scholarly journals The Application of BP Neural Network in Internet of Things

2012 ◽  
Vol 6-7 ◽  
pp. 1098-1102 ◽  
Author(s):  
Dan Dan Cui ◽  
Fei Liu
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Neural Network Learning ◽  
Network Learning ◽  
Input Layer ◽  
Telecommunications Network ◽  
Hidden Layer ◽  
The Internet Of Things

BP algorithm is a typical artificial neural network learning algorithm, the main structure consists of an input layer, one or more hidden layer, an output layer, the layers of the number of neurons, the output of each node the value is decided by the input values, the role, function and threshold. The Internet of Things is based on the information carrier of the traditional telecommunications network, so that all can be individually addressable ordinary physical objects to achieve the interoperability network. The paper puts forward the application of BP neural network in internet of things. The experiment shows BP is superior to RFID in internet of things.

Research on Improved BP Learning Algorithm of BP Neural Network

2013 ◽  
Vol 765-767 ◽  
pp. 1644-1647 ◽  
Author(s):  
Jian Li Chu ◽  
Hong Yan Li ◽  
Xiao Ji Chen
Keyword(s):  
Neural Network ◽  
Conjugate Gradient ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Error Function ◽  
Learning Rate ◽  
Good Effect ◽  
Dynamic Learning ◽  
Neural Network Learning ◽  
Hidden Layer

Aiming at the existence of the BP neural network learning algorithm in the slow learning speed, the possibility of failure is large, poor generalization ability, there are multiple issues, extreme value point and network structure are difficult to determine, in this paper, we study algorithm improvement methods. Explain the algorithm principle, on the basis of three improved methods are studied, respectively is dynamic learning rate, conjugate gradient, improved error function. Among them, the dynamic learning rate, it reaches the learning rate of the hidden layer and output layer; Conjugate gradient, this paper gives three calculating formula; Improved error function, to solve different problems are also given in three types of error function. BP learning algorithm in this paper, the research contents, make the convergence stability, convergence speed, initial value sensitivity, it has good effect, which has large significant in terms of academic and applied significance.

Application of a genetic algorithm to optimize BP neural networks

2020 ◽  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Local Minimum ◽  
Network Performance ◽  
Learning Algorithm ◽  
Simulation Data ◽  
Neural Network Learning ◽  
Network Learning ◽  
Fast Convergence Rate

A genetic algorithm is proposed to us to prevent a local minimum defect when using the BP neural network learning algorithm. The genetic algorithm is first used to optimize the weight and threshold of the BP neural network, and then obtained values are used to optimize the BP neural network. Optimized network performance is estimated using simulation data. The results of numerical simulations show that the BP neural network optimized by the genetic algorithm can effectively eliminate a local minimum defect, which is easy to find in the original BP neural network, and has the advantages of fast convergence rate and high accuracy. Keywords BP neural network; genetic algorithm; local minimum defect; optimization

The Application of BP Neural Network Learning Algorithm Based on the Particle Swarm Optimization

2013 ◽  
Vol 706-708 ◽  
pp. 2057-2062
Author(s):  
Zhi Hong Sun ◽  
Jun Wang ◽  
Bao Ji Xu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Real Estate ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Particle Swarm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Neural Network Learning ◽  
Network Learning

The development of real estate has been affected by various social factors, including economic factors. BP neural network can more accurately forecast the trend of real estate industry according to economic development indicators. But BP neural network is slow convergence in the training process, and easily falls into local optimum. The BP neural network learning algorithm based on the particle swarm optimization (PSO) optimizes the weights and thresholds of the network by PSO algorithm, then to train BP neural network. The experimental results show that the performance of this new algorithm is better than BP neural network, but also has good convergence.

scholarly journals The Compound Prediction Analysis of Information Network Security Situation based on Support Vector Combined with BP Neural Network Learning Algorithm

2022 ◽  
Vol 16 ◽  
pp. 489-496
Author(s):  
Jie Cheng ◽  
Bingjie Lin ◽  
Jiahui Wei ◽  
Ang Xia
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Support Vector ◽  
Neural Network Learning ◽  
Network Learning ◽  
Redundant Data ◽  
Slope Change ◽  
Neural Network Algorithm

In order to solve the problem of low security of data in network transmission and inaccurate prediction of future security situation, an improved neural network learning algorithm is proposed in this paper. The algorithm makes up for the shortcomings of the standard neural network learning algorithm, eliminates the redundant data by vector support, and realizes the effective clustering of information data. In addition, the improved neural network learning algorithm uses the order of data to optimize the "end" data in the standard neural network learning algorithm, so as to improve the accuracy and computational efficiency of network security situation prediction.MATLAB simulation results show that the data processing capacity of support vector combined BP neural network is consistent with the actual security situation data requirements, the consistency can reach 98%. the consistency of the security situation results can reach 99%, the composite prediction time of the whole security situation is less than 25s, the line segment slope change can reach 2.3% ,and the slope change range can reach 1.2%,, which is better than BP neural network algorithm.

scholarly journals A Geometric Perspective on Information Plane Analysis

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 711
Author(s):  
Mina Basirat ◽  
Bernhard C. Geiger ◽  
Peter M. Roth
Keyword(s):  
Neural Network ◽  
Mutual Information ◽  
Geometric Interpretation ◽  
Neural Network Training ◽  
Neural Network Learning ◽  
Network Learning ◽  
Plane Analysis ◽  
Network Training ◽  
Hidden Layer ◽  
The Impact

Information plane analysis, describing the mutual information between the input and a hidden layer and between a hidden layer and the target over time, has recently been proposed to analyze the training of neural networks. Since the activations of a hidden layer are typically continuous-valued, this mutual information cannot be computed analytically and must thus be estimated, resulting in apparently inconsistent or even contradicting results in the literature. The goal of this paper is to demonstrate how information plane analysis can still be a valuable tool for analyzing neural network training. To this end, we complement the prevailing binning estimator for mutual information with a geometric interpretation. With this geometric interpretation in mind, we evaluate the impact of regularization and interpret phenomena such as underfitting and overfitting. In addition, we investigate neural network learning in the presence of noisy data and noisy labels.

Adaptive Learning Algorithm for Cerebellar Model Articulation Controller: Neural Network Based Hybrid-Type Controller—Part I

2000 ◽  
Author(s):  
Magdy Mohamed Abdelhameed ◽  
Sabri Cetinkunt
Keyword(s):  
Neural Network ◽  
Control System ◽  
Adaptive Learning ◽  
Learning Algorithm ◽  
Cerebellar Model Articulation Controller ◽  
Neural Network Learning ◽  
Network Learning ◽  
Hybrid Type ◽  
Cmac Neural Network ◽  
Learning Technique

Abstract Cerebellar model articulation controller (CMAC) is a useful neural network learning technique. It was developed two decades ago but yet lacks an adequate learning algorithm, especially when it is used in a hybrid- type controller. This work is intended to introduce a simulation study for examining the performance of a hybrid-type control system based on the conventional learning algorithm of CMAC neural network. This study showed that the control system is unstable. Then a new adaptive learning algorithm of a CMAC based hybrid- type controller is proposed. The main features of the proposed learning algorithm, as well as the effects of the newly introduced parameters of this algorithm have been studied extensively via simulation case studies. The simulation results showed that the proposed learning algorithm is a robust in stabilizing the control system. Also, this proposed learning algorithm preserved all the known advantages of the CMAC neural network. Part II of this work is dedicated to validate the effectiveness of the proposed CMAC learning algorithm experimentally.

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