the generation ability for network, but also shortened the operation time. The experimental results showed that, while comparing the single BPNN and Elman neural network, IBPNN was more competent in recognizing limb motor imagery EEG signals. Also among these three networks, IBPNN had the least number of iterations, the shortest operation time and the best consistency of actual output and expected output, and had lifted the success recognition rate above 97 percent while other single network is around 93 percent.

Prediction of Surface Roughness in Magneto Rheological Abrasive Flow Finishing Process by Artificial Neural Networks and Regression Analysis

2015 ◽  
Vol 766-767 ◽  
pp. 1076-1084
Author(s):  
S. Kathiresan ◽  
K. Hariharan ◽  
B. Mohan
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Regression Analysis ◽  
Back Propagation ◽  
Hydraulic Pressure ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Magneto Rheological ◽  
Hidden Neurons ◽  
Abrasive Flow Finishing

In this study, to predict the surface roughness of stainless steel-304 in Magneto rheological Abrasive flow finishing (MRAFF) process, an artificial neural network (ANN) and regression models have been developed. In this models, the parameters such as hydraulic pressure, current to the electromagnet and number of cycles were taken as variables of the model.Taguchi’s technique has been used for designing the experiments in order to observe the different values of surface roughness . A neural network with feed forward with the help of back propagation was made up of 27 input neurons, 7 hidden neurons and one output neuron. The 6 sets of experiments were randomly selected from orthogonal array for training and residuals were used to analyze the performance. To check the validity of regression model and to determine the significant parameter affecting the surface roughness, Analysis of variance (ANOVA) andF-test were made. The numerical analysis depict that the current to the electromagnet was an paramount parameter on surface roughness.Key words: MRAFF, ANN, Regression analysis

scholarly journals ON SOME BOUNDS OF THE MINIMUM EDGE DOMINATING ENERGY OF A GRAPH

2021 ◽  
Vol 12 (4) ◽  
pp. 1285-1296
Author(s):  
Dr. Gauri Ghule , Et. al.
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Fuzzy Controller ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Superior Performance ◽  
Fuzzy Classifier ◽  
Manual Intervention ◽  
The Neural Network ◽  
Hidden Neurons

Number of hidden neurons is necessary constant for tuning the neural network to achieve superior performance. These parameters are set manually through experimentation. The performance of the network is evaluated repeatedly to choose the best input parameters.Random selection of hidden neurons may cause underfitting or overfitting of the network. We propose a novel fuzzy controller for finding the optimal value of hidden neurons automatically. The hybrid classifier helps to design competent neural network architecture, eliminating manual intervention for setting the input parameters. The effectiveness of tuning the number of hidden neurons automatically on the convergence of a back-propagation neural network, is verified on speech data. The experimental outcomes demonstrate that the proposed Neuro-Fuzzy classifier can be viably utilized for speech recognition with maximum classification accuracy.

scholarly journals Numerical Analysis of Modeling Based on Improved Elman Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Shao Jie ◽  
Wang Li ◽  
Zhao WeiSong ◽  
Zhong YaQin ◽  
Reza Malekian
Keyword(s):  
Neural Network ◽  
Memory Effect ◽  
Iteration Step ◽  
Elman Neural Network ◽  
Chebyshev Neural Network ◽  
Class D ◽  
Proposed Model ◽  
Tone Signal ◽  
Hidden Layer ◽  
Hidden Neurons

A modeling based on the improved Elman neural network (IENN) is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE) varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power amplifier (CDPA) with two-tone signal and broadband signals as input have shown that the proposed behavioral modeling can reconstruct the system of CDPAs accurately and depict the memory effect of CDPAs well. Compared with Volterra-Laguerre (VL) model, Chebyshev neural network (CNN) model, and basic Elman neural network (BENN) model, the proposed model has better performance.

scholarly journals Improving activated sludge classification based on imbalanced data

2014 ◽  
Vol 16 (6) ◽  
pp. 1331-1342 ◽  
Author(s):  
Y. Qian ◽  
Y. C. Liang ◽  
R. C. Guan
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Activated Sludge ◽  
Recognition Rate ◽  
Back Propagation ◽  
Area Under The Curve ◽  
Imbalanced Data ◽  
Real Data ◽  
Support Vector ◽  
Classification Problems

A fast and accurate classification method for sewage sludge biological activity classification is of great significance for wastewater treatment. However, the data are often imbalanced and the accuracy of traditional classification algorithms applied to imbalanced small classes of data is very low. Such small classes are crucial application data. Therefore, based on the analysis of eight microorganisms, a novel method is proposed in this paper for the classification of activated sludge known as balanced support-vector-based back-propagation (SV-BP) neural network. It first splits the multiclass classification problem into a plurality of pairwise classification problems and uses a support vector machine (SVM) to achieve equalization. Second, the new dataset is produced, following which back-propagation neural network (BPNN) is used for training and classification. To examine the efficiency of the model, 1731 real data points are collected from a wastewater treatment factory and divide the data into four classes with the help of wastewater experts. Based on the new model, data redundancy and noise are greatly reduced. With area under the curve (AUC) measurements, we find that the AUC of SV-BP is 6.9% higher than classical BPNN. In addition, the small-class recognition rate of SV-BP is far better than that by classical BPNN and SVM algorithms.

