Quantum Gate Elman Neural Network and Its Quantized Extended Gradient Back-propagation Training Algorithm

2014 ◽  
Vol 39 (9) ◽  
pp. 1511-1512 ◽  
Author(s):  
Peng-Hua LI ◽  
Yi CHAI ◽  
Qing-Yu XIONG
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Quantum Gate ◽  
Training Algorithm ◽  
Elman Neural Network

Short Term Electric Load Forecasting with Back Propagation Neural Network and Simulated Annealing Genetic Algorithm

2015 ◽  
Vol 785 ◽  
pp. 14-18 ◽  
Author(s):  
Badar ul Islam ◽  
Zuhairi Baharudin ◽  
Perumal Nallagownden
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Hybrid Approach ◽  
Back Propagation ◽  
Forecast Accuracy ◽  
Back Propagation Neural Network ◽  
Search Performance ◽  
Training Algorithm ◽  
Short Term

Although, Back Propagation Neural Network are frequently implemented to forecast short-term electricity load, however, this training algorithm is criticized for its slow and improper convergence and poor generalization. There is a great need to explore the techniques that can overcome the above mentioned limitations to improve the forecast accuracy. In this paper, an improved BP neural network training algorithm is proposed that hybridizes simulated annealing and genetic algorithm (SA-GA). This hybrid approach leads to the integration of powerful local search capability of simulated annealing and near accurate global search performance of genetic algorithm. The proposed technique has shown better results in terms of load forecast accuracy and faster convergence. ISO New England data for the period of five years is employed to develop a case study that validates the efficacy of the proposed technique.

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%.

Research on Diesel Engine Fault Diagnosis Modeling Based on Elman Neural Network

2011 ◽  
Vol 361-363 ◽  
pp. 1506-1509 ◽  
Author(s):  
Guo Dong Gao ◽  
Wen Xiao Zhang ◽  
Jiang Hua Sui ◽  
Guang Yu Mu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Diesel Engine ◽  
Training Algorithm ◽  
Elman Neural Network ◽  
Diagnosis Model

In order to solve the fault diagnosis problem of diesel engine, Elman neural network (ENN) was applied to build a fault diagnosis model of diesel engine. The training algorithm is in introduced and at the same time, the process of diesel engine fault diagnosis is also expatiated. The diagnosis results indicate the reliability. So a contingent fault of diesel engine can be identified effectively.

RBF-Elman Neural Network Control on Electro-Hydraulic Load Simulator

2013 ◽  
Vol 433-435 ◽  
pp. 1054-1060
Author(s):  
Xiao Hua Wang ◽  
Shuai Wu ◽  
Zong Xiao Jiao
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Control Method ◽  
Back Propagation ◽  
Network Control ◽  
Elman Neural Network ◽  
Simulation Performance ◽  
Neural Network Controller ◽  
Load Simulator ◽  
Load Simulation

Due to load simulation system existing strong disturbance, parameters time-variation and nonlinear, there was low control precision, poor adaptive ability and robustness in traditional control algorithm. In order to improve load simulation performance, The RBF-Elman neural network-based adaptive control method is presented. In this way, the load simulator system is identified by the RBF-Elman neural network identifier, which provides model information (Jacobian matrix) to the PID controller and synchronous compensator in order to make it adaptive. Back-propagation algorithms are used to train neural network. The PID controller which is designed by requirement for steady can overcome the shortcoming of the neural network controller. Finally, the simulations confirm that this control scheme results in a quick response, robustness, and excellent ability against disturbance.

CLASSIFICATION OF sEMG SIGNALS USING INTEGRATED NEURAL NETWORK WITH SMALL SIZED TRAINING DATA

2012 ◽  
Vol 24 (04) ◽  
pp. 365-376 ◽  
Author(s):  
Baofeng Sun ◽  
Wanzhong Chen
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Accurate Method ◽  
Back Propagation Neural Network ◽  
Training Data ◽  
Semg Signal ◽  
Elman Neural Network ◽  
Robotic Devices ◽  
Semg Signals

The sEMG (Surface electromyography) signals detected from activated muscles can be used as a control source for prosthesis. So an efficient and accurate method for the classification of sEMG signal patterns has become a hot research in recent years. Artificial neural network is a popular used method in this field, however, most neural networks require large numbers of samples in the training stage to obtain the potential relationships between input feature vectors and the outputs. In this paper, Integrated back propagation neural network (IBPNN) is used to classify sEMG signals acquired during five different hand motions. The correct classification rates of IBPNN for the five hand movements are significantly higher than that of BPNN and Elman neural network. This reveals that IBPNN achieves the best performance with a small sized training data and can be used in control systems on prosthetic hands and other robotic devices based on electromyography pattern recognition.

Viscosity prediction of hydrocarbon binary mixture using an artificial neural network-group contribution method

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Sara Nanvakenari ◽  
Mitra Ghasemi ◽  
Kamyar Movagharnejad
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Binary Mixture ◽  
Back Propagation ◽  
Training Algorithm ◽  
Group Contribution Method ◽  
Levenberg Marquardt ◽  
Hidden Layer

Abstract In this study, the viscosity of hydrocarbon binary mixtures has been predicted with an artificial neural network and a group contribution method (ANN-GCM) by utilizing various training algorithm including Scaled Conjugate Gradient (SCG), Levenberg-Marquardt (LM), Resilient back Propagation (RP), and Gradient Descent with variable learning rate back propagation (GDX). Moreover, different transfer functions such as Tan-sigmoid (tansig), Log-sigmoid (logsig), and purelin were investigated in hidden and output layer and their effects on network precision were estimated. Accordingly, 796 experimental data points of viscosity of hydrocarbon binary mixture were collected from the literature for a wide range of operating parameters. The temperature, pressure, mole fraction, molecular weight, and structural group of the system were selected as the independent input parameters. The statistical analysis results with R 2 = 0.99 revealed a small value for Average absolute relative deviation (AARD) of 1.288 and Mean square error (MSE) of 0.001018 by comparing the ANN predicted data with experimental data. Neural network configuration was also optimized. Based on the results, the network with one hidden layer and 27 neurons with the Levenberg-Marquardt training algorithm and tansig transfer function for hidden layer along with purelin transfer function for output layer constituted the best network structure. Further, the weights and bias were optimized to minimize the error. Then, the obtained results of the present study were compared with the data from some previous methods. The results suggested that this work can predict the viscosity of hydrocarbon binary mixture with better AARD. In general, the results indicated that combining ANN and GCM model is capable to predict the viscosity of hydrocarbon binary mixtures with a good accuracy.

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.

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