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.

scholarly journals Detection of Network Intrusion Threat Based on the Probabilistic Neural Network Model

2019 ◽  
Vol 48 (4) ◽  
pp. 618-625
Author(s):  
Benyou Wang ◽  
Li Gu
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Network Security ◽  
Simulation Analysis ◽  
Probabilistic Neural Network ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Data Simulation ◽  
Network Intrusion

With the popularity of the Internet, people's lives are becoming more and more convenient. However, the network security problems are becoming increasingly serious. This paper, aiming to better protect users’ network security from the internal and external malicious attacks, briefly introduces the probabilistic neural network and principal component analysis method, and combines them for detection of network intrusion data. Simulation analysis of Probabilistic Neural Network (PNN) and Principal Component Analysis-Probabilistic Neural Network (PCA-PNN) are carried out in MATLAB software. The results suggest that the Principal Component Analysis (PCA) algorithm greatly reduce the dimension of the original data and the amount of calculation. Compared with PNN, PCA-PNN has higher accuracy and precision rate, lower false alarm rate, and faster detecting speed. Moreover, PCA-PNN has better detecting performance when there are few training samples. In summary, PCA-PNN can be used for the detection of network intrusion threat.

Recognition of Mineral Water with an Electronic Tongue Based on PCA and PNN

2011 ◽  
Vol 467-469 ◽  
pp. 888-893
Author(s):  
Hong Men ◽  
Yu Ming Guo ◽  
Rui Xia Wen ◽  
Bin Zhu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Mineral Water ◽  
Sensor Array ◽  
Probabilistic Neural Network ◽  
Electronic Tongue ◽  
Principal Component ◽  
Original Data ◽  
Accuracy Rate ◽  
Neural Network Algorithm

Electronic Tongue is a kind of intelligent equipment which is used to distinguish tastes. An electronic tongue composed of a sensor array of ion-selective electrodes has been developed and used for the qualitative analysis of five different brands of mineral water. The acquired original data has been optimized by principal component analysis (PCA) and then the probabilistic neural network (PNN) model is designed to process the data. The application results show that the performance of the proposed method has surpasses the traditional BP neural network algorithm, the speed of recognition is fast and the accuracy rate can reach 100%, which gives the electronic tongue system good practicability and feasibility.

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 Probabilistic Neural Network and Genetic Algorithm for Abdominal Aorta Aneurysm Identification

2020 ◽  
Vol 9 (3) ◽  
pp. 3202-3208
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Abdominal Aorta ◽  
Probabilistic Neural Network ◽  
Notch Filter ◽  
Abdominal Ultrasound ◽  
Neural Network Classifier ◽  
Abdominal Aorta Aneurysm ◽  
Aorta Aneurysm ◽  
Size Accuracy

An abdominal aorta aneurysm (AAA) can cause severe threat if it burst. Doctors can detect the presence of AAA by using abdominal ultrasound. As the treatment depends on the location and size, accuracy plays a significant role. To prevent devastating clinical outcome in this proposed work, new approaches and algorithms were used for generating the infallible result. After processing the AAA image by using notch filter, exudate based segmentation is performed and the selected features gets classified by using probabilistic neural network classifier. By using PNN classifier, accuracy and sensitivity gets enhanced in this work. The achieved accuracy is 98% and sensitivity 97.5%. While analogizing the proposed work with other existing work. It’s very facile to perform and expected target gets achieved

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.

Self-organizing probability neural network-based intelligent non-intrusive load monitoring with applications to low-cost residential measuring devices

2020 ◽  
pp. 014233122095086
Author(s):  
Zejian Zhou ◽  
Yingmeng Xiang ◽  
Hao Xu ◽  
Yishen Wang ◽  
Di Shi ◽  
...  
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Low Cost ◽  
Back Propagation ◽  
Learning Technologies ◽  
Training Time ◽  
The Public ◽  
Measuring Devices ◽  
Load Monitoring ◽  
Self Organizing

Non-intrusive load monitoring (NILM) is a critical technique for advanced smart grid management due to the convenience of monitoring and analysing individual appliances’ power consumption in a non-intrusive fashion. Inspired by emerging machine learning technologies, many recent non-intrusive load monitoring studies have adopted artificial neural networks (ANN) to disaggregate appliances’ power from the non-intrusive sensors’ measurements. However, back-propagation ANNs have a very limit ability to disaggregate appliances caused by the great training time and uncertainty of convergence, which are critical flaws for low-cost devices. In this paper, a novel self-organizing probabilistic neural network (SPNN)-based non-intrusive load monitoring algorithm has been developed specifically for low-cost residential measuring devices. The proposed SPNN has been designed to estimate the probability density function classifying the different types of appliances. Compared to back-propagation ANNs, the SPNN requires less iterative synaptic weights update and provides guaranteed convergence. Meanwhile, the novel SPNN has less space complexity when compared with conventional PNNs by the self-organizing mechanism which automatically edits the neuron numbers. These advantages make the algorithm especially favourable to low-cost residential NILM devices. The effectiveness of the proposed algorithm is demonstrated through numerical simulation by using the public REDD dataset. Performance comparisons with well-known benchmark algorithms have also been provided in the experiment section.

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