北京谱仪III上利用神经网络方法进行粒子鉴别的研究

本文描述了一种多层感知器的神经网络系统在BESIII粒子鉴别技术中的应用。网络按照子探测器分别进行训练, 输出结果可以作为后续网络的输入或者可以为似然函数方法构建概率密度函数。蒙特卡罗模拟样本的检验结果表明, 利用神经网络方法可以在BESIII上获得较好的粒子鉴别效果。     

HL-2A tokamak disruption forecasting based on an artificial neural network

Artificial neural networks are trained to forecast the plasma disruption in HL-2A tokamak. Optimized network architecture is obtained. Saliency analysis is made to assess the relative importance of different diagnostic signals as network input. The trained networks can successfully detect the disruptive pulses of HL-2A tokamak. The results obtained show the possibility of developing a neural network predictor that intervenes well in advance for avoiding plasma disruption or mitigating its effects.     

Monte Carlo study on the low momentum μ-π identification of the BESⅢ EM calorimeter

The BESⅢ detector has a high-resolution electromagnetic calorimeter which can be used for low momentum μ-π identification.Based on Monte Carlo simulations, μ-π separation was studied.A multilayer perceptron neural network making use of the defined variables was used to do the identification and a good μ-π separation result was obtained.     

Optimization-based topology identification of complex networks

In many cases, the topological structures of a complex network are unknown or uncertain, and it is of significance to identify the exact topological structure. An optimization-based method of identifying the topological structure of a complex network is proposed in this paper. Identification of the exact network topological structure is converted into a minimal optimization problem by using the estimated network. Then, an improved quantum-behaved particle swarm optimization algorithm is used to solve the optimization problem. Compared with the previous adaptive synchronization-based method, the proposed method is simple and effective and is particularly valid to identify the topological structure of synchronization complex networks. In some cases where the states of a complex network are only partially observable, the exact topological structure of a network can also be identified by using the proposed method. Finally, numerical simulations are provided to show the effectiveness of the proposed method.     

An artificial neural network for proton identification in HERMES data

The HERMES time-of-flight (TOF) system is used for proton identification, but must be carefully calibrated for systematic biases in the equipment. This paper presents an artificial neural network (ANN) trained to recognize protons from Λ0 decay using only raw event data such as time delay, momentum, and trajectory. To avoid the systematic errors associated with Monte Carlo models, we collect a sample of raw experimental data from the year 2000. We presume that when for a positive hadron (assigned one proton mass) and a negative hadron (assigned one π- mass) the reconstructed invariant mass lies within the Λ0 resonance, the positive hadron is more likely to be a proton. Such events are assigned an output value of one during the training process; all others were assigned the output value zero.The trained ANN is capable of identifying protons in independent experimental data, with an efficiencyequivalent to the traditional TOF calibration. By modifying the threshold for proton identification, a researchercan trade off between selection efficiency and background rejection power. This simple and convenient methodis applicable to similar detection problems in other experiments.     

Artificial neural networks applied to the analysis of synchrotron nuclear resonant scattering data

The capabilities of artificial neural networks (ANNs) have been investigated for the analysis of nuclear resonant scattering (NRS) data obtained at a synchrotron source. The major advantage of ANNs over conventional analysis methods is that, after an initial training phase, the analysis is fully automatic and practically instantaneous, which allows for a direct intervention of the experimentalist on‐site. This is particularly interesting for NRS experiments, where large amounts of data are obtained in very short time intervals and where the conventional analysis method may become quite time‐consuming and complicated. To test the capability of ANNs for the automation of the NRS data analysis, a neural network was trained and applied to the specific case of an Fe/Cr multilayer. It was shown how the hyperfine field parameters of the system could be extracted from the experimental NRS spectra. The reliability and accuracy of the ANN was verified by comparing the output of the network with the results obtained by conventional data analysis.     

人工神经网络在配煤过程状态建模中的应用研究

本文详细介绍了人工神经网络应用于状态建模的方法．对神经网络应用中的一些难点提出了切实可行且有效的解决措施，并举例作了应用示范．同时还介绍了神经网络方法应用于优化动力配煤的情况，并就神经网络方法在优化动力配煤中的进一步应用作了展望．     

Partially connected feedforward neural networks on Apollonian networks

This paper presents a novel and data-independent method to construct a type of partially connected feedforward neural network (FNN). The proposed networks, called Apollonian network-based partially connected FNNs (APFNNs), are constructed in terms of the structures of two-dimensional deterministic Apollonian networks. The APFNNs are then applied in various experiments to solve function approximation, forecasting and classification problems. Their results are compared with those generated by partially connected FNNs with random connectivity (RPFNNs), different learning algorithm-based traditional FNNs and other benchmark methods. The results demonstrate that the proposed APFNNs have a good capacity to fit complicated input and output relations, and provide better generalization performance than traditional FNNs and RPFNNs. The APFNNs also demonstrate faster training speed in each epoch than traditional FNNs.     

An artificial neural network for proton identification in HERMES data

The HERMES time-of-flight （TOF） system is used for proton identification, but must be carefully calibrated for systematic biases in the equipment. This paper presents an artificial neural network （ANN） trained to recognize protons from ∧^0 decay using only raw event data such as time delay, momentum, and trajectory. To avoid the systematic errors associated with Monte Carlo models, we collect a sample of raw experimental data from the year 2000. We presume that when for a positive hadron （assigned one proton mass） and a negative hadron （assigned one π^- mass） the reconstructed invariant mass lies within the ∧^0 resonance, the positive hadron is more likely to be a proton. Such events are assigned an output value of one during the training process; all others were assigned the output value zero.
The trained ANN is capable of identifying protons in independent experimental data, with an efficiency equivalent to the traditional TOF calibration. By modifying the threshold for proton identification, a researcher can trade off between selection efficiency and background rejection power. This simple and convenient method is applicable to similar detection problems in other experiments.     

Control modeling of ash wood drying using process neural networks

For the control and system identification problems of the deceleration phase of the ash wood drying process, we propose a deceleration phase modeling method of ash wood drying using process neural networks with double hidden layers. This method applies time-varying characteristics of process neural networks and the ability to extract time-space cumulative effects. The time-varying characteristics of wood drying deceleration phase modeling under time series background are directly incorporated into the model. By comparison with traditional neural network modeling results, we prove that the model of process neural networks has better control accuracy, providing an idea to solve control and nonlinear system identification problems under a time series background.