State estimation for neural neutral-type networks with mixed time-varying delays and Markovian jumping parameters

This paper is concerned with a delay-dependent state estimator for neutral-type neural networks with mixed timevarying delays and Markovian jumping parameters.The addressed neural networks have a finite number of modes,and the modes may jump from one to another according to a Markov process.By construction of a suitable Lyapunov-Krasovskii functional,a delay-dependent condition is developed to estimate the neuron states through available output measurements such that the estimation error system is globally asymptotically stable in a mean square.The criterion is formulated in terms of a set of linear matrix inequalities（LMIs）,which can be checked efficiently by use of some standard numerical packages.

Study on Decision Method of Neutral Point Grounding Mode for Medium-Voltage Distribution Network

The neutral grounding mode of medium-voltage distribution network decides the reliability, overvoltage, relay protection and electrical safety. Therefore, a comprehensive consideration of the reliability, safety and economy is particularly important for the decision of neutral grounding mode. This paper proposes a new decision method of neutral point grounding mode for mediumvoltage distribution network. The objective function is constructed for the decision according the life cycle cost. The reliability of the neutral point grounding mode is taken into account through treating the outage cost as an operating cost. The safety condition of the neutral point grounding mode is preserved as the constraint condition of decision models, so the decision method can generate the most economical and reliable scheme of neutral point grounding mode within a safe limit. The example is used to verify the feasibility and effectiveness of the decision method.

The improved neutral network and its application for valuing rock mass mechanical parameter

The artificial neutral network(ANN) has the ability that self-study and self-remember, its 3 layers BP network has been applied extensively, but sometimes because of serious multi-correlation between the variables, and a few observations while many variables, there usually will result into paralyzing in study, and the neutral network further development is restricted in the system to some extent. The partial least square regression(PLS) has its advantage of building the calculation model between the variables with strong multi-correlation, especially much effective on a few data and many variables. So a new and effective method-improved neutral network has been introduced-the neutral network based on the PLS. The results of example show the improved method has a few calculations and high accuracy, and provide a new way for valuing the rock mass mechanical parameters.

Fault Prediction of Elevator Door System Based on PSO-BP Neural Network

Nowadays, the elevator has become an indispensable means of indoor transportation in people’s life, but in recent years this kind of traffic tools has caused many casualties because of the gate system fault. In order to ensure the safe and reliable operation of the elevator, the failure of elevator door system was predicted in this paper. Against the fault type of elevator door system: elevator door opened, excessive vibration when elevator door opened or closed, elevator door did not open or closed when reached the specified level. Three fault types were used as the output of the prediction model. There were 8 reasons for the failure, used them as input. A model based on particle swarm optimization (PSO) and BP neural network was established, using MATLAB to emulation;the results showed that: PSO-BP neural network algorithm was feasible in the fault prediction of the elevator door system.

Almost sure exponential stability of neutral stochastic delayed cellular neural networks

In this paper, almost sure exponential stability of neutral delayed cellular neural networks which are in the noised environment is studied by decomposing the state space to sub-regions in view of the saturation linearity of output functions of neurons of the cellular neural networks. Some algebraic criteria are obtained and easily verified. Some examples are given to illustrate the correctness of the results obtained.

Existence of Periodic Solutions for Neutral-Type Neural Networks with Delays on Time Scales

In this paper, we employ a fixed point theorem due to Krasnosel’skii to attain the existence of periodic solutions for neutral-type neural networks with delays on a periodic time scale. Some new sufficient conditions are established to show that there exists a unique periodic solution by the contraction mapping principle.

Multi-Source Underwater DOA Estimation Using PSO-BP Neural Network Based on High-Order Cumulant Optimization

Due to the complex and changeable environment under water,the performance of traditional DOA estimation algorithms based on mathematical model,such as MUSIC,ESPRIT,etc.,degrades greatly or even some mistakes can be made because of the mismatch between algorithm model and actual environment model.In addition,the neural network has the ability of generalization and mapping,it can consider the noise,transmission channel inconsistency and other factors of the objective environment.Therefore,this paper utilizes Back Propagation(BP)neural network as the basic framework of underwater DOA estimation.Furthermore,in order to improve the performance of DOA estimation of BP neural network,the following three improvements are proposed.(1)Aiming at the problem that the weight and threshold of traditional BP neural network converge slowly and easily fall into the local optimal value in the iterative process,PSO-BP-NN based on optimized particle swarm optimization(PSO)algorithm is proposed.(2)The Higher-order cumulant of the received signal is utilized to establish the training model.(3)A BP neural network training method for arbitrary number of sources is proposed.Finally,the effectiveness of the proposed algorithm is proved by comparing with the state-of-the-art algorithms and MUSIC algorithm.

Periodic Solution for Neutral Type Neural Networks

The principle aim of this paper is to explore the existence of periodic solution of neural networks model with neutral delay. Sufficient and realistic conditions are obtained by means of an abstract continuous theorem of k-set contractive operator and some analysis technique.

