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Learning fuzzy logic from examples
| Title: | Learning fuzzy logic from examples |
| Author: | Aranibar, Luis Alfonso Quiroga |
| Description: | Traditional manufacturing schemes, thereon referred as manufacturing planning, have not been able to provide tools with enough flexibility to adapt to the many constraints of every day production. These approaches are quantitative in nature and their application to control problems fails to perform satisfactorily when the information required to answer a query is non-linear or ill-defined. With the advent of high quality requirements, conventional manufacturing systems have been forced to explore innovative alternatives to cope with the pressure of global competitiveness. Neural Networks and Machine Learning, with their ability to learn from examples, have been proposed early on for solving non-linear control problems adaptively. More recently, Fuzzy Logic has emerged as a promising approach for controlling processes. This work studies the application of Fuzzy Logic through a methodology proposed by Wang and Mendel from the University of Southern California. This methodology is of relevant interest because it provides fuzzy model builders with a unique tool to generate rules from fuzzy sets which combine both numerical and linguistic information. The methodology is explained in detail and is applied to two uncomplicated case studies. The first case study is a typical control problem an it relates to robot motion control. A simulation is developed to obtain various information describing the movement of a two-degree-of-freedom robot arm. The collected pairs are used for the training of the fuzzy controller which is then tested with new data. Although Fuzzy Logic is now quite popular in commercial control applications, there is little reported in the area of manufacturing planning and control. In the second case study, the Wang-Mendel methodology is applied to a basic job shop scheduling problem. Performance of the fuzzy machine is compared against a traditional backpropagation neural network and Quinlan's ID3 machine learning technique. |
| Permanent Link: |
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1176495652
http://hdl.handle.net/2374.OX/14011 |
| Date: | 1994 |
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