Interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Althou...Interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Although IT2 FNNs have more advantages in processing uncertain, incomplete, or imprecise information compared to their type-1 counterparts, a large number of parameters need to be tuned in the IT2 FNNs,which increases the difficulties of their design. In this paper,big bang-big crunch(BBBC) optimization and particle swarm optimization(PSO) are applied in the parameter optimization for Takagi-Sugeno-Kang(TSK) type IT2 FNNs. The employment of the BBBC and PSO strategies can eliminate the need of backpropagation computation. The computing problem is converted to a simple feed-forward IT2 FNNs learning. The adoption of the BBBC or the PSO will not only simplify the design of the IT2 FNNs, but will also increase identification accuracy when compared with present methods. The proposed optimization based strategies are tested with three types of interval type-2 fuzzy membership functions(IT2FMFs) and deployed on three typical identification models. Simulation results certify the effectiveness of the proposed parameter optimization methods for the IT2 FNNs.展开更多
In this paper, a four-layer fuzzy neural network using the Back Propagation （BP） Algorithm and the fuzzy logic was built to study the nonlinear relationships between different physical -chemical factors and the dens...In this paper, a four-layer fuzzy neural network using the Back Propagation （BP） Algorithm and the fuzzy logic was built to study the nonlinear relationships between different physical -chemical factors and the denseness of red tide algae, and to anticipate the denseness of the red tide algae. For the first time, the fuzzy neural network technology was applied to research the prediction of red tide. Compared with BP network and RBF network, the outcome of this method is better.展开更多
The fuzzy logic and neural networks are combined in this paper, setting upthe fuzzy neural network (FNN ) ; meanwhile, the distinct differences and connections between thefuzzy logic and neural network are compared. F...The fuzzy logic and neural networks are combined in this paper, setting upthe fuzzy neural network (FNN ) ; meanwhile, the distinct differences and connections between thefuzzy logic and neural network are compared. Furthermore, the algorithm and structure of the FNN areintroduced. In order to diagnose the faults of nuclear power plant, the FNN is applied to thenuclear power planl, and the intelligence fault diagnostic system of the nuclear power plant isbuilt based on the FNN . The fault symptoms and the possibility of the inverted U-tube breakaccident of steam generator are discussed. In order to test the system' s validity, the invertedU-tube break accident of steam generator is used as an example and many simulation experiments areperformed. The test result shows that the FNN can identify the fault.展开更多
By modeling the decision-making process of garment coordination of fashion designers, a kind of computer-aid garment coordination using fuzzy neural network was propesed. The Takagi Sugeno Fuzzy Neural Network （TSFNN...By modeling the decision-making process of garment coordination of fashion designers, a kind of computer-aid garment coordination using fuzzy neural network was propesed. The Takagi Sugeno Fuzzy Neural Network （TSFNN） is used to learn the knowledge and rules of fashion designers on garment coordination and calculate the garment coordination satisfaction index （GCSI）. The implementation of the computer-aid garment coordination tool is divided into two stages. The first stage is to acquire the knowledge of garment coordination. The second stage is to train and use the fuzzy neural network to conduct garment coordination. Three layers structure were also discussed for developing the system. By applying the computer-aid garment coordination tool into a real fushionretailing store, the experimental results show the system pexforms well with choosing a suitable value for screening out the satisfaction coordination pairs.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
Artificial intelligence techniques have been used to predict basic oxygen furnace(BOF) end-points. However,the main challenge is to effectively reduce the input nodes as too many input nodes in neural network increase...Artificial intelligence techniques have been used to predict basic oxygen furnace(BOF) end-points. However,the main challenge is to effectively reduce the input nodes as too many input nodes in neural network increase complexity,decrease accuracy and slow down the training speed of the network.Simply picking-up variables as input usually influence validity of model.It is quite necessary to develop an effective method to reduce the number of input nodes whereby to simplify the network and improve model performance.In this study,a variable-filtrating technique combining both metallurgical mechanism model and partial least-squares(PLS ) regression method has been proposed by taking the advantages of both of them,i.e.qualitive and quantative relationships between variables respectively.Accordingly,a fuzzy-reasoning neural network(FNN) prediction model for basic oxygen furnace(BOF) end-point carbon content based on this technique has been developed.The prediction results showed that this model can effectively improve the hit rate of end-point carbon content and increase network training speed.The successful hit rate of the model can reach up to 94.12%with about 0.02% error range.展开更多
基金supported by the National Natural Science Foundation of China (61873079,51707050)
文摘Interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Although IT2 FNNs have more advantages in processing uncertain, incomplete, or imprecise information compared to their type-1 counterparts, a large number of parameters need to be tuned in the IT2 FNNs,which increases the difficulties of their design. In this paper,big bang-big crunch(BBBC) optimization and particle swarm optimization(PSO) are applied in the parameter optimization for Takagi-Sugeno-Kang(TSK) type IT2 FNNs. The employment of the BBBC and PSO strategies can eliminate the need of backpropagation computation. The computing problem is converted to a simple feed-forward IT2 FNNs learning. The adoption of the BBBC or the PSO will not only simplify the design of the IT2 FNNs, but will also increase identification accuracy when compared with present methods. The proposed optimization based strategies are tested with three types of interval type-2 fuzzy membership functions(IT2FMFs) and deployed on three typical identification models. Simulation results certify the effectiveness of the proposed parameter optimization methods for the IT2 FNNs.
