scholarly journals Optimization Strategy of IoT Sensor Configuration Based on Genetic Algorithm-Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yuanyuan Yang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Data Communication ◽  
Piezoelectric Sensor ◽  
Sensor Data ◽  
Improved Genetic Algorithm ◽  
Management Platform ◽  
Sensor Configuration ◽  
Web Management ◽  
The Web

This article carries out the overall design framework of the IoT sensor data processing platform and analyzes the advantages of using the integrated construction platform. The platform is divided into two parts, a web management platform and a data communication system, and interacts with the database by integrating the business layers of the two into one. The web management platform provides configurable communication protocol customization services, equipment information, personal information, announcement information management services, and data collection information monitoring and analysis services. The collected data is analyzed by the sensor data communication service system and then provided to the web management platform for query and call. This paper discusses the theoretical basis of the combination of genetic algorithm and neural network and proposes the necessity of improving genetic algorithm. The improved level involves chromosome coding methods, fitness function selection, and genetic manipulation. We propose an improved genetic algorithm and use an improved genetic algorithm (IGA) to optimize the neural network structure. The finite element method is adopted, the finite element model is established, and the shock piezoelectric response is numerically simulated. The genetic neural network method is used to simulate the collision damage location detection problem. The piezoelectric sensor is optimized, and the optimal sensor configuration corresponding to its initial layout is obtained, which provides guidance for the optimal configuration of the actual piezoelectric sensor.

Neural Network Based On the Improved Genetic Algorithm to Optimize Component Separation Conditions of Small Peptide

2011 ◽  
Vol 284-286 ◽  
pp. 261-264
Author(s):  
Jing Wen Tian ◽  
Feng Jun Wu ◽  
Hui Chen ◽  
Jing Di Ren
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Liquid Chromatography ◽  
Forecast Model ◽  
Optimization Methods ◽  
Reversed Phase ◽  
Improved Genetic Algorithm ◽  
Small Peptide ◽  
Neural Network Theory ◽  
Separation Conditions

Reference to traditional optimization methods, neural network based on improved genetic algorithm is used in optimization of reversed phase chromatography pluralistic isocratic mobile phase separation conditions. With detailing the combination of the improved genetic algorithm and neural network theory, the optimization process for the liquid chromatography conditions is introduced in details. Used this method to small peptide RP chromatography optimization, after searching operation, the establishment of an effective separation of forecast model receives satisfactory predictive value, which can prove that this method can be used in optimization of drug liquid chromatography conditions.

scholarly journals Air Combat Maneuver Trajectory Prediction Model of Target Based on Chaotic Theory and IGA-VNN

2020 ◽  
Vol 2020 ◽  
pp. 1-23
Author(s):  
Zhi-fei Xi ◽  
An Xu ◽  
Ying-xin Kou ◽  
Zhan-wu Li ◽  
Ai-wu Yang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Time Series ◽  
Bp Neural Network ◽  
Functional Model ◽  
Mutation Operator ◽  
Improved Genetic Algorithm ◽  
Trajectory Prediction ◽  
Target Trajectory ◽  
Chaotic Theory

Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and threat assessment. Aiming at the problem of low prediction accuracy in traditional trajectory prediction methods, combined with the chaotic characteristics of the target maneuver trajectory time series, a target maneuver trajectory prediction model based on chaotic theory and improved genetic algorithm-Volterra neural network (IGA-VNN) model is proposed, mathematically deducing and analyzing the consistency between Volterra functional model and back propagation (BP) neural network in structure. Firstly, the C-C method is used to reconstruct the phase space of the target trajectory time series, and the maximum Lyapunov exponent of the time series of the target maneuver trajectory is calculated. It is proved that the time series of the target maneuver trajectory has chaotic characteristics, so the chaotic method can be used to predict the target trajectory time series. Then, the practicable Volterra functional model and BP neural network are combined together, learning the advantages of both and overcoming the difficulty in obtaining the high-order kernel function of the Volterra functional model. At the same time, an adaptive crossover mutation operator and a combination mutation operator based on the difference degree of gene segments are proposed to improve the traditional genetic algorithm; the improved genetic algorithm is used to optimize BP neural network, and the optimal initial weights and thresholds are obtained. Finally, the IGA-VNN model of chaotic time series is applied to the prediction of target maneuver trajectory time series, and the experimental results show that its estimated performance is obviously superior to other prediction algorithms.

scholarly journals Research on Quality Anomaly Recognition Method Based on Optimized Probabilistic Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Li-li Li ◽  
Kun Chen ◽  
Jian-min Gao ◽  
Hui Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Control Chart ◽  
Control Charts ◽  
Probabilistic Neural Network ◽  
Principal Component ◽  
Original Data ◽  
Improved Genetic Algorithm ◽  
Training Time ◽  
Mixed Pattern

Aiming at the problems of the lack of abnormal instances and the lag of quality anomaly discovery in quality database, this paper proposed the method of recognizing quality anomaly from the quality control chart data by probabilistic neural network (PNN) optimized by improved genetic algorithm, which made up deficiencies of SPC control charts in practical application. Principal component analysis (PCA) reduced the dimension and extracted the feature of the original data of a control chart, which reduced the training time of PNN. PNN recognized successfully both single pattern and mixed pattern of control charts because of its simple network structure and excellent recognition effect. In order to eliminate the defect of experience value, the key parameter of PNN was optimized by the improved (SGA) single-target optimization genetic algorithm, which made PNN achieve a higher rate of recognition accuracy than PNN optimized by standard genetic algorithm. Finally, the above method was validated by a simulation experiment and proved to be the most effective method compared with traditional BP neural network, single PNN, PCA-PNN without parameters optimized, and SVM optimized by particle swarm optimization algorithm.

An Improved Prediction Method of SOC Based on the GA-RBF Neural Network

2014 ◽  
Vol 953-954 ◽  
pp. 800-805 ◽  
Author(s):  
Meng Di Liang ◽  
Tie Zhou Wu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Prediction Method ◽  
Charged State ◽  
Improved Genetic Algorithm ◽  
State Prediction ◽  
Radial Basis

Concerning the prediction problems’accuracy of the state-of-charge(SOC) of the battery，this paper proposes a prediction method based on an improved genetic algorithm-radial basis function neural network for power battery charged state. The prediction method, based on intensity of information interaction and neural activity, adjusts the size of the neural network online and solves the problem that radial basis function neural network structure adjustment influences the accuracy of charged state prediction. The simulation results show that，compared with the method of radial basis function neural network based on genetic algorithm , the accuracy of charged state prediction is more stable and more precise.

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