Application of evolutionary algorithm in optimizing the fuzzy rule base for nonlinear system modeling and control

Shill, Pintu Chandra; Akhand, M. A. H.; Das, Shila Rani; Paul, Aruna

doi:10.1109/iccce.2010.5556820

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…That we choose the input combination to some extreme will not help the equipment operation itself, and is not conducive to energy conservation and environmental protection, although we can achieve our control targets. We need to consider the role of various factors to achieve better control quality [2,3] in intelligent system control. According to the importance of the control outputs, we can translate a multiobjective problem into a problem of multilevel control objectives.…”

Section: Introductionmentioning

confidence: 99%

Optimization analysis of input combinations for the multivariable systems

Zhou

Hong-xia

2014

2014 IEEE International Conference on Control Science and Systems Engineering

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There are a lot of input combinations which lead to the same main outputs in multivariable systems. Taking it all round, we can find that the superiority of different combinations are usually different [1]. To get better combinations, from the static and dynamic perspectives in this paper, we present two multiobjective optimization models for the inputs combinations that lead to the same main outputs. And we can get some optimal combinations in the Pareto sense from the models. The Pareto optimal input combinations can make system outputs get more range that is dynamic in the control process. Finally, we suggest a condition and a method that can transfer input combinations smoothly from the ones with straitness dynamic range to the ones with wide dynamic range.

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Section: Introductionmentioning

confidence: 99%

Optimization analysis of input combinations for the multivariable systems

Zhou

Hong-xia

2014

2014 IEEE International Conference on Control Science and Systems Engineering

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“…This effort will enable to raise the prediction accuracy on electricity consumption by optimizing Sugeno fuzzy method than using ordinary regression. In its application evolution algorithm optimization strategies proven to finish on fuzzy sets and can improve the performance better than fuzzy method [15].…”

Section: Introductionmentioning

confidence: 99%

Rule Optimization of Fuzzy Inference System Sugeno using Evolution Strategy for Electricity Consumption Forecasting

Santika¹,

Mahmudy²,

Naba³

2017

IJECE

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The need for accurate load forecasts will increase in the future because of the dramatic changes occurring in the electricity consumption. Sugeno fuzzy inference system (FIS) can be used for short-term load forecasting. However, challenges in the electrical load forecasting are the data used the data trend. Therefore, it is difficult to develop appropriate fuzzy rules for Sugeno FIS. This paper proposes Evolution Strategy method to determine appropriate rules for Sugeno FIS that have minimum forecasting error. Root Mean Square Error (RMSE) is used to evaluate the goodness of the forecasting result. The numerical experiments show the effectiveness of the proposed optimized Sugeno FIS for several test-case problems. The optimized Sugeno FIS produce lower RMSE comparable to those achieved by other well-known method in the literature.

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Application of evolutionary algorithm in optimizing the fuzzy rule base for nonlinear system modeling and control

Cited by 2 publications

References 8 publications

Optimization analysis of input combinations for the multivariable systems

Optimization analysis of input combinations for the multivariable systems

Rule Optimization of Fuzzy Inference System Sugeno using Evolution Strategy for Electricity Consumption Forecasting

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