Applications on iterative control and multiple input multiple output (MIMO) system were developed. Two new charts derived from extrinsic information transfer (EXIT) chart were employed as the designing tools, which ar...Applications on iterative control and multiple input multiple output (MIMO) system were developed. Two new charts derived from extrinsic information transfer (EXIT) chart were employed as the designing tools, which are called as output mutual information chart with defined iterative degree (DID) chart and near optimum output mutual information (NOMI) chart respectively. Different from the EXIT chart, they can show the iterative performance on the whole signal-to-noise ratio range with one single curve, whereas computation complexity is greatly reduced compared with conventional bit error ratio (BER) performance curve. The iterative control was implemented according to a near-optimum iterative degree vector determined by NOMI chart, the reasonability of uncertain parameters was analyzed in one MIMO system. The concepts were illustrated based on bit-interleaved coded modulation with iterative decoding (BICM-ID).展开更多
In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occu...In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occurred＇ and transfer ＇not occurred＇. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation （multilayer perceptron algorithms） could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties （e.g., temperature, density） show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.展开更多
基金The National Natural Science Foundation of China (No. 60496316) The National Hi-Tech Research and Development Program (863) of China (No. 2006AA01Z270)
文摘Applications on iterative control and multiple input multiple output (MIMO) system were developed. Two new charts derived from extrinsic information transfer (EXIT) chart were employed as the designing tools, which are called as output mutual information chart with defined iterative degree (DID) chart and near optimum output mutual information (NOMI) chart respectively. Different from the EXIT chart, they can show the iterative performance on the whole signal-to-noise ratio range with one single curve, whereas computation complexity is greatly reduced compared with conventional bit error ratio (BER) performance curve. The iterative control was implemented according to a near-optimum iterative degree vector determined by NOMI chart, the reasonability of uncertain parameters was analyzed in one MIMO system. The concepts were illustrated based on bit-interleaved coded modulation with iterative decoding (BICM-ID).
基金Dr. Steve Jones, Scientific Advisor of the Canon Foundation for Scientific Research (7200 The Quorum, Oxford Business Park, Oxford OX4 2JZ, England). Canon Foundation for Scientific Research funded the UPC 2013 tuition fees of the corresponding author during her writing this article
文摘In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occurred＇ and transfer ＇not occurred＇. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation （multilayer perceptron algorithms） could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties （e.g., temperature, density） show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.
