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On-Line Real Time Realization and Application of Adaptive Fuzzy Inference Neural Network
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作者 Han, Jianguo Guo, Junchao Zhao, Qian 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2000年第1期67-74,共8页
In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and... In this paper, a modeling algorithm developed by transferring the adaptive fuzzy inference neural network into an on-line real time algorithm, combining the algorithm with conventional system identification method and applying them to separate identification of nonlinear multi-variable systems is introduced and discussed. 展开更多
关键词 Fuzzy control Identification (control systems) inference engines Learning algorithms Mathematical models Multivariable control systems Neural networks Nonlinear control systems Real time systems
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Modeling and inferring 2.1D sketch with mixed Markov random field
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作者 Anlong Ming Yu Zhou Tianfu Wu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2017年第2期361-373,共13页
This paper presents a method of computing a 2.1D sketch (i.e., layered image representation) from a single image with mixed Markov random field (MRF) under the Bayesian framework. Our model consists of three layers: t... This paper presents a method of computing a 2.1D sketch (i.e., layered image representation) from a single image with mixed Markov random field (MRF) under the Bayesian framework. Our model consists of three layers: the input image layer, the graphical representation layer of the computed 2D atomic regions and 3-degree junctions (such as T or arrow junctions), and the 2.1D sketch layer. There are two types of vertices in the graphical representation of the 2D entities: (i) regions, which act as the vertices found in traditional MRF, and (ii) address variables assigned to the terminators decomposed from the 3-degree junctions, which are a new type of vertices for the mixed MRF. We formulate the inference problem as computing the 2.1D sketch from the 2D graphical representation under the Bayesian framework, which consists of two components: (i) region layering/coloring based on the Swendsen-Wang cuts algorithm, which infers partial occluding order of regions, and (ii) address variable assignments based on Gibbs sampling, which completes the open bonds of the terminators of the 3-degree junctions. The proposed method is tested on the D-Order dataset, the Berkeley segmentation dataset and the Stanford 3D dataset. The experimental results show the efficiency and robustness of our approach. © 2017 Beijing Institute of Aerospace Information. 展开更多
关键词 Graphic methods Image segmentation inference engines Markov processes Structural frames
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Research on Intelligent Decision Support System of Soybean
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作者 Wu Wan-sheng Su Zhong-bin Li Xiao-ming 《Journal of Northeast Agricultural University(English Edition)》 CAS 2013年第2期71-76,共6页
Based on agriculture production knowledge and computer technology, this paper applied database, artificial intelligent, management information system, decision support system, network technology and information integr... Based on agriculture production knowledge and computer technology, this paper applied database, artificial intelligent, management information system, decision support system, network technology and information integration technology to the field of soybean production, and offered decision support to users in imitating the curse during which experts solved the problems, therefore, guided production practice, introduced structure and function of decision support system of soybean, and then we analyzed the design of the knowledge base and the realization of inference engine in details as well. 展开更多
关键词 SOYBEAN decision-making system inference engine
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