Multitask Fuzzy Learning with Rule Weight

2013 ◽  
Vol 774-776 ◽  
pp. 1883-1886
Author(s):  
Zhen Xing Li ◽  
Wei Hua Li
Keyword(s):  
Error Correction ◽  
Membership Function ◽  
Fuzzy Set ◽  
Multitask Learning ◽  
Learning System ◽  
Experimental Results ◽  
Learning Machines ◽  
Fuzzy Learning

In fuzzy learning system based on rule weight, certainty grade, denoted by membership function of fuzzy set, defines how close a rule to a classification. In this system, several rules can correspond to same classification. But it cannot reflect the changing while training several tasks simultaneously. In this paper, we propose multitask fuzzy learning based on error-correction, and define belonging grade to show how much a sample belongs to a rule. Experimental results demonstrate efficiency of multitask fuzzy learning, and multitask learning could help to improve learning machines prediction.

Representing a probabilistic linguistic term set with an interval type-2 fuzzy set and the application in green supplier selection

2021 ◽  
pp. 1-18
Author(s):  
Le Jiang ◽  
Hongbin Liu
Keyword(s):  
Decision Making ◽  
Membership Function ◽  
Fuzzy Set ◽  
Supplier Selection ◽  
Symbolic Model ◽  
Green Supplier Selection ◽  
Linguistic Term ◽  
Interval Type ◽  
Decision Making Model

The use of probabilistic linguistic term sets (PLTSs) means the process of computing with words. The existing methods computing with PLTSs mainly use symbolic model. To provide a semantic model for computing with PLTSs, we propose to represent a PLTS by using an interval type-2 fuzzy set (IT2FS). The key step is to compute the footprint of uncertainty of the IT2FS. To this aim, the upper membership function is computed by aggregating the membership functions of the linguistic terms contained in the PLTS, and the lower membership function is obtained by moving the upper membership function downward with the step being total entropy of the PLTS. The comparison rules, some operations, and an aggregation operator for PLTSs are introduced. Based on the proposed method of computing with PLTSs, a multi-criteria group decision making model is introduced. The proposed decision making model is then applied in green supplier selection problem to show its feasibility.

scholarly journals Trust between Humans and Learning Machines: Developing the Gray Box

2017 ◽  
Vol 139 (06) ◽  
pp. S9-S13 ◽  
Author(s):  
James C. Christensen ◽  
Joseph B. Lyons
Keyword(s):  
Situation Awareness ◽  
Task Allocation ◽  
Intelligent Agent ◽  
Semantic Space ◽  
Learning Systems ◽  
Learning System ◽  
Sufficient Information ◽  
Learning Technology ◽  
Learning Machines ◽  
Potential Impact

This article explores the notion of the ‘Gray Box’ to symbolize the idea of providing sufficient information about the learning technology to establish trust. The term system is used throughout this article to represent an intelligent agent, robot, or other form of automation that possesses both decision initiative and authority to act. The article also discusses a proposed and tested Situation Awareness-based Agent Transparency (SAT) model, which posits that users need to understand the system’s perception, comprehension, and projection of a situation. One of the key challenges is that a learning system may adopt behavior that is difficult to understand and challenging to condense to traditional if-then statements. Without a shared semantic space, the system will have little basis for communicating with the human. One of the key recommendations of this article is that there is a need to provide learning systems with transparency as to the state of the human operator, including their momentary capabilities and potential impact of changes in task allocation and teaming approach.

scholarly journals Remote Judgment Method of Painting Image Style Plagiarism Based on Wireless Network Multitask Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Zhijun Wang
Keyword(s):  
Wireless Network ◽  
Kernel Function ◽  
Digital Image ◽  
Learning Algorithm ◽  
Deep Level ◽  
Multitask Learning ◽  
Experimental Results ◽  
Spatial Consistency ◽  
Discrimination Rate ◽  
Edge Sampling

Since the artistry of the work cannot be accurately described, the identification of reproducible plagiarism is more difficult. The identification of reproducible plagiarism of digital image works requires in-depth research on the artistry of artistic works. In this paper, a remote judgment method for plagiarism of painting image style based on wireless network multitask learning is proposed. According to this new method, the uncertainty of painting image samples is removed based on multitask learning algorithm edge sampling. The deep-level details of the painting image are extracted through the multitask classification kernel function, and most of the pixels in the image are eliminated. When the clustering density is greater than the judgment threshold, it can be considered that the two images have spatial consistency. It can also be judged based on this that the two images are similar, that is, there is plagiarism in the painting. The experimental results show that the discrimination rate is always close to 100%, the misjudgment rate of plagiarism of painting images has been reduced, and the various indicators in the discrimination process are the lowest, which fully shows that a very satisfactory discrimination result can be obtained.

scholarly journals Integration of Convolutional Neural Network and Error Correction for Indoor Positioning

2020 ◽  
Vol 9 (2) ◽  
pp. 74
Author(s):  
Eric Hsueh-Chan Lu ◽  
Jing-Mei Ciou
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Error Correction ◽  
Spatial Information ◽  
Rapid Development ◽  
Indoor Positioning ◽  
Experimental Results ◽  
Global Navigation Satellite Systems ◽  
Signal Interference ◽  
Position Vector

