Titlebook: Artificial Intelligence and Soft Computing; 11th International C Leszek Rutkowski,Marcin Korytkowski,Jacek M. Zurad Conference proceedings

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書目名稱Artificial Intelligence and Soft Computing影響因子(影響力)




書目名稱Artificial Intelligence and Soft Computing影響因子(影響力)學科排名




書目名稱Artificial Intelligence and Soft Computing網絡公開度




書目名稱Artificial Intelligence and Soft Computing網絡公開度學科排名




書目名稱Artificial Intelligence and Soft Computing被引頻次




書目名稱Artificial Intelligence and Soft Computing被引頻次學科排名




書目名稱Artificial Intelligence and Soft Computing年度引用




書目名稱Artificial Intelligence and Soft Computing年度引用學科排名




書目名稱Artificial Intelligence and Soft Computing讀者反饋




書目名稱Artificial Intelligence and Soft Computing讀者反饋學科排名

忍受

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性別

Incidental Neural Networks as Nomograms Generatorshe XIII Hilbert’s problem which was presented 1900 in the context of nomography, for the particular nomographic construction. The problem was solved by V. Arnold (a student of Andrey Kolomogorov) in 1957. For numeric data of unknown functional relation we developed the . as nomograms generators – the graphic calculating devices.

sphincter

On Learning in a Time-Varying Environment by Using a Probabilistic Neural Network and the Recursive in time-varying environment. The general regression neural network is based on the orthogonal-type kernel functions. The appropriate algorithm is presented in a recursive form. Sufficient simulations confirm empirically the convergence of the algorithm.

Feature

Fachenglisch für GesundheitsberufeThis paper presents the parallel architecture of the Recurrent Multi Layer Perceptron learning algorithm. The proposed solution is based on the high parallel three dimensional structure to speed up learning performance. Detailed parallel neural network structures are explicitly shown.

誘騙

Fachenglisch für GesundheitsberufeSufficient conditions for uniform convergence of general regression neural networks, based on the orthogonal series-type kernel, are given. The convergence is guarantee even if variance of noise diverges to infinity. Simulation results are presented.

Consensus

mosque

https://doi.org/10.1007/978-3-540-28534-2Sufficient conditions for strong convergence of recursive general regression neural networks are given assuming nonstationary noise. The orthogonal series-type kernel is applied. Simulation results show convergence even if variance of noise diverges to infinity.

搖曳

Norma Huss,Sandra Schiller,Matthias SchmidtA problem of learning in non-stationary environment is solved by making use of order statistics in combination with the Parzen kernel-type regression neural network. Probabilistic properties of the algorithm are investigated and weak convergence is established. Experimental results are presented.