Associative Learning, Adaptive Pattern Recognition, And Cooperative-Competitive Decision Making By Neural Networks

1986 ◽  
Author(s):  
Gail A. Carpenter ◽  
Stephen Grossberg
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Pattern Recognition ◽  
Associative Learning ◽  
Adaptive Pattern

A Probabilistic Neural Network-Based Module for Recognition of Objects from their 3-D Images

2013 ◽  
Vol 2 (2) ◽  
pp. 66-79 ◽  
Author(s):  
Onsy A. Abdel Alim ◽  
Amin Shoukry ◽  
Neamat A. Elboughdadly ◽  
Gehan Abouelseoud
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Pattern Recognition ◽  
Object Recognition ◽  
Probabilistic Neural Network ◽  
Training Procedure ◽  
Generalization Capability ◽  
Mine Detection ◽  
Recognition Of Objects

In this paper, a pattern recognition module that makes use of 3-D images of objects is presented. The proposed module takes advantage of both the generalization capability of neural networks and the possibility of manipulating 3-D images to generate views at different poses of the object that is to be recognized. This allows the construction of a robust 3-D object recognition module that can find use in various applications including military, biomedical and mine detection applications. The paper proposes an efficient training procedure and decision making strategy for the suggested neural network. Sample results of testing the module on 3-D images of several objects are also included along with an insightful discussion of the implications of the results.

scholarly journals Modelling and simulation of artificial neural networks using software SIMBRAIN

2021 ◽  
Vol 7 ◽  
pp. 71-81
Author(s):  
Yoana Ivanova
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Morphological Analysis ◽  
Critical Infrastructure ◽  
Modelling And Simulation ◽  
Advanced Technology ◽  
Artificial Neural ◽  
And Robotics

This paper is considered to be a continuation of a previous publication devoted to tendencies in the applications of advanced technology solutions to strengthen the cybersecurity of critical infrastructure (Yearbook Telecommunications, vol. 6, 2019). The specificity of the research is related to tracing the evolution of artificial neural networks (ANN) from their establishment to their modelling and simulation. The theoretical framework involves a well-supported rationale by some practical examples of advanced methods of design and simulation of ANN using SIMBRAIN. These methods are applicable in Cognitive science and Robotics because of their contribution to scientific researches related to study of perceptions and behaviors, abilities of decision making, pattern recognition and morphological analysis and etc.

scholarly journals The role of expert evaluation for microsleep detection

2015 ◽  
Vol 1 (1) ◽  
pp. 92-95
Author(s):  
M. Golz ◽  
A. Schenka ◽  
D. Sommer ◽  
B. Geißler ◽  
A. Muttray
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Driving Simulation ◽  
Support Vector ◽  
Expert Evaluation ◽  
Simulation Studies ◽  
Independent Information ◽  
Vector Machines ◽  
Adaptive Pattern

AbstractRecently, it has been shown by overnight driving simulation studies that microsleep density is the only known sleepiness indicator which rapidly increases within a few seconds immediately before sleepiness related crashes. This indicator is based solely on EEG and EOG and subsequent adaptive pattern recognition. Accurate microsleep recognition is very important for the performance of this sleepiness indicator. The question is whether expensive evaluations of microsleep events by a) experts are necessary or b) non-experts provide sufficient evaluations. Based on 11,114 microsleep events in case a) and 12,787 in case b) recognition accuracies were investigated utilizing (i) artificial neural networks and (ii) support-vector machines. Cross validated classification accuracies ranged between 92.2 % for (i,b) and 99.3 % for (ii, a). It is concluded that expert evaluations are very important to provide independent information for detecting microsleep.

Hybrid Intelligent Diagnosis Approach Based On Neural Pattern Recognition and Fuzzy Decision-Making

2012 ◽  
pp. 444-466
Author(s):  
Amine Chohra ◽  
Nadia Kanaoui ◽  
Véronique Amarger ◽  
Kurosh Madani
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Pattern Recognition ◽  
Fuzzy Logic ◽  
Decision Support ◽  
Fault Diagnosis ◽  
Soft Computing ◽  
Industrial Applications ◽  
Suggested Approach ◽  
Intelligent Diagnosis

