Hebbian learning reconsidered: Representation of static and dynamic objects in associative neural nets

1989 ◽  
Vol 60 (6) ◽  
pp. 457-467 ◽  
Author(s):  
A. Herz ◽  
B. Sulzer ◽  
R. Kühn ◽  
J. L. van Hemmen
Keyword(s):  
Hebbian Learning ◽  
Neural Nets ◽  
Dynamic Objects

NEURAL NETS FOR DUAL SUBSPACE PATTERN RECOGNITION METHOD

1991 ◽  
Vol 02 (03) ◽  
pp. 169-184 ◽  
Author(s):  
Lei Xu ◽  
Adam Krzyzak ◽  
Erkki Oja
Keyword(s):  
Pattern Recognition ◽  
Hebbian Learning ◽  
Network Models ◽  
Neural Nets ◽  
Experimental Comparison ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Neural Network Models ◽  
Learning Rules ◽  
Dual Subspace

A new modification of the subspace pattern recognition method, called the dual subspace pattern recognition (DSPR) method, is proposed, and neural network models combining both constrained Hebbian and anti-Hebbian learning rules are developed for implementing the DSPR method. An experimental comparison is made by using our model and a three-layer forward net with backpropagation learning. The results illustrate that our model can outperform the backpropagation model in suitable applications.

Remote diagnostics of vibrations of dynamic objects by Doppler microwave sensors

Metrologiya ◽  
2020 ◽  
pp. 25-42
Author(s):  
Dmitrii V. Khablov
Keyword(s):  
Intelligent Systems ◽  
Electromagnetic Waves ◽  
Continuous Monitoring ◽  
Phase Changes ◽  
Vibration Diagnostics ◽  
Remote Diagnostics ◽  
Active Vibration ◽  
Dynamic Objects ◽  
Microwave Sensors ◽  
Non Destructive

This paper describes a promising method for non-contact vibration diagnostics based on the use of Doppler microwave sensors. In this case, active irradiation of the object with electromagnetic waves and the allocation of phase changes using two-channel quadrature processing of the received reflected signal are used. The modes of further fine analysis of the resulting signal using spectral or wavelet transformations depending on the nature of the active vibration are considered. The advantages of this non-contact and remote vibration analysis method for the study of complex dynamic objects are described. The convenience of the method for machine learning and use in intelligent systems of non-destructive continuous monitoring of the state of these objects by vibration is noted.

Advanced Statistical Tools for Improving Yield and Reliability

1999 ◽  
Author(s):  
Richard C. Kittler
Keyword(s):  
Data Mining ◽  
Analysis Data ◽  
Neural Nets ◽  
Future Trends ◽  
Statistical Tools ◽  
Mining Technology ◽  
Explanatory Variables ◽  
Data Coverage ◽  
Statistical Approaches ◽  
Adequate Data

Abstract Analysis of manufacturing data as a tool for failure analysts often meets with roadblocks due to the complex non-linear behaviors of the relationships between failure rates and explanatory variables drawn from process history. The current work describes how the use of a comprehensive engineering database and data mining technology over-comes some of these difficulties and enables new classes of problems to be solved. The characteristics of the database design necessary for adequate data coverage and unit traceability are discussed. Data mining technology is explained and contrasted with traditional statistical approaches as well as those of expert systems, neural nets, and signature analysis. Data mining is applied to a number of common problem scenarios. Finally, future trends in data mining technology relevant to failure analysis are discussed.

A MATHEMATICAL TRUST MODEL TO ENSURE IN THE INFORMATION ENVIRONMENT

2020 ◽  
Author(s):  
Мадина Усенбай ◽  
Акмарал Иманбаева
Keyword(s):  
Trust Model ◽  
Time Parameter ◽  
Information Environment ◽  
User Privacy ◽  
Cloud Technology ◽  
Secret Information ◽  
Dynamic Objects

Конфиденциальность является одним из важных параметров для повышения безопасности в сети, цель которого - сохранить секретную информацию. Рассмотрена модель доверия, состоящая из текущих и прошлых оценок на основе репутации объекта в сети. В модели используется параметр времени для защиты конфиденциальности пользователя для статических и динамических объектов, например, в IoT или облачной технологии. Confidentiality is one of the important parameters for increasing security on the network, the coal of which is to keep secret information. A trust model consisting of current and past assessments based on the object reputation in the network is considered. The model uses a time parameter to protect user privacy for static and dynamic objects, for example, in IoT or cloud technology.

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