New fast time delay neural networks using cross correlation performed in the frequency domain

2006 ◽  
Vol 69 (16-18) ◽  
pp. 2360-2363 ◽  
Author(s):  
Hazem M. El-Bakry
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Frequency Domain ◽  
Cross Correlation ◽  
Fast Time

FAST TIME DELAY NEURAL NETWORKS

2005 ◽  
Vol 15 (06) ◽  
pp. 445-455 ◽  
Author(s):  
HAZEM M. EL-BAKRY ◽  
QIANGFU ZHAO
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Frequency Domain ◽  
Cross Correlation ◽  
Input Pattern ◽  
Fast Time ◽  
New Approach ◽  
Entire Data ◽  
Speed Up ◽  
Simulation Results

This paper presents a new approach to speed up the operation of time delay neural networks. The entire data are collected together in a long vector and then tested as a one input pattern. The proposed fast time delay neural networks (FTDNNs) use cross correlation in the frequency domain between the tested data and the input weights of neural networks. It is proved mathematically and practically that the number of computation steps required for the presented time delay neural networks is less than that needed by conventional time delay neural networks (CTDNNs). Simulation results using MATLAB confirm the theoretical computations.

scholarly journals A Modified Leakage Localization Method Using Multilayer Perceptron Neural Networks in a Pressurized Gas Pipe

2019 ◽  
Vol 9 (9) ◽  
pp. 1954 ◽  
Author(s):  
Qi Wu ◽  
Chang-Myung Lee
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Multilayer Perceptron ◽  
Cross Correlation ◽  
Peak Frequency ◽  
Flexural Mode ◽  
Location Method ◽  
Ae Signal ◽  
Sensor Signals ◽  
Leakage Location

Leak detection and location in a gas distribution network are significant issues. The acoustic emission (AE) technique can be used to locate a pipeline leak. The time delay between two sensor signals can be determined by the cross-correlation function (CCF), which is a measure of the similarity of two signals as a function of the time delay between them. Due to the energy attenuation, dispersion effect and reverberation of the leakage-induced signals in the pipelines, the CCF location method performs poorly. To improve the leakage location accuracy, this paper proposes a modified leakage location method based on the AE signal, and combines the modified generalized cross-correlation location method and the attenuation-based location method using multilayer perceptron neural networks (MLPNN). In addition, the wave speed was estimated more accurately by the peak frequency of the leakage-induced AE signal in combination with the group speed dispersive curve of the fundamental flexural mode. To verify the reliability of the proposed location method, many tests were performed over a range of leak-sensor distances. The location results show that compared to using the CCF location method, the MLPNN locator reduces the average of the relative location errors by 14%, therefore, this proposed method is better than the CCF method for locating a gas pipe leak.

Fast time-delay measurement for integrated pulsar pulse profiles

2017 ◽  
Vol 89 (2) ◽  
pp. 297-303 ◽  
Author(s):  
Zhiwei Kang ◽  
Xin He ◽  
Jin Liu ◽  
Tianyuan Tao
Keyword(s):  
Correlation Function ◽  
Time Delay ◽  
Cross Correlation ◽  
Fast Time ◽  
Pulse Profile ◽  
Third Order ◽  
Content Type ◽  
The Third ◽  
Delay Measurement ◽  
Time Delay Measurement

Purpose The authors proposed a new method of fast time delay measurement for integrated pulsar pulse profiles in X-ray pulsar-based navigation (XNAV). As a basic observation of exact orientation in XNAV, time of arrival (TOA) can be obtained by time delay measurement of integrated pulsar pulse profiles. Therefore, the main purpose of the paper is to establish a method with fast time delay measurement on the condition of limited spacecraft’s computing resources. Design/methodology/approach Given that the third-order cumulants can suppress the Gaussian noise and reduce calculation to achieve precise and fast positioning in XNAV, the proposed method sets the third-order auto-cumulants of standard pulse profile, the third-order cross-cumulants of the standard and the observed pulse profile as basic variables and uses the cross-correlation function of these two variables to estimate the time delay of integrated pulsar pulse profiles. Findings The proposed method is simple, fast and has high accuracy in time delay measurement for integrated pulsar pulse profiles. The result shows that compared to the bispectrum algorithm, the method improves the precision of the time delay measurement and reduced the computation time significantly as well. Practical implications To improve the performance of time delay estimation in XNAV systems, the authors proposed a novel method for XNAV to achieve precise and fast positioning. Originality/value Compared to the bispectrum algorithm, the proposed method can improve the speed and precision of the TOA’s calculation effectively by using the cross-correlation function of integrated pulsar pulse profile’s third-order cumulants instead of Fourier transform in bispectrum algorithm.

scholarly journals A novel method for quantifying periodicity and time delay in dynamic neural networks using unstable subaction potential threshold depolarizations

2020 ◽  
Vol 123 (3) ◽  
pp. 1236-1246
Author(s):  
Julian Sorensen ◽  
Nick J. Spencer
Keyword(s):  
Neural Networks ◽  
Correlation Function ◽  
Time Delay ◽  
Action Potentials ◽  
Cross Correlation ◽  
Time Lag ◽  
Cross Correlation Function ◽  
Electrical Signals ◽  
The Cross

Techniques to identify and correlate the propagation of electrical signals (like action potentials) along neural networks are well described, using multisite recordings. In these cases, the waveform of action potentials is usually relatively stable and discriminating relevant electrical signals straightforward. However, problems can arise when attempting to identify and correlate the propagation of signals when their waveforms are unstable (e.g., fluctuations in amplitude or time course). This makes correlation of the degree of synchronization and time lag between propagating electrical events across two or more recording sites problematic. Here, we present novel techniques for the determination of the periodicity of electrical signals at individual sites. When recording from two independent sites, we present novel analytical techniques for joint determination of periodicity and time delay. The techniques presented exploit properties of the cross-correlation function, rather than utilizing the time lag at which the cross-correlation function is maximized. The approach allows determination of directionality of the spread of excitation along a neural network based on measurements of the time delay between recording sites. This new method is particularly applicable to analysis of signals in other biological systems that have unstable characteristics in waveform that show dynamic variability. NEW & NOTEWORTHY The determination of frequency(s) at which two sources are synchronized, and relative time delay between them, is a fundamental problem for a wide a range of signal-processing applications. In this methodology paper, we present novel procedures for periodicity estimation for single time series and joint periodicity and time delay estimation for two time series. The methods use properties of the cross-correlation function rather than the cross-correlation function explicitly.

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