With the rapid development of surveying and spatial information technologies, more and more attention has been given to positioning. In outdoor environments, people can easily obtain positioning services through global navigation satellite systems (GNSS). In indoor environments, the GNSS signal is often lost, while other positioning problems, such as dead reckoning and wireless signals, will face accumulated errors and signal interference. Therefore, this research uses images to realize a positioning service. The main concept of this work is to establish a model for an indoor field image and its coordinate information and to judge its position by image eigenvalue matching. Based on the architecture of PoseNet, the image is input into a 23-layer convolutional neural network according to various sizes to train end-to-end location identification tasks, and the three-dimensional position vector of the camera is regressed. The experimental data are taken from the underground parking lot and the Palace Museum. The preliminary experimental results show that this new method designed by us can effectively improve the accuracy of indoor positioning by about 20% to 30%. In addition, this paper also discusses other architectures, field sizes, camera parameters, and error corrections for this neural network system. The preliminary experimental results show that the angle error correction method designed by us can effectively improve positioning by about 20%.

A Fuzzy Adaptive K-SVD Dictionary Algorithm for Face Recogntion

2013 ◽  
Vol 347-350 ◽  
pp. 3797-3803 ◽  
Author(s):  
Xiao Ning Song ◽  
Zi Liu
Keyword(s):  
Iterative Method ◽  
Fuzzy Set ◽  
Sparse Coding ◽  
Sparse Representations ◽  
Experimental Results ◽  
Sparse Signal ◽  
Signal Representations ◽  
The Given ◽  
Class Assignment ◽  
Fuzzy Adaptive

Sparse representations using overcomplete dictionaries has concentrated mainly on the study of pursuit algorithms that decompose signals with respect to a given dictionary. Designing dictionaries to better fit the above model can be done by either selecting one from a prespecified set of linear transforms or adapting the dictionary to a set of training signals. The K-SVD algorithm is an iterative method that alternates between sparse coding of the examples based on the current dictionary and a process of updating the dictionary atoms to better fit the data. However, the existing K-SVD algorithm is employed to dwell on the concept of a binary class assignment meaning that the multi-classes samples are assigned to the given classes definitely. The work proposed in this paper provides a novel fuzzy adaptive way to adapting dictionaries in order to achieve the fuzzy sparse signal representations, the update of the dictionary columns is combined with an update of the sparse representations by incorporated a new mechanism of fuzzy set, which is called fuzzy K-SVD. Experimental results conducted on the ORL and Yale face databases demonstrate the effectiveness of the proposed method.

scholarly journals Learning Effect of Implicit Learning in Joining-in-type Robot-assisted Language Learning System

2019 ◽  
Vol 14 (02) ◽  
pp. 105
Author(s):  
AlBara Khalifa ◽  
Tsuneo Kato ◽  
Seiichi Yamamoto
Keyword(s):  
Language Learning ◽  
Implicit Learning ◽  
Learning Style ◽  
Communication Skills ◽  
Corrective Feedback ◽  
Learning System ◽  
Experimental Results ◽  
Linguistic Knowledge ◽  
Robot Assisted ◽  
Positive Effects

The introduction of robots into language learning systems has been highly useful, especially in motivating learners to engage in the learning process and in letting human learners converse in more realistic conversational situations. This paper describes a novel robot-assisted language learning system that induces the human learner into a triad conversation with two robots through which he or she improves practical communication skills in various conversational situations. The system applies implicit learning as the main learning style for conveying linguistic knowledge, in an indirect way, through conversations on several topics. A series of experiments was conducted using 80 recruited participants to evaluate the effect of implicit learning and the retention effect in a joining-in-type robot-assisted language learning system. The experimental results show positive effects of implicit learning and repetitive learning in general. Based on these experimental results, we propose an improved method, integrating implicit learning and tutoring with corrective feedback in an adaptive way, to increase performance in practical communication skills even for a wide variety of proficiency of L2 learners.

Logistics Demand Forecasting with Asymmetry-Width Probabilistic Fuzzy Logic System

2013 ◽  
Vol 706-708 ◽  
pp. 2012-2016
Author(s):  
Zhong Wei Wang ◽  
Li Xin Lu
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Membership Function ◽  
Fuzzy Set ◽  
Demand Forecasting ◽  
Fuzzy Logic System ◽  
The Asymmetry ◽  
Logistics Demand ◽  
Better Than ◽  
Logic System

There are a lot of approaches in logistics demand forecasting field and perform different characters. The probabilistic fuzzy set (PFS) and probabilistic fuzzy logic system is designed for handling the uncertainties in both stochastic and nonstochastic nature. In this paper, an asymmetric probabilistic fuzzy set is proposed by randomly varying the width of asymmetric Gaussian membership function. And the related PFLS is constructed to be applied to a logistics demand forecasting. The performance discloses that the asymmetry-width probabilistic fuzzy set performs better than precious symmetric one. It is because the asymmetric probabilistic fuzzy sets variability and malleability is higher than this of the symmetric probabilistic fuzzy set.

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