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基于神经网络的水深插值研究 被引量:9
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作者 施朝健 《中国航海》 CSCD 北大核心 2003年第4期6-10,共5页
提出了利用神经网络技术,对离散的、分布不规则的海图水深数据进行插值处理得到连续水深数值的方法,研究了应用Levenberg Marquardt反向学习网络进行函数逼近和径向基函数网络进行函数插值二种算法,用函数曲面和实际水域数值对算法进行... 提出了利用神经网络技术,对离散的、分布不规则的海图水深数据进行插值处理得到连续水深数值的方法,研究了应用Levenberg Marquardt反向学习网络进行函数逼近和径向基函数网络进行函数插值二种算法,用函数曲面和实际水域数值对算法进行了测试和误差分析,并对实际应用中区域分块、规一化和等深线数据处理等问题作了说明。 展开更多
关键词 神经网络 海图 水深数据 水深插值 反向学习网络 计算方法 误差分析 水路运输 空间插值
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LEARNING ALGORITHM OF STAGE CONTROL NBP NETWORK 被引量:1
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作者 Yan Lixiang Qin Zheng (Xi’an JiaoTong University, Xi’an 710049) 《Journal of Electronics(China)》 2003年第6期467-471,共5页
This letter analyzes the reasons why the known Neural Back Promulgation (NBP)network learning algorithm has slower speed and greater sample error. Based on the analysis and experiment, the training group descending En... This letter analyzes the reasons why the known Neural Back Promulgation (NBP)network learning algorithm has slower speed and greater sample error. Based on the analysis and experiment, the training group descending Enhanced Combination Algorithm (ECA) is proposed.The analysis of the generalized property and sample error shows that the ECA can heighten the study speed and reduce individual error. 展开更多
关键词 Neural Back Promulgation(NBP) network Training group descending Enhanced Combination Algorithm (ECA)
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A biologically inspired model for pattern recognition 被引量:1
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作者 Eduardo GONZALEZ Hans LILJENSTROM +1 位作者 Yusely RUIZ Guang LI 《Journal of Zhejiang University-Science B(Biomedicine & Biotechnology)》 SCIE CAS CSCD 2010年第2期115-126,共12页
In this paper, a novel bionic model and its performance in pattern recognition are presented and discussed. The model is constructed from a bulb model and a three-layered cortical model, mimicking the main features of... In this paper, a novel bionic model and its performance in pattern recognition are presented and discussed. The model is constructed from a bulb model and a three-layered cortical model, mimicking the main features of the olfactory system. The olfactory bulb and cortex models are connected by feedforward and feedback fibers with distributed delays. The Breast Cancer Wisconsin dataset consisting of data from 683 patients divided into benign and malignant classes is used to demonstrate the capacity of the model to learn and recognize patterns, even when these are deformed versions of the originally learned patterns. The performance of the novel model was compared with three artificial neural networks (ANNs), a back-propagation network, a support vector machine classifier, and a radial basis function classifier. All the ANNs and the olfactory bionic model were tested in a benchmark study of a standard dataset. Experimental results show that the bionic olfactory system model can learn and classify patterns based on a small training set and a few learning trials to reflect biological intelligence to some extent. 展开更多
关键词 Olfactory system Neural network Bionic model Pattern recognition
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