scholarly journals A Modified Conjugate Gradient Formula for Back Propagation Neural Network Algorithm

2009 ◽  
Vol 5 (11) ◽  
pp. 849-856 ◽  
Author(s):  
Abbas Y. Al Bayati ◽  
Najmaddin A. Sulaiman ◽  
Gulnar W. Sadiq
Keyword(s):  
Neural Network ◽  
Conjugate Gradient ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Network Algorithm ◽  
Neural Network Algorithm

Research and Realization of Hardware Back-Propagation Neural Network Based on FPGA

2013 ◽  
Vol 333-335 ◽  
pp. 2469-2474
Author(s):  
Fei Guo ◽  
Xiao Luo
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Variable Structure ◽  
Maximum Error ◽  
Back Propagation Neural Network ◽  
Neural Network Modeling ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Timing Simulation

In order to meet the requirements of real-time and embedded of industrial field, a reconfigurable Back-Propagation neural network based on FPGA has been implemented on Xilinx's Spartan-3E (XC3S250E) chip which has 250000 gate. First the optimal network structure and weights were gotten by a variable structure of BP neural network algorithm. Then an improved hardware approaching method of excitation function was put forward, and the maximum error was 1.58% by simulation and comparative analysis on the error. Finally hardware co-imitation and timing simulation was token based on a reasonable choice of data accuracy, and then the hardware BP neural network algorithm was been downloaded and implemented on FPGA. This method has better accuracy and speed, it is an effective method of BP neural network modeling based on hardware, and lays the foundation for the hardware realization of other neural network and embedded image processing.

scholarly journals Porosity Prediction of Granular Materials through Discrete Element Method and Back Propagation Neural Network Algorithm

2020 ◽  
Vol 10 (5) ◽  
pp. 1693
Author(s):  
Yu Liu ◽  
Miaomiao Li ◽  
Peifeng Su ◽  
Biao Ma ◽  
Zhanping You
Keyword(s):  
Neural Network ◽  
Granular Material ◽  
Granular Materials ◽  
Particle Shape ◽  
Back Propagation ◽  
Discrete Element ◽  
Back Propagation Neural Network ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Bpnn Model

Granular materials are used directly or as the primary ingredients of the mixtures in industrial manufacturing, agricultural production and civil engineering. It has been a challenging task to compute the porosity of a granular material which contains a wide range of particle sizes or shapes. Against this background, this paper presents a newly developed method for the porosity prediction of granular materials through Discrete Element Modeling (DEM) and the Back Propagation Neural Network algorithm (BPNN). In DEM, ball elements were used to simulate particles in granular materials. According to the Chinese specifications, a total of 400 specimens in different gradations were built and compacted under the static pressure of 600 kPa. The porosity values of those specimens were recorded and applied to train the BPNN model. The primary parameters of the BPNN model were recommended for predicting the porosity of a granular material. Verification was performed by a self-designed experimental test and it was found that the prediction accuracy could reach 98%. Meanwhile, considering the influence of particle shape, a shape reduction factor was proposed to achieve the porosity reduction from sphere to real particle shape.

Hysteresis modeling of piezoelectric actuator using particle swarm optimization-based neural network

2020 ◽  
Vol 234 (23) ◽  
pp. 4695-4707
Author(s):  
Quan Zhang ◽  
Xin Shen ◽  
Jianguo Zhao ◽  
Qing Xiao ◽  
Jun Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Moving Average ◽  
Back Propagation ◽  
Particle Swarm ◽  
Piezoelectric Actuators ◽  
Back Propagation Neural Network ◽  
Swarm Optimization ◽  
Network Algorithm ◽  
Neural Network Algorithm

Piezoelectric actuators have been received much attention for the advantages of high precision, no wear and rapid response, etc. However, the intrinsic hysteresis behavior of the piezoelectric materials seriously degraded the output performance of piezoelectric actuators. In this paper, to decrease such nonlinear effects and further improve the output performances of piezoelectric actuators, a modified nonlinear autoregressive moving average with exogenous inputs model, which could describe the rate-dependent hysteresis features of piezoelectric actuators was investigated. In the experiment, the different topologies of the proposed back propagation neural network algorithm were compared and the optimal topology was selected considering both the tracking precision and the structure complexity. The experimental results validated that the modified nonlinear autoregressive moving average with exogenous inputs model featured the hysteresis characteristics description ability with high precision, and the predicted motion matched well with the real trajectory. Then, the initial parameters of the back propagation neural network algorithm were further optimized by particle swarm optimization algorithm. The experimental results also verified that the proposed model based on particle swarm optimization–back propagation neural network algorithm was more accurate than that identified through the conventional back propagation neural network algorithm, and has a better predicting performance.

scholarly journals A study of waterborne diseases during flooding using Radarsat-2 imagery and a back propagation neural network algorithm

2013 ◽  
Vol 6 (4) ◽  
pp. 289-307 ◽  
Author(s):  
Peera Yomwan ◽  
Chunxiang Cao ◽  
Preesan Rakwatin ◽  
Warawut Suphamitmongkol ◽  
Rong Tian ◽  
...  
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Waterborne Diseases ◽  
Network Algorithm ◽  
Neural Network Algorithm

A New Back-Propagation Neural Network Algorithm for a Big Data Environment Based on Punishing Characterized Active Learning Strategy

2013 ◽  
Vol 4 (4) ◽  
pp. 32-45 ◽  
Author(s):  
Qiuhong Zhao ◽  
Feng Ye ◽  
Shouyang Wang
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Active Learning ◽  
Learning Strategy ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Bp Algorithm ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Active Learning Strategy

This paper introduces the active learning strategy to the classical back-propagation neural network algorithm and proposes punishing-characterized active learning Back-Propagation (BP) Algorithm (PCAL-BP) to adapt to big data conditions. The PCAL-BP algorithm selects samples and punishments based on the absolute value of the prediction error to improve the efficiency of learning complex data. This approach involves reducing learning time and provides high precision. Numerical analysis shows that the PCAL-BP algorithm is superior to the classical BP neural network algorithm in both learning efficiency and precision. This advantage is more prominent in the case of extensive sample data. In addition, the PCAL-BP algorithm is compared with 16 types of classical classification algorithms. It performs better than 14 types of algorithms in the classification experiment used here. The experimental results also indicate that the prediction accuracy of the PCAL-BP algorithm can continue to increase with an increase in sample size.

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