Fault diagnosis is a complex and fuzzy cognitive process, and soft computing methods and technologies based on Neural Networks (NN) and Fuzzy Logic (FL), have shown great potential in the development of Decision Support Systems (DSS). Dealing with expert (human) knowledge consideration, Computer Aided Diagnosis (CAD) dilemma is one of the most interesting, but also one of the most difficult problems. Among difficulties contributing to challenging nature of this problem, one can mention the need of fine pattern recognition (classification) and decision-making. This Chapter deals with classification and decision-making based on Artificial Intelligence using multiple model approaches under soft computing implying modular Neural Networks (NN) and Fuzzy Logic (FL) for biomedical and industrial applications. The aim of this Chapter is absolutely not to replace specialized human but to suggest decision support tools: hybrid intelligent diagnosis systems with a satisfactory reliability degree for CAD. In this Chapter, a methodology is given in order to design hybrid intelligent diagnosis systems for a large field of biomedical and industrial applications. For this purpose, first, a survey on diagnosis tasks in such applications is presented. Second, fault diagnosis systems are presented. Third, the main steps of hybrid intelligent diagnosis systems are developed, for each step emphasizing problems and suggesting solutions able to ensure the design of hybrid intelligent diagnosis systems with a satisfactory reliability degree. In fact, the main steps discussed are knowledge representation, classification, classifier issued information fusion, and decision-making. Then, the suggested approach is developed for a CAD in biomedicine, from Auditory Brainstem Response (ABR) test, and the prototype design and experimental results are presented. Finally, a discussion is given with regard to the reliability and large application field of the suggested approach.

Hybrid Intelligent Diagnosis Approach Based On Neural Pattern Recognition and Fuzzy Decision-Making

2011 ◽  
pp. 372-394
Author(s):  
Amine Chohra ◽  
Nadia Kanaoui ◽  
Véronique Amarger ◽  
Kurosh Madani
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Pattern Recognition ◽  
Fuzzy Logic ◽  
Decision Support ◽  
Fault Diagnosis ◽  
Soft Computing ◽  
Industrial Applications ◽  
Suggested Approach ◽  
Intelligent Diagnosis

Fault diagnosis is a complex and fuzzy cognitive process, and soft computing methods and technologies based on Neural Networks (NN) and Fuzzy Logic (FL), have shown great potential in the development of Decision Support Systems (DSS). Dealing with expert (human) knowledge consideration, Computer Aided Diagnosis (CAD) dilemma is one of the most interesting, but also one of the most difficult problems. Among difficulties contributing to challenging nature of this problem, one can mention the need of fine pattern recognition (classification) and decision-making. This Chapter deals with classification and decision-making based on Artificial Intelligence using multiple model approaches under soft computing implying modular Neural Networks (NN) and Fuzzy Logic (FL) for biomedical and industrial applications. The aim of this Chapter is absolutely not to replace specialized human but to suggest decision support tools: hybrid intelligent diagnosis systems with a satisfactory reliability degree for CAD. In this Chapter, a methodology is given in order to design hybrid intelligent diagnosis systems for a large field of biomedical and industrial applications. For this purpose, first, a survey on diagnosis tasks in such applications is presented. Second, fault diagnosis systems are presented. Third, the main steps of hybrid intelligent diagnosis systems are developed, for each step emphasizing problems and suggesting solutions able to ensure the design of hybrid intelligent diagnosis systems with a satisfactory reliability degree. In fact, the main steps discussed are knowledge representation, classification, classifier issued information fusion, and decision-making. Then, the suggested approach is developed for a CAD in biomedicine, from Auditory Brainstem Response (ABR) test, and the prototype design and experimental results are presented. Finally, a discussion is given with regard to the reliability and large application field of the suggested approach.

scholarly journals An empirical analysis of Brazilian courts law documents using learning techniques

2019 ◽  
Author(s):  
Bruno Silva ◽  
Marjory Da Costa-Abreu
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Pattern Recognition ◽  
Radial Basis Functions ◽  
Empirical Analysis ◽  
Basis Functions ◽  
Multi Layer Perceptron ◽  
Criminal Courts ◽  
Learning Techniques ◽  
Data Organisation

This paper describes a survey on investigating judicial data to find patterns and relations between crime attributes and corresponding decisions made by courts, aiming to find import directions that interpretation of the law might be taking. We have developed an initial methodology and experimentation to look for behaviour patterns to build judicial sentences in the scope of Brazilian criminal courts and achieved results related to important trends in decision making. Neural networks-based techniques were applied for classification and pattern recognition, based on Multi-Layer Perceptron and Radial-basis Functions, associated with data organisation techniques and behavioral modalities extraction.

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