滴注
发表于 2025-3-23 11:42:57
Optimizing the self-organizing-process of topology maps,serves the neighbourhood relations of the input data, if the learning parameters, learning coefficient and width of the neighbourhood function, are chosen correctly. In general, the parameters are chosen empirically, dependent on the distribution of the training data and the network architecture
antenna
发表于 2025-3-23 15:12:03
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Largess
发表于 2025-3-23 19:01:17
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creatine-kinase
发表于 2025-3-24 00:05:10
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Ablation
发表于 2025-3-24 03:31:48
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callous
发表于 2025-3-24 07:37:16
Using Genetic Engineering to find modular structures and activation functions for architectures of it is possible, with the help of a graph-database and Genetic Engineering, to find modular structures for these networks. Some new graph-rewritings are used to construct families of architectures from these modular structures. Simulation results for two problems are given. An analysis of the data in
tackle
发表于 2025-3-24 13:42:54
Genetic Algorithms for solving Systems of Fuzzy Relational Equations,ased on the use of Genetic Algorithms (GA) and on a probabilistic algorithm for solving a SFRE — presented elsewhere. This approach is useful both in classical SFRE problems and in dynamic system identification. Some numerical results regarding both aspects show that our method can be successfully a
phase-2-enzyme
发表于 2025-3-24 16:46:24
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令人心醉
发表于 2025-3-24 19:55:28
Statistical Methods for Data Analysis,biological species. The integration of these two fields, fuzzy logic and neural networks, has the potential for combining the benefits of these two fascinating fields into a single capsule. The intent of this paper is to describe the basic notions of biological and computational neuronal morphologie
独特性
发表于 2025-3-25 03:01:09
Allen P. Davis,Richard H. McCuenccessfully. This process is then followed by an extended Kalman filter algorithm, which estimates the width of the neighbourhood function..In case of fast self-organizing algorithms, as published in , the proposed parameter estimation method is essential for the training of data with unknown dens