scholarly journals UNSTABLE MORSE CODE RECOGNITION SYSTEM WITH EXPERT-GATING NEURAL NETWORK

2002 ◽  
Vol 14 (01) ◽  
pp. 12-19 ◽  
Author(s):  
DUU-TONG FUH ◽  
CHING-HSING LUO
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recognition Rate ◽  
Back Propagation ◽  
Recognition System ◽  
Disabled Persons ◽  
Single Chip ◽  
Morse Code ◽  
Average Recognition Rate ◽  
Rate Test

The standard Morse code defines the tone ratio (dash/dot) and the silent ratio (dash-space/dotspace) as 3:1. Since human typing ratio can't keep this ratio precisely and the two ratios —tone ratio and silent ratio—are not equal, the Morse code can't be recognized automatically. The requirement of the standard ratio is difficult to satisfy even for an ordinary person. As for the unstable Morse code typing pattern, the auto-recognition algorithms in the literature are not good enough in applications. The disabled persons usually have difficulty in maintaining a stable typing speeds and typing ratios, we therefore adopted an Expert-Gating neural network model to implement in single chip and recognize online unstable Morse codes. Also, we used another method—a linear back propagation recalling algorithm, to implement in single chip and recognize unstable Morse codes. From three person tests: Test one is a cerebral palsy; Test two is a beginner: Test three is a skilled expert, we have the results: in the experiment of test one, we have 91.15% (use 6 characters average time series as thresholds) and 91.54% (learning 26 characters) online average recognition rate; test two have 95.77% and 96.15%, and test three have 98.46% and 99.23% respectively. As for linear back propagation recalling method online recognized rate, we have the results from test one: 92.31% online average recognition rate; test two: 96.15%; and test three 99.23% respectively. So, we concluded: The Expert-Gating neural network and the linear back propagation recalling algorithm have successfully overcome the difficulty of analyzing a severely online unstable Morse code time series and successfully implement in single chip to recognize online unstable Morse code.

Prediction of collapsibility of moulds and cores of CO2 sands using a neural network

2003 ◽  
Vol 217 (4) ◽  
pp. 475-487 ◽  
Author(s):  
R K Ohdar ◽  
P T Pushp
Keyword(s):  
Neural Network ◽  
Test Data ◽  
Process Parameters ◽  
Coal Dust ◽  
Back Propagation ◽  
Maximum Error ◽  
Ann Model ◽  
Input Process ◽  
Artificial Neural Network Ann ◽  
Hidden Neurons

The CO2 process of making sand moulds and cores is a well-established process and suitable for all types of foundry. However, the collapsibility of CO2 sand is quite poor. A variety of additives are used to improve collapsibility of CO2 sands. Several other process parameters also affect collapsibility of CO2 sands. In the present investigation an attempt has been made to use an artificial neural network (ANN) model for prediction of the collapsibility of CO2 sand. Experiments were conducted with various input process parameters, such as binder content, gassing time, temperature and additive content using three different additives, namely coal dust, dextrin and alumina. The objective of the experiments was to generate basic data to train a back-propagation ANN model and finally predict collapsibility in terms of retained compressive strength of CO2 sands for the test data. A three-layer neural network model with six input neurons corresponding to six input process parameters, one output neuron corresponding to collapsibility and 19 hidden neurons has been suggested, which gives a maximum error of 2 per cent in prediction of test data. Results indicate that prediction of the collapsibility of CO2 sand with an ANN model is feasible. Predicted values match experimental values quite closely.

Research on Classification of Gait Signal Based on Neural Network

2013 ◽  
Vol 303-306 ◽  
pp. 1081-1084
Author(s):  
Jing Yin
Keyword(s):  
Neural Network ◽  
Neural Network Model ◽  
Recognition Rate ◽  
Back Propagation ◽  
Healthy People ◽  
Signal Recognition ◽  
Characteristic Parameters ◽  
Error Back Propagation ◽  
The Neural Network

To effectively recognize gait signal between healthy people and patients with Parkinson, a gait signal recognition model is established based on neural network of error back propagation (EBP), and a method is proposed to effectively extract characteristic parameters. In this paper, coefficient of variation is applied in the research of gait-pressure multi-characteristic parameters through gait-pressure signal, and the neural network model can automatically recognize gait-pressure characteristics between healthy people and patients with Parkinson. This can contribute to the recognition and diagnosis of patients with Parkinson. Experiment results show a recognition rate of 90%.

Sign in / Sign up

Export Citation Format

Share Document