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The conventional Takagi-Sugeno (T-S) fuzzy model is an effective tool used to approximating behaviors of nonlinear systems on the basis of precise and certain input and output observations. In some situations, however, we can only obtain mixture of precise data (for input variables), imprecise and uncertain data (for output variable/response). This paper presents a method used to constructing T-S...
Particle Swarm Optimization (PSO) algorithm is widely used to deal with global optimization problems. However, it is easy to be trapped into local optimal and thus usually fall into premature convergence when encountering complicated problems, such as high-dimension and peak optimizations. To solve such problems, we propose a hybrid search strategy, derived by combining a grid searching and stochastic...
This paper proposes a methodology for automatically extracting a convenient version of T-S fuzzy models from data using a novel clustering technique, called variable string length Artificial Bee Colony (ABC) algorithm based fuzzy c-means clustering approach (VABC-FCM). In this methodology, the rule number of the T-S model is automatically determined by the VABC-FCM, without knowing the rule number...
In practice engineering, some process parameters, which are pivotal to enhance the product ability, can not be monitored online and offline. In this paper, two methods are proposed for monitoring the so-called unmeasured parameters such as level of coal powder in power plant. The first model is nonlinear PLS model based on precise training samples, and the second one is evidential soft sensor model...
Automatic target recognition technique, based on the projections in the samples range space, can be availably used in SAR target recognition. It requires that the relativity between the range spaces of different targets should be minimal. However, the relativity between the corresponding range spaces is very big if the different targets have similar appearance and structure, which will considerably...
In this paper, a robust adaptive version of evidence theoretic k-NN classification rule was proposed. In the robust rule, an adaptive distance metric was proposed to be used instead of the Euclidean distance metric. All the parameters brought in by the proposed adaptive distance metric and some other important structural parameters fixed in the original rule are optimized based on training set by...
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