Optimization of SAG Control Rule Based on Clustering Learning Algorithm

2021 ◽  
Author(s):  
Guicheng Wang ◽  
Xiaojia Sun ◽  
Chuntao Jia ◽  
Min Zhang ◽  
Jiale Zhu ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Rule Based ◽  
Control Rule

A FILTER PROPOSAL FOR INCLUDING FEATURE CONSTRUCTION IN A GENETIC LEARNING ALGORITHM

2012 ◽  
Vol 20 (supp02) ◽  
pp. 31-49 ◽  
Author(s):  
DAVID GARCIA ◽  
ANTONIO GONZALEZ ◽  
RAUL PEREZ
Keyword(s):  
Learning Algorithm ◽  
A Priori ◽  
Fuzzy Rule ◽  
Feature Construction ◽  
Rule Based ◽  
Filter Model ◽  
Information Measures ◽  
Construction Techniques ◽  
Basic Learning ◽  
Current Proposal

In system identification process often a predetermined set of features is used. However, in many cases it is difficult to know a priori whether the selected features were really the more appropriate ones. This is the reason why the feature construction techniques have been very interesting in many applications. Thus, the current proposal introduces the use of these techniques in order to improve the description of fuzzy rule-based systems. In particular, the idea is to include feature construction in a genetic learning algorithm. The construction of attributes in this study will be restricted to the inclusion of functions defined on the initial attributes of the system. Since the number of functions and the number of attributes can be very large, a filter model, based on the use of information measures, is introduced. In this way, the genetic algorithm only needs to explore the particular new features that may be of greater interest to the final identification of the system. In order to manage the knowledge provided by the new attributes based on the use of functions we propose a new model of rule by extending a basic learning fuzzy rule-based model. Finally, we show the experimental study associated with this work.

A sequential learning algorithm for online constructing belief-rule-based systems

2010 ◽  
Vol 37 (2) ◽  
pp. 1790-1799 ◽  
Author(s):  
Zhi-Jie Zhou ◽  
Chang-Hua Hu ◽  
Jian-Bo Yang ◽  
Dong-Ling Xu ◽  
Mao-Yin Chen ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Sequential Learning ◽  
Rule Based ◽  
Rule Based Systems

Hybrid Module of the IACVIRTUAL Project

2006 ◽  
Vol 10 (4) ◽  
pp. 472-476 ◽  
Author(s):  
Lourdes M. Brasil ◽  
◽  
Jean C. C. Rojas ◽  
Fernando M. de Azevedo ◽  
Carlos W. D. de Almeida ◽  
...  
Keyword(s):  
Expert System ◽  
Input Data ◽  
Learning Algorithm ◽  
Implementation Process ◽  
Surgical Team ◽  
Rule Based ◽  
Hybrid Module ◽  
The Neural Networks ◽  
Definition Of ◽  
Structure Knowledge

This work represents the hybrid module of the IACVIRTUAL meta-environment. In this context we will basically approach the Hybrid Expert System (HES), which is composed by the Neural Networks Based Expert System (NNES) and by the Rule-Based Expert System (RBES). The HES is destined to support the decision of a clinical-surgical team, in the area of cardiology, in the definition of a therapeutic conduct in patients with coronary heart disease. The implementation process starts with the Knowledge Acquisition (KA), which comes from the analysis of a series of clinical parameters, which are used as input data for the NNES. This way, knowledge acquired during elicitation is converted in fuzzy rules. Through these rules, elicitated knowledge is mapped in AND/OR graphs, which then represent the starting structure of the NNES. Learning and optimization of the RBES are made through the Genetic-Backpropagation Based Learning Algorithm (GENBACK). This algorithm can, during the learning process, modify the weight of the connections, as well as the network structure. Knowledge abstracted from the RBES, being already refined, as well as trained and tested, is used to form the Knowledge Base of the RBES.

A FINITE STATE COMMA TAGGER

2004 ◽  
Vol 13 (03) ◽  
pp. 449-468 ◽  
Author(s):  
SEBASTIAN VAN DELDEN ◽  
FERNANDO GOMEZ
Keyword(s):  
Learning Algorithm ◽  
Finite State Automata ◽  
Rule Based ◽  
Domain Specific ◽  
Part Of Speech ◽  
System A ◽  
Finite State ◽  
Rule Based Approach

A method has been developed and implemented that assigns syntactic roles to commas. Text that has been tagged using a part-of-speech tagger serves as the input to the system. A set of Finite State Automata first assigns temporary syntactic roles to each comma in the sentence. A greedy learning algorithm is then used to determine the final syntactic roles of the commas. The system requires no training and is not domain specific. The performance of the system on numerous corpora is given and compared against a rule-based approach.

