A plastic self-adaptive learning machine for pattern recognition

2003 ◽  
Author(s):  
V.G. Kaburlasos ◽  
E.C. Tacker ◽  
D.D. Egbert
Keyword(s):  
Pattern Recognition ◽  
Adaptive Learning ◽  
Learning Machine ◽  
Self Adaptive

scholarly journals A Negotiation Optimization Strategy of Collaborative Procurement with Supply Chain Based on Multi-Agent System

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Chouyong Chen ◽  
Chao Xu
Keyword(s):  
Machine Learning ◽  
Adaptive Learning ◽  
Learning Effect ◽  
Multi Agent System ◽  
Optimization Strategy ◽  
Agent System ◽  
Negotiation Model ◽  
Multi Agent ◽  
Learning Machine ◽  
Self Adaptive

In the process of collaborative procurement, buyers and suppliers are prone to conflict in cooperation due to differences in needs and preferences. Negotiation is a crucial way to resolve the conflict. Aimed at ameliorating the situations of underdeveloped self-adaptive learning effect of current collaborative procurement negotiation, this paper constructs a negotiation model based on multi-agent system and proposes a negotiation optimization strategy combined with machine learning. It provides a novel perspective for the analysis of intelligent SCM. The experimental results suggest that the proposed strategy improves the success rate of self-adaptive learning and joint utility of agents compared with the strategy of single learning machine, and it achieves win-win cooperation between purchasing enterprise and supplier.

scholarly journals Research on Pattern Recognition Method of Blockage Signal in Pipeline Based on LMD Information Entropy and ELM

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Jingzong Yang ◽  
Xiaodong Wang ◽  
Zao Feng ◽  
Guoyong Huang
Keyword(s):  
Pattern Recognition ◽  
Impulse Response ◽  
Information Entropy ◽  
Operating Conditions ◽  
Support Vector ◽  
Entropy Theory ◽  
Pattern Recognition Method ◽  
Response Signal ◽  
Recognition Method ◽  
Learning Machine

Aiming at the nonstationary and nonlinear characteristics of acoustic impulse response signal in pipeline blockage and the difficulty in identifying the different degrees of blockage, this paper proposed a pattern recognition method based on local mean decomposition (LMD), information entropy theory, and extreme learning machine (ELM). Firstly, the impulse response signals of pipeline extracted in different operating conditions were decomposed with LMD method into a series of product functions (PFs). Secondly, based on the information entropy theory, the appropriate energy entropy, singular spectrum entropy, power spectrum entropy, and Hilbert spectrum entropy were extracted as the input feature vectors. Finally, ELM was introduced for classification of pipeline blockage. Through the analysis of acoustic impulse response signal collected under the condition of health and different degrees of blockages in pipeline, the results show that the proposed method can well characterize the state information. Also, it has a great advantage in terms of accuracy and it is time consuming when compared with the support vector machine (SVM) and BP (backpropagation) model.

SELF-ADAPTIVE LEARNING CLASSIFIER SYSTEM

2010 ◽  
Vol 19 (01) ◽  
pp. 275-296 ◽  
Author(s):  
OLGIERD UNOLD
Keyword(s):  
Mutation Rate ◽  
Adaptive Learning ◽  
Adaptive Mutation ◽  
Learning Classifier System ◽  
Classifier System ◽  
Learning Classifier ◽  
Adaptive Parameters ◽  
Proposed Model ◽  
Fixed Parameter ◽  
Self Adaptive

This article introduces a new kind of self-adaptation in discovery mechanism of learning classifier system XCS. Unlike the previous approaches, which incorporate self-adaptive parameters in the representation of an individual, proposed model evolves competitive population of the reduced XCSs, which are able to adapt both classifiers and genetic parameters. The experimental comparisons of self-adaptive mutation rate XCS and standard XCS interacting with 11-bit, 20-bit, and 37-bit multiplexer environment were provided. It has been shown that adapting the mutation rate can give an equivalent or better performance to known good fixed parameter settings, especially for computationally complex tasks. Moreover, the self-adaptive XCS is able to solve the problem of inappropriate for a standard XCS parameters.

scholarly journals Adaptive Learning Mode of a Multimedia-based "English Literature" Learning System

2017 ◽  
Vol 12 (01) ◽  
pp. 71 ◽  
Author(s):  
Jie Mei ◽  
Yanhong Guo ◽  
Xiaokun Li
Keyword(s):  
Adaptive Learning ◽  
English Literature ◽  
Learning System ◽  
The Self ◽  
Independent Learning ◽  
Learning Ability ◽  
Teaching Model ◽  
Learning Situation ◽  
Teaching Pattern ◽  
Self Adaptive

In this paper, a multimedia-based English pronunciation learning system was designed. On this basis, a self-adaptive learning mode which consists of the teaching mode and the independent learning mode was proposed. The self-adaptive teaching model uses corpus technology and covers the exploratory “3I” (Illustration-Interaction-Induction) teaching model, thereby changing the traditional teaching pattern of “spoon-feeding”; when it comes to the independent learning mode, the self-adaptive system can automatically set corresponding learning tasks according to the learning situation of students, to improve the autonomy and differences of students’ self-learning. At the same time, the approach of comparative teaching was especially adopted to test the validity of this system and the learning mode. Specifically, the exquisite course of “English Literature” for students of Grade 2015 majoring in English was selected as the experimental group, to compare with the learning situation of their counterparts of Grade 2014 in the last year. The results show that the learning mode is remarkable in its teaching practicality, could bring a significant effect on improving teaching efficiency and students’ independent learning ability, and enjoys a high research value and a promising application prospect.

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