In lots of data based prediction or modeling applications,uncertainties and/or noises in the observed data cannot be avoided.In such cases,it is more preferable and reasonable to provide linguistic(fuzzy)predicted res...In lots of data based prediction or modeling applications,uncertainties and/or noises in the observed data cannot be avoided.In such cases,it is more preferable and reasonable to provide linguistic(fuzzy)predicted results described by fuzzy memberships or fuzzy sets instead of the crisp estimates depicted by numbers.Linguistic dynamic system(LDS)provides a powerful tool for yielding linguistic(fuzzy)results.However,it is still difficult to construct LDS models from observed data.To solve this issue,this paper first presents a simplified LDS whose inputoutput mapping can be determined by closed-form formulas.Then,a hybrid learning method is proposed to construct the data-driven LDS model.The proposed hybrid learning method firstly generates fuzzy rules by the subtractive clustering method,then carries out further optimization of centers of the consequent triangular fuzzy sets in the fuzzy rules,and finally adopts multiobjective optimization algorithm to determine the left and right end-points of the consequent triangular fuzzy sets.The proposed approach is successfully applied to three real-world prediction applications which are:prediction of energy consumption of a building,forecasting of the traffic flow,and prediction of the wind speed.Simulation results show that the uncertainties in the data can be effectively captured by the linguistic(fuzzy)estimates.It can also be extended to some other prediction or modeling problems,in which observed data have high levels of uncertainties.展开更多
Linguistic dynamic systems(LDS)are dynamic processes involving computing with words(CW)for modeling and analysis of complex systems.In this paper,a fuzzy neural network(FNN)structure of LDS was proposed.In addition,an...Linguistic dynamic systems(LDS)are dynamic processes involving computing with words(CW)for modeling and analysis of complex systems.In this paper,a fuzzy neural network(FNN)structure of LDS was proposed.In addition,an improved nonlinear particle swarm optimization was employed for training FNN.The experiment results on logistics formulation demonstrates the feasibility and the efficiency of this FNN model.展开更多
基金supported by the National Natural Science Foundation of China(61473176,61773246)the Natural Science Foundation of Shandong Province for Outstanding Young Talents in Provincial Universities(ZR2015JL021)the Taishan Scholar Project of Shandong Province(TSQN201812092)
文摘In lots of data based prediction or modeling applications,uncertainties and/or noises in the observed data cannot be avoided.In such cases,it is more preferable and reasonable to provide linguistic(fuzzy)predicted results described by fuzzy memberships or fuzzy sets instead of the crisp estimates depicted by numbers.Linguistic dynamic system(LDS)provides a powerful tool for yielding linguistic(fuzzy)results.However,it is still difficult to construct LDS models from observed data.To solve this issue,this paper first presents a simplified LDS whose inputoutput mapping can be determined by closed-form formulas.Then,a hybrid learning method is proposed to construct the data-driven LDS model.The proposed hybrid learning method firstly generates fuzzy rules by the subtractive clustering method,then carries out further optimization of centers of the consequent triangular fuzzy sets in the fuzzy rules,and finally adopts multiobjective optimization algorithm to determine the left and right end-points of the consequent triangular fuzzy sets.The proposed approach is successfully applied to three real-world prediction applications which are:prediction of energy consumption of a building,forecasting of the traffic flow,and prediction of the wind speed.Simulation results show that the uncertainties in the data can be effectively captured by the linguistic(fuzzy)estimates.It can also be extended to some other prediction or modeling problems,in which observed data have high levels of uncertainties.
基金National Natural Science Foundation of China(No.60873179)Doctoral Program Foundation of Institutions of Higher Education of China(No.20090121110032)+3 种基金Shenzhen Science and Technology Research Foundations,China(No.JC200903180630A,No.ZYB200907110169A)Key Project of Institutes Serving for the Economic Zone on the Western Coast of the Tai wan Strait,ChinaNatural Science Foundation of Xiamen,China(No.3502Z2093018)Projects of Education Depart ment of Fujian Province of China(No.JK2009017,No.JK2010031,No.JA10196)
文摘Linguistic dynamic systems(LDS)are dynamic processes involving computing with words(CW)for modeling and analysis of complex systems.In this paper,a fuzzy neural network(FNN)structure of LDS was proposed.In addition,an improved nonlinear particle swarm optimization was employed for training FNN.The experiment results on logistics formulation demonstrates the feasibility and the efficiency of this FNN model.
