This paper defines and proves a new closeness degree firstly, and then presents an improved-CFART neural network model, after introducing the closeness degree into the standard fuzzy ART model. This paper also develop...This paper defines and proves a new closeness degree firstly, and then presents an improved-CFART neural network model, after introducing the closeness degree into the standard fuzzy ART model. This paper also develops an information analysis and simulation system based on the recognition of industrial real-time data from field. The results of simulation experiments demonstrate that the CFART model has excellent capability of pattern recognition for dynamic waveform.展开更多
This paper presents a seafloor classification method of multibeam sonar data, based on the use of Adaptive Resonance Theory （ART） neural networks. A general ART-based neural network, Fuzzy ARTMAP, has been proposed ...This paper presents a seafloor classification method of multibeam sonar data, based on the use of Adaptive Resonance Theory （ART） neural networks. A general ART-based neural network, Fuzzy ARTMAP, has been proposed for seafloor classification of multibeam sonar data. An evolutionary strategy was used to generate new training samples near the cluster boundaries of the neural network, therefore the weights can be revised and refined by supervised learning. The proposed method resolves the training problem for Fuzzy ARTMAP neural networks, which are applied to seafloor classification of multibeam sonar data when there are less than adequate ground-troth samples. The results were synthetically analyzed in comparison with the standard Fuzzy ARTMAP network and a conventional Bayesian classifier. The conclusion can be drawn that Fuzzy ARTMAP neural networks combining with GA algorithms can be alternative powerful tools for seafloor classification of multibeam sonar data.展开更多
文摘This paper defines and proves a new closeness degree firstly, and then presents an improved-CFART neural network model, after introducing the closeness degree into the standard fuzzy ART model. This paper also develops an information analysis and simulation system based on the recognition of industrial real-time data from field. The results of simulation experiments demonstrate that the CFART model has excellent capability of pattern recognition for dynamic waveform.
文摘This paper presents a seafloor classification method of multibeam sonar data, based on the use of Adaptive Resonance Theory （ART） neural networks. A general ART-based neural network, Fuzzy ARTMAP, has been proposed for seafloor classification of multibeam sonar data. An evolutionary strategy was used to generate new training samples near the cluster boundaries of the neural network, therefore the weights can be revised and refined by supervised learning. The proposed method resolves the training problem for Fuzzy ARTMAP neural networks, which are applied to seafloor classification of multibeam sonar data when there are less than adequate ground-troth samples. The results were synthetically analyzed in comparison with the standard Fuzzy ARTMAP network and a conventional Bayesian classifier. The conclusion can be drawn that Fuzzy ARTMAP neural networks combining with GA algorithms can be alternative powerful tools for seafloor classification of multibeam sonar data.
