scholarly journals Modeling FOG Drift Using Back-Propagation Neural Network Optimized by Artificial Fish Swarm Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Rui Song ◽  
Xiyuan Chen ◽  
Chong Shen ◽  
Hong Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Fiber Optic Gyroscope ◽  
Temperature Drift ◽  
Error Sources ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

Based on the temperature drift characteristic of fiber optic gyroscope (FOG), a novel modeling and compensation method which integrated the artificial fish swarm algorithm (AFSA) and back-propagation (BP) neural network is proposed to improve the output accuracy of FOG and the precision of inertial navigation system. In this paper, AFSA is used to optimize the weights and threshold of BP neural network which determine precision of the model directly. In order to verify the effectiveness of the proposed algorithm, the predicted results of BP optimized by genetic algorithm (GA) and AFSA are compared and a quantitative evaluation of compensation results is analyzed by Allan variance. The comparison result illustrated the main error sources and the sinusoidal noises in the FOG output signal are reduced by about 50%. Therefore, the proposed modeling method can be used to improve the FOG precision.

scholarly journals Parameter Estimation for PMSM based on a Back Propagation Neural Network Optimized by Chaotic Artificial Fish Swarm Algorithm

2019 ◽  
Vol 14 (6) ◽  
pp. 615-632
Author(s):  
Jianwu Jiang ◽  
Zhi Chen ◽  
Yihuai Wang ◽  
Tao Peng ◽  
Shilang Zhu ◽  
...  
Keyword(s):  
Neural Network ◽  
Parameter Identification ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
System Stability ◽  
Strong Nonlinearity ◽  
Artificial Fish Swarm Algorithm ◽  
Motor Parameter ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

Permanent Magnet Synchronous Motor(PMSM) control system with strong nonlinearity makes it difficult to accurately identify motor parameters such as stator winding, dq axis inductance, and rotor flux linkage. Aiming at the premature convergence of traditional Back Propagation Neural Network(BPNN) in PMSM motor parameter identification, a new method of PMSM motor parameter identification is proposed. It uses Chaotic Artificial Fish Swarm Algorithm(CAFSA) to optimize the initial weights and thresholds of BPNN, and then strengthens training by BPNN algorithm. Thus, the global optimal network parameters are obtained by using the global optimization of CAFSA and the local search ability of BPNN. The simulation results and experimental data show that the initial value sensitivity of the network model optimized by CAFS-BPNN Algorithm is weak, the parameter setting is robust, and the system stability is good under complex conditions. Compared with other intelligent algorithms, such as RSL and PSO, CAFS-BPNNA has high identification accuracy and fast convergence speed for PMSM motor parameters.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

Weight Loss Prediction Model for Pig Carcass Based on a Genetic Algorithm Back- Propagation Neural Network

2020 ◽  
Vol 63 (4) ◽  
pp. 1071-1077
Author(s):  
Chenyang Sun ◽  
Lusheng Chen ◽  
Yinian Li ◽  
Hao Yao ◽  
Nan Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Weight Loss ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Time Interval ◽  
Spraying Parameters ◽  
Pig Carcasses

HighlightsWe propose five spraying parameters according to the characteristics of pig carcasses in the spray-chilling process.A prediction model for pig carcass weight loss, based on a genetic algorithm back-propagation neural network, is proposed to reveal the relationship between weight loss and spraying parameters.To study the effects of various spraying parameters on weight loss, an automatic spray-chilling device was designed, which can modify up to five spraying parameters.Abstract. Because the weight loss of a pig carcass in the spray-chilling process is easily affected by the spraying frequency and duration, a prediction model for weight loss based on a genetic algorithm (GA) back-propagation (BP) neural network is proposed in this article. With three-way crossbred pig carcasses selected as the test materials, the duration and time interval of high-frequency spraying, the duration and time interval of low-frequency spraying, and the duration of a single spray were selected as inputs to the network model. The weight and threshold of the network were then optimized by the GA. The prediction model for pig carcass weight loss established by the GA BP neural network yielded a correlation coefficient of R = 0.99747 between the network output value of the test samples and the target value. Weight loss prediction by the model is feasible and allows better expression of the nonlinear relationship between weight loss and the main controlling factors. The results can be a reference for chilled meat production. Keywords: BP neural network, Genetic algorithm, Pig carcass, Predictive model, Weight loss

Selection and Prediction of Earthquake Casualties Related Factors Based on BP Neural Network Analysis

2014 ◽  
Vol 543-547 ◽  
pp. 2084-2088 ◽  
Author(s):  
Run Biao Bao ◽  
Man Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Prediction Error ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Related Factors ◽  
Value Model ◽  
Correlation Factors ◽  
Relevant Factors ◽  
Earthquake Casualties

To reduce the prediction error rate of earthquake casualties, the paper proposed a prediction model with two steps: (1) screening of the earthquake casualties correlation factors; (2) improving the predictive veracity of general BP(Back Propagation) neural network model.By the analysis of 9 kinds of correlation factors, the paper established the MIV(Mean Impact Value) model based on BP neural network to screen the final correlation factors, and the paper got 6 main correlation factors according to the size of output weights of the factors. Finally, the paper verified the accuracy and practicability of the model through the validation of the model and the solving of prediction error of relevant factors hasn't been selected.

Research on Nonlinear Approximation Model of Radial Basis Function Neural Network Trained Using Artificial Fish Swarm Algorithm with Adaptive Adjustment

2015 ◽  
Vol 713-715 ◽  
pp. 1855-1858 ◽  
Author(s):  
Xu Sheng Gan ◽  
Xue Qin Tang ◽  
Hai Long Gao
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Nonlinear Approximation ◽  
Nonlinear Function ◽  
Training Algorithm ◽  
Step Size ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Adaptive Adjustment ◽  
Swarm Algorithm

In order to improve the modeling efficiency of RBF neural network, an Artificial Fish Swarm Algorithm (AFSA) training algorithm with an adaptive mechanism is proposed. In the training algorithm, the search step size and visible domain of AFSA algorithm can be adjusted dynamically according to the convergence characteristics of artificial fish swarm, and then the improved AFSA algorithm is used to optimize the parameters of RBF neural network. The example shows that, the proposed model is a better approximation performance for the nonlinear function.

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.

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