scholarly journals Estimating Weak Pulse Signal in Chaotic Background with Jordan Neural Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Liyun Su ◽  
Xiu Ling
Keyword(s):  
Neural Network ◽  
Pulse Signal ◽  
Least Square ◽  
Data Matrix ◽  
Support Vector ◽  
Model Parameters ◽  
Elman Neural Network ◽  
Useful Signal ◽  
Weak Pulse ◽  
Space Data

In target estimating sea clutter or actual mechanical fault diagnosis, useful signal is often submerged in strong chaotic noise, and the targeted signal data are difficult to recover. Traditional schemes, such as Elman neural network (ENN), backpropagation neural network (BPNN), support vector machine (SVM), and multilayer perceptron- (MLP-) based model, are insufficient to extract the weak signal embedded in a chaotic background. To improve the estimating accuracy, a novel estimating method for aiming at extracting problem of weak pulse signal buried in a strong chaotic background is presented. Firstly, the proposed method obtains the vector sequence signal by reconstructing higher-dimensional phase space data matrix according to the Takens theorem. Then, a Jordan neural network- (JNN-) based model is designed, which can minimize the error squared sum by mixing the single-point jump model for targeting signal. Finally, based on short-term predictability of chaotic background, estimation of weak pulse signal from the chaotic background is achieved by a profile least square method for optimizing the proposed model parameters. The data generated by the Lorenz system are used as chaotic background noise for the simulation experiment. The simulation results show that Jordan neural network and profile least square algorithm are effective in estimating weak pulse signal from chaotic background. Compared with the traditional method, (1) the presented method can estimate the weak pulse signal in strong chaotic noise under lower error than ENN-based, BPNN-based, SVM-based, and -ased models and (2) the proposed method can extract the weak pulse signal under a higher output SNR than BPNN-based model.

scholarly journals Statistical Detection of Weak Pulse Signal under Chaotic Noise Based on Elman Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Liyun Su ◽  
Li Deng ◽  
Wanlin Zhu ◽  
Shengli Zhao
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Signal Detection ◽  
Prediction Error ◽  
Pulse Signal ◽  
Learning Ability ◽  
Weak Signal ◽  
Weak Signal Detection ◽  
Elman Neural Network ◽  
Weak Pulse

Weak signal detection is a significant problem in modern detection such as mechanical fault diagnosis. The uniqueness of chaos and good learning ability of neural networks provide new ideas and framework for weak signal detection field. In this paper, Elman neural network is applied to detect and recover weak pulse signal in chaotic noise. For detection problem of weak pulse signal under chaotic noise, based on short-term predictability of chaotic observations, phase space reconstruction for observed signals is carried out. And Elman deep learning adaptive detection model (EDAD model) is established for weak pulse signal detection, and a hypothesis test is used to detect weak pulse signal from the prediction error. For the recovery of weak pulse signal under chaotic noise, a double-layer Elman deep neural network recovery model (DEDR model) is proposed, which is based on the Elman deep learning network model and single-point jump model for weak pulse signal, and it is optimized with goal of minimizing mean square prediction error of the Elman model. The profile least squares method is applied to estimate parameters of the DEDR model for difficult recovery of weak pulse signal because the DEDR model is essentially a semiparametric model with parametric and nonparametric parts. In the end, simulation experiments show that the model built in this paper can effectively detect and recover weak pulse signal in the background of chaotic noise.

Identification of stochastic neutron pulse signal of 252Cf nuclear system based on Elman neural network

2011 ◽  
Vol 23 (8) ◽  
pp. 2224-2228
Author(s):  
冯鹏 Feng Peng ◽  
刘思远 Liu Siyuan ◽  
米德伶 Mi Deling
Keyword(s):  
Neural Network ◽  
Pulse Signal ◽  
Neutron Pulse ◽  
Nuclear System ◽  
Elman Neural Network

Model-Based EEG Analysis: Proposal and Verification of Mathematical Model for Application to Neurotechnology

