A highly sensitive double artificial neural network （DANN） analysis with flow-injection chemiluminescence （FI-CL） has been developed to simultaneously determine the trace amounts of the gold and platinum in simula...A highly sensitive double artificial neural network （DANN） analysis with flow-injection chemiluminescence （FI-CL） has been developed to simultaneously determine the trace amounts of the gold and platinum in simulated mixed samples, without the boring process.展开更多
Double parallel forward neural network （DPFNN） model is a mixture structure of single-layer perception and single-hidden-layer forward neural network （SLFN）. In this paper, by making use of the idea of online sequ...Double parallel forward neural network （DPFNN） model is a mixture structure of single-layer perception and single-hidden-layer forward neural network （SLFN）. In this paper, by making use of the idea of online sequential extreme learning machine （OS-ELM） on DPFNN, we derive the online sequential double parallel extreme learning machine algorithm （OS-DPELM）. Compared to other similar algorithms, our algorithms can achieve approximate learning performance with fewer numbers of hidden units, as well as the parameters to be determined. The experimental results show that the proposed algorithm has good generalization performance for real world classification problems, and thus can be a necessary and beneficial complement to OS-ELM.展开更多
文摘A highly sensitive double artificial neural network （DANN） analysis with flow-injection chemiluminescence （FI-CL） has been developed to simultaneously determine the trace amounts of the gold and platinum in simulated mixed samples, without the boring process.
基金Supported by the National Natural Science Foundation of China(Grant Nos.1140107661473328+1 种基金1117136761473059)
文摘Double parallel forward neural network （DPFNN） model is a mixture structure of single-layer perception and single-hidden-layer forward neural network （SLFN）. In this paper, by making use of the idea of online sequential extreme learning machine （OS-ELM） on DPFNN, we derive the online sequential double parallel extreme learning machine algorithm （OS-DPELM）. Compared to other similar algorithms, our algorithms can achieve approximate learning performance with fewer numbers of hidden units, as well as the parameters to be determined. The experimental results show that the proposed algorithm has good generalization performance for real world classification problems, and thus can be a necessary and beneficial complement to OS-ELM.
