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" A Multi-Agent Expert System for Steel Grade Classification Using Adaptive Neuro-fuzzy Systems. "
Mohammad Hossein Fazel Zarandi
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BL
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| Record Number
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790712
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b610743
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| Main Entry
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Mohammad Hossein Fazel Zarandi
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| Title & Author
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A Multi-Agent Expert System for Steel Grade Classification Using Adaptive Neuro-fuzzy Systems.\ Mohammad Hossein Fazel Zarandi
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| Publication Statement
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INTECH Open Access Publisher, 2010
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| ISBN
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9533070323
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: 9789533070322
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| Abstract
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Iron and steel manufacturing is a crucial basic industry for most of the industrial activities. The influence of an efficient process control on the cost and energy reduction has made the process control one of the main issues of this industry. Iron and steel manufacturing should mainly rely on the new integrated production processes to improve productivity, reduce energy consumption, and maintain competitiveness in the market. In the most steel companies, the principal production planning and scheduling techniques are essentially manual techniques with little computerized decision support. These manual techniques are mainly based on the know-how and the experiences of those experts who have worked in the plant for years. Moreover, steel production is a multi-stage process, logically and geographically distributed, involving a variety of production processes. Also, in a steel grade classification, an operator has to determine the amount of additive materials in steel-making process. Because of the above reasons, a steel automation system is needed to represent distribution and integration existing in this industry. A fuzzy multi-agent expert system can enable such capabilities. This chapter proposes a multi-agent expert system includes three different types of agents: Initiator Agent: Provides the initial membership functions and cluster centers for the clustering agent. Clustering Agent: Produces the initial cluster centers for training of the ANFIS agents ANFIS Agents: By using ANFIS we can refine fuzzy if-then rules obtained from human expert to describe the input-output behaviour of a complex system. However, if human expertise is not available we can still set up reasonable membership functions and start the learning process to generate a set up fuzzy if-then rules to approximate a desired data set. The results show that the proposed system can identify the amounts of the additives for different classes of steel grade. Also the results show that the Multi-agent expert systems can be applied effectively in the steel-making.
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Open Access Collection.
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| LC Classification
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TP182.M643 2010
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| Added Entry
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Behnam Ganji
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Milad Avazbeigi
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Mohammad Hassan Anssari
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Mohammad Hossein Fazel Zarandi
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