A tree-based learning approach for document structure analysis and its application to web search

2014 ◽  
Vol 21 (4) ◽  
pp. 569-605 ◽  
Author(s):  
F. CANAN PEMBE ◽  
TUNGA GÜNGÖR
Keyword(s):  
Machine Learning ◽  
Web Search ◽  
Learning Algorithm ◽  
Classification Problem ◽  
Support Vector ◽  
Learning Approach ◽  
Rule Based ◽  
Document Structure ◽  
Rule Based Approach ◽  
Extraction Model

AbstractIn this paper, we study the problem of structural analysis of Web documents aiming at extracting the sectional hierarchy of a document. In general, a document can be represented as a hierarchy of sections and subsections with corresponding headings and subheadings. We developed two machine learning models: heading extraction model and hierarchy extraction model. Heading extraction was formulated as a classification problem whereas a tree-based learning approach was employed in hierarchy extraction. For this purpose, we developed an incremental learning algorithm based on support vector machines and perceptrons. The models were evaluated in detail with respect to the performance of the heading and hierarchy extraction tasks. For comparison, a baseline rule-based approach was used that relies on heuristics and HTML document object model tree processing. The machine learning approach, which is a fully automatic approach, outperformed the rule-based approach. We also analyzed the effect of document structuring on automatic summarization in the context of Web search. The results of the task-based evaluation on TREC queries showed that structured summaries are superior to unstructured summaries both in terms of accuracy and user ratings, and enable the users to determine the relevancy of search results more accurately than search engine snippets.

scholarly journals Implementation of XGBoost Ensemble Learning Model for Detecting Money Laundering

2021 ◽  
Vol 9 (VII) ◽  
pp. 312-316
Author(s):  
Abarna Ramprakash
Keyword(s):  
Machine Learning ◽  
Ensemble Learning ◽  
Money Laundering ◽  
Learning Algorithm ◽  
Learning Model ◽  
False Positives ◽  
Machine Learning Algorithm ◽  
Huge Amount ◽  
Rule Based ◽  
Complex Sequence

Money laundering is the illegal process of concealing the origins of money obtained illegally by passing it through a complex sequence of banking transfers. Currently banks use rule based systems to identify the suspicious transactions which could be used for money laundering. However these systems generate a large number of false positives which leads the banks to spend a huge amount of money and time in investigating the false positives. Hence, in this paper, the monitoring of transactions is to be done using XGBoost machine learning algorithm in order to reduce the number of false positives and to increase the probability of identifying true positives.

Knowledge Acquisition of Self-organizing Systems with Deep Multiagent Reinforcement Learning

2021 ◽  
pp. 1-47
Author(s):  
Hao Ji ◽  
Yan Jin
Keyword(s):  
Reinforcement Learning ◽  
Knowledge Acquisition ◽  
Learning Algorithm ◽  
Task Complexity ◽  
Functional Differentiation ◽  
Simple Task ◽  
Social Rules ◽  
Rule Based ◽  
Multi Agent ◽  
Self Organizing

Abstract Self-organizing systems (SOS) can perform complex tasks in unforeseen situations with adaptability. Previous work has introduced field-based approaches and rule-based social structuring for individual agents to not only comprehend the task situations but also take advantage of the social rule-based agent relations to accomplish their tasks without a centralized controller. Although the task fields and social rules can be predefined for relatively simple task situations, when the task complexity increases and the task environment changes, having a priori knowledge about these fields and the rules may not be feasible. In this paper, a multi-agent reinforcement learning based model is proposed as a design approach to solving the rule generation problem with complex SOS tasks. A deep multi-agent reinforcement learning algorithm was devised as a mechanism to train SOS agents for knowledge acquisition of the task field and social rules. Learning stability, functional differentiation and robustness properties of this learning approach were investigated with respect to the changing team sizes and task variations. Through computer simulation studies of a box-pushing problem, the results have shown that there is an optimal range of number of agents that achieves good learning stability; agents in a team learn to differentiate from other agents with changing team sizes and box dimensions; and the robustness of the learned knowledge shows to be stronger to the external noises than with changing task constraints.

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