2019 ◽  
Author(s):  
Kenyu Uehara ◽  
Takashi Saito
Keyword(s):  
Mathematical Model ◽  
Binary Classification ◽  
Least Square ◽  
Mental Condition ◽  
Support Vector ◽  
Model Parameters ◽  
Eeg Analysis ◽  
Analysis Method ◽  
Model Based ◽  
Proposed Model

Abstract We have modeled dynamics of EEG with one degree of freedom nonlinear oscillator and examined the relationship between mental state of humans and model parameters simulating behavior of EEG. At the IMECE conference last year, Our analysis method identified model parameters sequentially so as to match the waveform of experimental EEG data of the alpha band using one second running window. Results of temporal variation of model parameters suggested that the mental condition such as degree of concentration could be directly observed from the dynamics of EEG signal. The method of identifying the model parameters in accordance with the EEG waveform is effective in examining the dynamics of EEG strictly, but it is not suitable for practical use because the analysis (parameter identification) takes a long time. Therefore, the purpose of this study is to test the proposed model-based analysis method for general application as a neurotechnology. The mathematical model used in neuroscience was improved for practical use, and the test was conducted with the cooperation of four subjects. model parameters were experimentally identified approximately every one second by using least square method. We solved a binary classification problem of model parameters using Support Vector Machine. Results show that our proposed model-based EEG analysis is able to discriminate concentration states in various tasks with an accuracy of over 80%.

scholarly journals Detection and Extraction of Weak Pulse Signals in Chaotic Noise with PTAR and DLTAR Models

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Liyun Su ◽  
Li Deng ◽  
Wanlin Zhu ◽  
Shengli Zhao
Keyword(s):  
Roc Curve ◽  
Linear Form ◽  
Hypothesis Test ◽  
Pulse Signal ◽  
Support Vector ◽  
Detector Performance ◽  
Threshold Autoregressive ◽  
Profile Least Squares ◽  
Area Under Roc Curve ◽  
Weak Pulse

With the development in communications, the weak pulse signal is submerged in chaotic noise, which is very common in seismic monitoring and detection of ocean clutter targets, and is very difficult to detect and extract. Based on the threshold autoregressive model, pulse linear form, Markov chain Monte Carlo (MCMC), and profile least squares (PrLS) algorithm, phase threshold autoregressive (PTAR) model and double layer threshold autoregressive (DLTAR) model are proposed for detection and extraction of weak pulse signals in chaotic noise, respectively. Firstly, based on noisy chaotic observation, phase space is reconstructed according to Takens’s delay embedding theorem, and the phase threshold autoregressive (PTAR) model is presented to detect weak pulse signals, and then the MCMC algorithm is applied to estimate parameters in the PTAR model; lastly, we obtain one-step prediction error, which is used to realize adaptively detection of weak signals with the hypothesis test. Secondly, a linear form for the pulse signal and PTAR model is fused to build a DLTAR model to extract weak pulse signals. The DLTAR model owns two kinds of parameters, which are affected mutually. Here, the PrLS algorithm is applied to estimate parameters of the DLTAR model and ultimately extract weak pulse signals. Finally, accurate rate (Acc), receiver operating characteristic (ROC) curve, and area under ROC curve (AUC) are used as the detector performance index; mean square error (MSE), mean absolute percent error (MAPE), and relative error (Re) are used as the extraction accuracy index. The presented scheme does not need prior knowledge of chaotic noise and weak pulse signals, and simulation results show that the proposed PTAR-DLTAR model is significantly effective for detection and extraction of weak pulse signals under chaotic interference. Specifically, in very low signal-to-interference ratio (SIR), weak pulse signals can be detected and extracted compared with support vector machine (SVM) class and neural network model.