文摘In this paper, a four-layer fuzzy neural network using the Back Propagation （BP） Algorithm and the fuzzy logic was built to study the nonlinear relationships between different physical -chemical factors and the denseness of red tide algae, and to anticipate the denseness of the red tide algae. For the first time, the fuzzy neural network technology was applied to research the prediction of red tide. Compared with BP network and RBF network, the outcome of this method is better.
文摘The fuzzy logic and neural networks are combined in this paper, setting upthe fuzzy neural network (FNN ) ; meanwhile, the distinct differences and connections between thefuzzy logic and neural network are compared. Furthermore, the algorithm and structure of the FNN areintroduced. In order to diagnose the faults of nuclear power plant, the FNN is applied to thenuclear power planl, and the intelligence fault diagnostic system of the nuclear power plant isbuilt based on the FNN . The fault symptoms and the possibility of the inverted U-tube breakaccident of steam generator are discussed. In order to test the system' s validity, the invertedU-tube break accident of steam generator is used as an example and many simulation experiments areperformed. The test result shows that the FNN can identify the fault.
基金National Natural Science Foundations of China (No.60975059, No.60775052)Specialized Research Fund for the Doctoral Program of Higher Education from Ministry of Education of China (No.20090075110002)Projects of Shanghai Committee of Science and Technology, China (No.09JC1400900, No.08JC1400100, No.10DZ0506500)
文摘By modeling the decision-making process of garment coordination of fashion designers, a kind of computer-aid garment coordination using fuzzy neural network was propesed. The Takagi Sugeno Fuzzy Neural Network （TSFNN） is used to learn the knowledge and rules of fashion designers on garment coordination and calculate the garment coordination satisfaction index （GCSI）. The implementation of the computer-aid garment coordination tool is divided into two stages. The first stage is to acquire the knowledge of garment coordination. The second stage is to train and use the fuzzy neural network to conduct garment coordination. Three layers structure were also discussed for developing the system. By applying the computer-aid garment coordination tool into a real fushionretailing store, the experimental results show the system pexforms well with choosing a suitable value for screening out the satisfaction coordination pairs.
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.
文摘Artificial intelligence techniques have been used to predict basic oxygen furnace(BOF) end-points. However,the main challenge is to effectively reduce the input nodes as too many input nodes in neural network increase complexity,decrease accuracy and slow down the training speed of the network.Simply picking-up variables as input usually influence validity of model.It is quite necessary to develop an effective method to reduce the number of input nodes whereby to simplify the network and improve model performance.In this study,a variable-filtrating technique combining both metallurgical mechanism model and partial least-squares(PLS ) regression method has been proposed by taking the advantages of both of them,i.e.qualitive and quantative relationships between variables respectively.Accordingly,a fuzzy-reasoning neural network(FNN) prediction model for basic oxygen furnace(BOF) end-point carbon content based on this technique has been developed.The prediction results showed that this model can effectively improve the hit rate of end-point carbon content and increase network training speed.The successful hit rate of the model can reach up to 94.12%with about 0.02% error range.
