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- W205815272 abstract "1. Introduction Fuzzyinference models can conduct advanced infer-ence like humans and are applied to many intelligentsystems. In this paper, we propose an on-line learningfuzzyinferencemodel. The proposedmodelexecutesaself-organizing learning with input/output (I/O) dataof the model and their evaluations obtained by trial-and-error of a task. Therefore the model can adaptto environmental changes. We confirm performanceof the model using a mobile robot task simulation.2. Previous works Most of the conventional learning models of fuzzyinference are supervised learning models[1, 2, 3].Hence the models require teacher signals to learn.However, it is difficult to determine teacher sig-nals under an environment with changing. SORNetwork[4] can learn without teacher signals usinga self-organizing approach. The model requires anI/O data and theone-to-one corresponding evaluationwith I/O data, which is obtained by a trial-and-error,at each learning.A robot navigation task[5] aims at moving of a mo-bile robot from start to goal without any collisionswith a wall. The robot is controlled by repeating theinference, and only one evaluation is given after thetask. Therefore, learning data obtained by one trialof a task are a series of I/O data and only one evalua-tion shared among the series; note that there is not aone-to-one correspondence between I/O data and anevaluation. SOR Network can not handle such a task.3. The Proposed Model The proposed model learns using a self-organizingapproach, and can perform on-line learning with onlyone evaluation shared with several I/O data in a trial.Fig.1(a) is the structure of the model. The modelconsists of a fuzzy inference unit and a buffer. Thefuzzy inference unit has some fuzzy rules described inif-then form. This unit infers an action from inputdata and puts out it. I/O data of the fuzzy infer-ence are stored in the buffer during a trial, and areexploited in learning mode with the evaluation value(Fig.1(b)). Evaluation value" @default.
- W205815272 created "2016-06-24" @default.
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- W205815272 date "2005-01-01" @default.
- W205815272 modified "2023-09-26" @default.
- W205815272 title "Self-Organizing On-line Learning Fuzzy Inference Model" @default.
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