A time simulated annealing-back propagation algorithm and its application in disease prediction

2018 ◽  
Vol 32 (25) ◽  
pp. 1850303 ◽  
Author(s):  
Fang Hu ◽  
Mingzhu Wang ◽  
Yanhui Zhu ◽  
Jia Liu ◽  
Yalin Jia
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Bp Algorithm ◽  
Disease Prediction ◽  
Back Propagation Algorithm ◽  
Network Algorithm ◽  
Learning Rates ◽  
Neural Network Algorithm

In this paper, based on the Back Propagation (BP) neural network algorithm, we introduce the idea of the Simulated Annealing (SA), and then propose a new neural network algorithm: Time Simulated Annealing-Back Propagation (TSA-BP) algorithm. The proposed algorithm can improve the convergence rate and numerical stability. By using this proposed algorithm, the learning rates and initial weights in the BP neural network could be easily adjusted. We show that the TSA-BP algorithm could reduce the errors caused by human-made factors. Several numerical experiments have been tested by using different disease data. Furthermore, we compared the TSA-BP algorithm to the other existing, well-known algorithms. Numerical results show higher accuracy and efficiency of the TSA-BP algorithm.

An Improved BP Neural Network Algorithm and its Application

2014 ◽  
Vol 543-547 ◽  
pp. 2120-2123 ◽  
Author(s):  
Ming Jun Chen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Ant Colony Algorithm ◽  
Back Propagation ◽  
Indicator System ◽  
Global Optimum ◽  
Bp Algorithm ◽  
Nonlinear Simulation ◽  
Network Algorithm ◽  
Neural Network Algorithm

Back-propagation (BP) neural network algorithm is currently used most widely and grows fastest for its powful nonlinear simulation capability. However BP neural network is so easy to fall into local minima that it cant find the global optimum which limits its application in many fields. The paper, taking tax innovation teaching evaluation for example, advances a new evaluation algorithm based on improved BP neural network algorithm. Firstly an evaluation indicator system of tax major innovation teaching is designed through analyzing the specific characteristics of innovation teaching requirements. Secondly, in order to overcome the shortages of low convergence speed of original BP neural network algorithm, the paper improves BP algorithm through integrating BP algorithm and ant colony algorithm, ant improving the overall search method of integrated algorithm. Thirdly data from three universities are taken for examples to verify the validity and feasibility of the model and the experimental results show that the model can evaluate university innovation teaching practically.

Research on BP Neural Network Algorithm Based on Quasi-Newton Method

2014 ◽  
Vol 686 ◽  
pp. 388-394 ◽  
Author(s):  
Pei Xin Lu
Keyword(s):  
Neural Network ◽  
Empirical Analysis ◽  
Newton Method ◽  
Bp Neural Network ◽  
Bp Algorithm ◽  
Newton Methods ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Quasi Newton ◽  
Improved Bp Algorithm

With more and more researches about improving BP algorithm, there are more improvement methods. The paper researches two improvement algorithms based on quasi-Newton method, DFP algorithm and L-BFGS algorithm. After fully analyzing the features of quasi-Newton methods, the paper improves BP neural network algorithm. And the adjustment is made for the problems in the improvement process. The paper makes empirical analysis and proves the effectiveness of BP neural network algorithm based on quasi-Newton method. The improved algorithms are compared with the traditional BP algorithm, which indicates that the improved BP algorithm is better.

scholarly journals RESEARCH ON ATHLETES’ PSYCHOLOGICAL REGULATION ABILITY BASED ON BACK PROPAGATION (BP) NEURAL NETWORK ALGORITHM

2021 ◽  
Vol 27 (spe2) ◽  
pp. 83-86
Author(s):  
Yun Tan ◽  
Guoqing Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Psychological Control ◽  
Back Propagation ◽  
Rotation Matrix ◽  
Psychological State ◽  
Advantages And Disadvantages ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Game State