The Hammerstein Predict Model of Control Valve Based on Least Square Support Vector Machine

2014 ◽  
Vol 472 ◽  
pp. 164-170
Author(s):  
Ai Qin Huang ◽  
Yong Wang
Keyword(s):  
Linear Equations ◽  
Control Valve ◽  
Least Square ◽  
Hammerstein Model ◽  
Support Vector ◽  
Model Parameters ◽  
Chemistry Industry ◽  
Single Output ◽  
Controlling System ◽  
Value Decomposition

Control valve is a kind of essential terminal control component controlling the parameters of fluid such as flow and pressure in process-control. However it is a complex nonlinear, multi-input and single-output (MISO) system that is hard to model by traditional methodologies. To establish the pressure model of control valve, this paper presents a Hammerstein modeling method based on the least squares support vector machines (LS-SVM). The linear model parameters and the static nonlinearity of Hammerstein model can be obtained simultaneously by solving a set of linear equations followed by the singular value decomposition (SVD). As an example, a set of actual production data from a controlling system of chlorine in the salt chemistry industry were applied. The simulation results demonstrate that the obtained LS-SVM Hammerstein model can efficiently approximate the pressure of a control valve. Furthermore, the proposed LS-SVM Hammerstein model can be used in artificial intelligent control and the default diagnosis.

scholarly journals Predicting Freeway Travel Time Using Multiple- Source Heterogeneous Data Integration

2018 ◽  
Vol 9 (1) ◽  
pp. 104 ◽  
Author(s):  
Kejun Long ◽  
Wukai Yao ◽  
Jian Gu ◽  
Wei Wu ◽  
Lee Han
Keyword(s):  
Neural Network ◽  
Travel Time ◽  
Bp Neural Network ◽  
Historical Data ◽  
Multiple Source ◽  
Function Parameter ◽  
Support Vector ◽  
Model Parameters ◽  
Svm Model ◽  
Adverse Weather

Freeway travel time is influenced by many factors including traffic volume, adverse weather, accidents, traffic control, and so on. We employ the multiple source data-mining method to analyze freeway travel time. We collected toll data, weather data, traffic accident disposal logs, and other historical data from Freeway G5513 in Hunan Province, China. Using the Support Vector Machine (SVM), we proposed the travel time predicting model founded on these databases. The new SVM model can simulate the nonlinear relationship between travel time and those factors. In order to improve the precision of the SVM model, we applied the Artificial Fish Swarm algorithm to optimize the SVM model parameters, which include the kernel parameter σ, non-sensitive loss function parameter ε, and penalty parameter C. We compared the new optimized SVM model with the Back Propagation (BP) neural network and a common SVM model, using the historical data collected from freeway G5513. The results show that the accuracy of the optimized SVM model is 17.27% and 16.44% higher than those of the BP neural network model and the common SVM model, respectively.

scholarly journals Analysis of ECG Arrhythmia for Heart Disease Detection using SVM and Cuckoo Search Optimized Neural Network

2018 ◽  
Vol 7 (2.17) ◽  
pp. 27
Author(s):  
Dinesh D. Patil ◽  
R P. Singh ◽  
Vilas M. Thakare ◽  
Avinash K. Gulve
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Recognition Performance ◽  
Cuckoo Search ◽  
Data Matrix ◽  
Support Vector ◽  
Electrocardiogram Signal ◽  
Biased Estimators ◽  
Main Components ◽  
Ecg Arrhythmia

This paper tried to address several topics concerning the analysis, synthesis and compression of the electrocardiogram signal (ECG) using the MIT database. We detect the R-wave by identifying the location of each interval delineating a QRS complex using unbiased and biased estimators. In the second part of the work, we segmented the signal into RR periods constituting the vectors of a data matrix, where we extracted its main components in order to reduce the size of the cardiac information, and then further reduced in addition the size by the use of a threshold on the signal. Then the classification is done for automatic detection of heart disease using Support Vector Machine (SVM) and Cuckoo Search Optimized Neural Network. ECG beats with 4 types of abnormalities (RBBB, APC, PVC and LBBB) from ECG records is retrieved from the MIT-BIH arrhythmia database. Analysis of the different groups shows the overall recognition performance was 99.50%. The worst is 99.63% for the RBBB class. 

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