sustained
发表于 2025-3-21 18:36:32
书目名称Artificial Neural Nets and Genetic Algorithms影响因子(影响力)<br> http://impactfactor.cn/2024/if/?ISSN=BK0162617<br><br> <br><br>书目名称Artificial Neural Nets and Genetic Algorithms影响因子(影响力)学科排名<br> http://impactfactor.cn/2024/ifr/?ISSN=BK0162617<br><br> <br><br>书目名称Artificial Neural Nets and Genetic Algorithms网络公开度<br> http://impactfactor.cn/2024/at/?ISSN=BK0162617<br><br> <br><br>书目名称Artificial Neural Nets and Genetic Algorithms网络公开度学科排名<br> http://impactfactor.cn/2024/atr/?ISSN=BK0162617<br><br> <br><br>书目名称Artificial Neural Nets and Genetic Algorithms被引频次<br> http://impactfactor.cn/2024/tc/?ISSN=BK0162617<br><br> <br><br>书目名称Artificial Neural Nets and Genetic Algorithms被引频次学科排名<br> http://impactfactor.cn/2024/tcr/?ISSN=BK0162617<br><br> <br><br>书目名称Artificial Neural Nets and Genetic Algorithms年度引用<br> http://impactfactor.cn/2024/ii/?ISSN=BK0162617<br><br> <br><br>书目名称Artificial Neural Nets and Genetic Algorithms年度引用学科排名<br> http://impactfactor.cn/2024/iir/?ISSN=BK0162617<br><br> <br><br>书目名称Artificial Neural Nets and Genetic Algorithms读者反馈<br> http://impactfactor.cn/2024/5y/?ISSN=BK0162617<br><br> <br><br>书目名称Artificial Neural Nets and Genetic Algorithms读者反馈学科排名<br> http://impactfactor.cn/2024/5yr/?ISSN=BK0162617<br><br> <br><br>
BOAST
发表于 2025-3-21 22:57:11
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giggle
发表于 2025-3-22 01:38:29
A learning probabilistic neural network with fuzzy inference,roposed. The advantages of this network lie in the possibility of classification of the data with substantially overlapping clusters, and tuning of the activation function parameters improves the accuracy of classification. Simulation results confirm the efficiency of the proposed approach in the da
PATHY
发表于 2025-3-22 08:10:43
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尖叫
发表于 2025-3-22 12:10:06
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tariff
发表于 2025-3-22 14:59:12
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啮齿动物
发表于 2025-3-22 18:49:19
A hybrid algorithm for weight and connectivity optimization in feedforward neural networks,or performance because of lack of expressional capacity, while a too large network fits noise or apparent relations in the data sets studied. The work required to find a parsimonious network is often considerable with respect to both time and computational effort. This paper presents a method for tr
挥舞
发表于 2025-3-23 00:11:29
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Mercurial
发表于 2025-3-23 04:30:13
Binary Factorization in Hopfield-Like Neural Autoassociator: A Promising Tool for Data Compression,n feature extraction procedure which maps original patterns into features (factors) space of reduced, possibily very small, dimension. In this paper, we outline that Hebbian unsupervised learning of Hopfield-like neural network is a natural procedure for factor extraction. Due to this learning, fact
使人入神
发表于 2025-3-23 07:54:42
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