ABSTRACT Athletes’ psychological control ability directly affects competitions. Therefore, it is necessary to supervise the athletes’ game psychology. Athletes’ game state supervision model is constructed through the facial information extraction algorithm. The homography matrix and the calculation method are introduced. Then, two methods are introduced to solve the rotation matrix from the homography matrix. After the rotation matrix is solved, the method of obtaining the facial rotation angle from the rotation matrix is introduced. The two methods are compared in the simulation data, and the advantages and disadvantages of each algorithm are analyzed to determine the method used in this paper. The experimental results show that the model prediction accuracy reaches 70%, which can effectively supervise the psychological state of athletes. This research study is of great significance to improve the performance of athletes in competitions and improve the application of back propagation (BP) 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.

Research on the Improvement of BP Neural Network Algorithm and its Application

2014 ◽  
Vol 926-930 ◽  
pp. 3216-3219 ◽  
Author(s):  
Xiong Kai
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Bp Algorithm ◽  
Network Algorithm ◽  
Simulation Calculation ◽  
Neural Network Algorithm ◽  
Working Principle ◽  
Calculation Ability ◽  
Improved Bp Algorithm

BP neural network has excellent ability to solve nonlinear optimal problem for its powerful simulation calculation ability and is wildly used in various disciplines in recent years, but its shortages of low convergence speed and falling into local minimum easily limit the usage of the algorithm. Based on the Levenberg-Marqardt optimization algorithm, the paper improves the BP neural network to overcome its shortages. First, the working structure and shortages of BP neural network are analyzed with more details. Second, the working principle of BP neural network algorithm is improved with Levenberg-Marqardt optimization algorithm and the calculation flows is redesigned. Third, data from three universities are used to realize the improved algorithm and the experimental results shows that the improved BP algorithm has better performance in calculation time and evaluation accuracy when used in university classroom teaching evaluation practically.

A Modified Back Propagation Algorithm of Neural Network with Global Optimization

2014 ◽  
Vol 1042 ◽  
pp. 232-238
Author(s):  
Jing Jing Li ◽  
Zhe Cui
Keyword(s):  
Neural Network ◽  
Global Optimization ◽  
Optimization Algorithm ◽  
Back Propagation ◽  
Bp Algorithm ◽  
Efficient Global Optimization ◽  
Back Propagation Algorithm ◽  
Global Optimization Algorithm ◽  
Function Fitting ◽  
Neural Network Algorithm

The advantages and weakens of traditional BP algorithm is briefly analyzed and an efficient global optimization algorithm is proposed.The basic principle of the algorithm is presented,and a new BP neural network algorithm based on the existing BP algorithm and the new global optimization algorithm is proposed, considering the new global optimization algorithm can solve the problem of local minimum efficiently. To verify the effectiveness of the new BP algorithm,the paper compared the experimental results of various algorithms in solving function fitting problem.

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.

Study on PCA-Based BP Neural Network Algorithm

2014 ◽  
Vol 530-531 ◽  
pp. 517-521
Author(s):  
Jian Qing Hong ◽  
De'an Zhao ◽  
Wei Kuan Jia
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Computing Time ◽  
Principal Component ◽  
Bp Algorithm ◽  
Complex Data ◽  
Network Algorithm ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Low Dimensional

Using the neural network to deal with complex data, because the pending sample with many variables, aiming at this nature of the pending sample and the structure properties of the BP neural network, in this paper, we propose the new BP neural network algorithm base on principal component analysis (PCA-BP algorithm). The new algorithm through PCA dimension reduction for complex data, got the low-dimensional data as the BP neural networks input, it will be beneficial to design the hidden layer of neural network, save a lot of storage space and computing time, and conductive to the convergence of the neural network. In order to verify the validity of the new algorithm, compared with the traditional BP algorithm, through the case analysis, the result show that the new algorithm improve the efficiency and recognition precise, worthy of further promotion.

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