scholarly journals System Identification of Neural Signal Transmission Based on Backpropagation Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Xiangyu Li ◽  
Chunhua Yuan ◽  
Bonan Shan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Signal Transmission ◽  
Neural Model ◽  
Neural Firing ◽  
Output Data ◽  
Neural Network Learning ◽  
Network Learning ◽  
Network Training ◽  
Input And Output

The identification method of backpropagation (BP) neural network is adopted to approximate the mapping relation between input and output of neurons based on neural firing trajectory in this paper. In advance, the input and output data of neural model is used for BP neural network learning, so that the identified BP neural network can present the transfer characteristics of the model, which makes the network precisely predict the firing trajectory of the neural model. In addition, the method is applied to identify electrophysiological experimental data of real neurons, so that the output of the identified BP neural network can not only accurately fit the neural firing trajectories of neurons participating in the network training but also predict the firing trajectories and spike moments of neurons which are not involved in the training process with high accuracy.

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.

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.

BINARY NEURAL NETWORK TRAINING ALGORITHMS BASED ON LINEAR SEQUENTIAL LEARNING

2003 ◽  
Vol 13 (05) ◽  
pp. 333-351 ◽  
Author(s):  
DI WANG ◽  
NARENDRA S. CHAUDHARI
Keyword(s):  
Neural Network ◽  
Sequential Learning ◽  
Training Algorithms ◽  
Neural Network Training ◽  
Neural Network Learning ◽  
Network Learning ◽  
Linear Separability ◽  
Network Training ◽  
Binary Neural Network ◽  
Hidden Layer

A key problem in Binary Neural Network learning is to decide bigger linear separable subsets. In this paper we prove some lemmas about linear separability. Based on these lemmas, we propose Multi-Core Learning (MCL) and Multi-Core Expand-and-Truncate Learning (MCETL) algorithms to construct Binary Neural Networks. We conclude that MCL and MCETL simplify the equations to compute weights and thresholds, and they result in the construction of simpler hidden layer. Examples are given to demonstrate these conclusions.

A Parallel BP Neural Network Based on the FPGA

2012 ◽  
Vol 239-240 ◽  
pp. 1541-1545 ◽  
Author(s):  
Yu Hui Guo ◽  
De Tai Zhou ◽  
Xiang Ping Qian ◽  
Xian Qiang Zeng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Software System ◽  
Neural Network Learning ◽  
Network Learning ◽  
System On Programmable Chip ◽  
Computer Unit

This paper, with the analysis of BP neural network learning and execution algorithm on single computer unit, a parallel neural network on many computer units is constructed, a system on programmable chip based on FPGA and uClinux is provided. Because it’s flexibility and reliability, this parallel neural network can be widely used where there is a large quantity of data to be processed. In addition to it, the system based on the SOPC has good versatility and easy to transplant because the reconfiguration of the hardware logic and software system.

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 Influence of Data Scaling and Normalization on Overall Neural Network Performances in Photoacoustics

2021 ◽  
Author(s):  
Miroslava Ivko Jordovic Pavlovic ◽  
Katarina Djordjevic ◽  
Zarko Cojbasic ◽  
Slobodanka Galovic ◽  
Marica Popovic ◽  
...  
Keyword(s):  
Neural Network ◽  
Input Data ◽  
Photoacoustic Signal ◽  
Output Data ◽  
Network Training ◽  
Input And Output ◽  
The Neural Network ◽  
Data Scaling ◽  
Logarithmic Scaling ◽  
Network Prediction

Abstract In this paper, the influence of the input and output data scaling and normalization on the neural network overall performances is investigated aimed at inverse problem-solving in photoacoustics of semiconductors. The logarithmic scaling of the photoacoustic signal amplitudes as input data and numerical scaling of the sample thermal parameters as output data are presented as useful tools trying to reach maximal network precision. Max and min-max normalizations to the input data are presented to change their numerical values in the dataset to common scales, without distorting differences. It was demonstrated in theory that the largest network prediction error of all targeted parameters is obtained by a network with non-scaled output data. Also, it was found out that the best network prediction was achieved with min-max normalization of the input data and network predicted output data scale within the range of [110]. Network training and prediction performances analyzed with experimental input data show that the benefits and improvements of input and output scaling and normalization are not guaranteed but are strongly dependent on a specific problem to be solved.

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.

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