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The Fuzzy Modeling Algorithm for Complex Systems Based on Stochastic Neural Network
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作者 李波 张世英 李银惠 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2002年第3期46-51,共6页
A fuzzy modeling method for complex systems is studied. The notation of general stochastic neural network (GSNN) is presented and a new modeling method is given based on the combination of the modified Takagi and Suge... A fuzzy modeling method for complex systems is studied. The notation of general stochastic neural network (GSNN) is presented and a new modeling method is given based on the combination of the modified Takagi and Sugeno's (MTS) fuzzy model and one-order GSNN. Using expectation-maximization(EM) algorithm, parameter estimation and model selection procedures are given. It avoids the shortcomings brought by other methods such as BP algorithm, when the number of parameters is large, BP algorithm is still difficult to apply directly without fine tuning and subjective tinkering. Finally, the simulated example demonstrates the effectiveness. 展开更多
关键词 Complex system modeling General stochastic neural network MTS fuzzy model Expectation-maximization algorithm
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A multi-input and multi-output design on automotive engine management system
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作者 翟禹嘉 孙研 +1 位作者 钱科军 LEE Sang-hyuk 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第12期4687-4692,共6页
Lookup table is widely used in automotive industry for the design of engine control units(ECU).Together with a proportional-integral controller,a feed-forward and feedback control scheme is often adopted for automotiv... Lookup table is widely used in automotive industry for the design of engine control units(ECU).Together with a proportional-integral controller,a feed-forward and feedback control scheme is often adopted for automotive engine management system(EMS).Usually,an ECU has a structure of multi-input and single-output(MISO).Therefore,if there are multiple objectives proposed in EMS,there would be corresponding numbers of ECUs that need to be designed.In this situation,huge efforts and time were spent on calibration.In this work,a multi-input and multi-out(MIMO) approach based on model predictive control(MPC) was presented for the automatic cruise system of automotive engine.The results show that the tracking of engine speed command and the regulation of air/fuel ratio(AFR) can be achieved simultaneously under the new scheme.The mean absolute error(MAE) for engine speed control is 0.037,and the MAE for air fuel ratio is 0.069. 展开更多
关键词 neural network spark-ignition engine dynamical system modeling system identification multi-input and mult-output(MIMO) control system
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Equivalency and unbiasedness of grey prediction models 被引量:4
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作者 Bo Zeng Chuan Li +1 位作者 Guo Chen Xianjun Long 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2015年第1期110-118,共9页
In order to deeply research the structure discrepancy and modeling mechanism among different grey prediction models, the equivalence and unbiasedness of grey prediction models are analyzed and verified. The results sh... In order to deeply research the structure discrepancy and modeling mechanism among different grey prediction models, the equivalence and unbiasedness of grey prediction models are analyzed and verified. The results show that all the grey prediction models that are strictly derived from x^(0)(k) +az^(1)(k) = b have the identical model structure and simulation precision. Moreover, the unbiased simulation for the homogeneous exponential sequence can be accomplished. However, the models derived from dx^(1)/dt + ax^(1)= b are only close to those derived from x^(0)(k) + az^(1)(k) = b provided that |a| has to satisfy|a| 0.1; neither could the unbiased simulation for the homogeneous exponential sequence be achieved. The above conclusions are proved and verified through some theorems and examples. 展开更多
关键词 system modeling grey prediction models equivalency and unbiasedness
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