Titlebook: Artificial Intelligence and Soft Computing; 15th 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(wǎng)絡(luò)公開度




書目名稱Artificial Intelligence and Soft Computing網(wǎng)絡(luò)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

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Visualizing and Understanding Nonnegativity Constrained Sparse Autoencoder in?Deep?Learningt use the architecture of Nonnegativity Constrained Autoencoder (NCAE). We show that by constraining most of the weights in the network to be nonnegative using both . and . nonnegativity penalization, a more understandable structure can result with minute deterioration in classification accuracy. Al

思想靈活

Experimental Analysis of Forecasting Solar Irradiance with Echo State Networks and?Simulating Anneal High forecast accuracy can help in the management of industrial strategies. We present an approach that combines the potential of a Neural Network named . and a well-known optimisation technique named .. We use the SA technique for selecting the meteorological variables relevant in the forecasting

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Neural System for Power Load Prediction in a Week Time Horizoner neural networks that have common input. Each network is dedicated to predict the total load in one of the seven successive days. Various form of input vectors as well as various ways of encoding them were tested. Verification which type of input data are crucial as well as which periodic aspects

Homocystinuria

Anhydrous

Parallel Learning of Feedforward Neural Networks Without Error Backpropagationd on a new idea of learning neural networks without error backpropagation. The proposed solution is based on completely new parallel structures to effectively reduce high computational load of this algorithm. Detailed parallel 2D and 3D neural network learning structures are explicitely discussed.

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Pepsin

Ensemble ANN Classifier for Structural Health Monitoring often perform differently due to the random distribution of initial weights. These issues cause the practical use of ANNs a challenging task. Some of the mentioned drawbacks can be eliminated using ensembles of ANNs. However, relevance of a single ensemble member might be different in different cla

鍍金

ARK