基于CPSO-BP神经网络的柴油机排气门间隙故障诊断

针对柴油机排气门间隙故障信号不易提取的特点,提出了将混沌粒子群神经网(Chaotic particle swarm optimization-Back Propagation,CPSO-BP)聚类模型应用于柴油机排气门间隙故障诊断.首先,采用经验模式分解(Empirical Mode Decomposition,EMD)对柴油机振动信号进行分解,将得到的前6个模态函数能量百分比作为反映故障状态的特征参数,重构BP神经网并用混沌粒子群算法对其结构和权值进行优化;最后,基于优化的神经网对排气门间隙为0.2mm,0.4mm,0.6mm等3种故障工况的信号进行聚类.结果表明:所有样本的测试结果均与实际状况一致,该方法可以较好地用于排气门间隙故障诊断.

基于PSO-BP-PID神经网络的注塑机料筒温度预测算法研究

提出了一种基于粒子群优化(PSO)算法、BP神经网络及比例积分微分(PID)控制的复合算法的注塑机料筒温度预测模型,即PSO-BP-PID神经网络模型,并进行了仿真研究。结果表明:使用PSO算法确定该模型的输出权重,并且对混合核函数参数进行优化升级;在模型训练过程中,使用更大的容许度处理正误差,保证预测误差始终处于正值,使预测结果科学可靠;将高斯核函数与多项式核函数结合,生成一个新型混合核函数,提高核函数极限学习机性能;PSO-BP-PID神经网络模型的预测效果整体较传统PID模型好,温度总体趋势与实际预测数据相近,具有更好的拟合度。

Aero-engine Blade Fatigue Analysis Based on Nonlinear Continuum Damage Model Using Neural Networks

Fatigue life and reliability of aero-engine blade are always of important significance to flight safety.The establishment of damage model is one of the key factors in blade fatigue research.Conventional linear Miner＇s sum method is not suitable for aero-engine because of its low accuracy.A back propagation neutral network（BPNN） based on the combination of Levenberg-Marquardt（LM） and finite element method（FEM） is used to describe process of nonlinear damage accumulation behavior in material and predict fatigue life of the blade.Fatigue tests of standard specimen made from TC4 are carried out to obtain material fatigue parameters and S-N curve.A nonlinear continuum damage model（CDM）,based on the BPNN with one hidden layer and ten neurons,is built to investigate the nonlinear damage accumulation behavior,in which the results from the tests are used as training set.Comparing with linear models and previous nonlinear models,BPNN has the lowest calculation error in full load range.It has significant accuracy when the load is below 500 MPa.Especially,when the load is 350 MPa,the calculation error of the BPNN is only 0.4%.The accurate model of the blade is built by using 3D coordinate measurement technology.The loading cycle in fatigue analysis is defined from takeoff to cruise in 10 min,and the load history is obtained from finite element analysis（FEA）.Then the fatigue life of the compressor blade is predicted by using the BPNN model.The final fatigue life of the aero-engine blade is 6.55 104 cycles（10 916 h） based on the BPNN model,which is effective for the virtual design of aero-engine blade.

基于IPSO-BP算法的燃油系统气压控制优化

针对燃油系统地面模拟试验气箱压力控制所存在的非线性、时变性问题,提出了一种基于改进粒子群算法(IPSO)优化的BP-PID控制器。该控制器结合改进粒子群算法全局搜索能力强和收敛速度快的特点,对BP神经网络的连接权值不断地调整,输出最优的PID控制参数。依据地面模拟试验参数,建立了主要器件的数学模型,利用Simulink软件对各器件的仿真模块进行搭建,构成一个完整的气箱控制系统进行研究。结果表明,IPSO-BP-PID控制比BP-PID控制响应速度快,稳定性好,超调量小,大大提高了气箱压力控制过程的精确性与鲁棒性。

Application of fuzzy analytic hierarchy process and neural network in power transformer risk assessment

In operation,risk arising from power transformer faults is of much uncertainty and complicacy.To timely and objectively control the risks,a transformer risk assessment method based on fuzzy analytic hierarchy process(FAHP) and artificial neural network(ANN) from the perspective of accuracy and quickness is proposed.An analytic hierarchy process model for the transformer risk assessment is built by analysis of the risk factors affecting the transformer risk level and the weight relation of each risk factor in transformer risk calculation is analyzed by application of fuzzy consistency judgment matrix;with utilization of adaptive ability and nonlinear mapping ability of the ANN,the risk factors with large weights are used as input of neutral network,and thus intelligent quantitative assessment of transformer risk is realized.The simulation result shows that the proposed method increases the speed and accuracy of the risk assessment and can provide feasible decision basis for the transformer risk management and maintenance decisions.

Control System of Neutral Beam Injection on HT-7

Neutral Beam Injection control system (NBICS) is constructed to measure the plasma current, Magnet current, vacuum pressure, cryopump temperature, control water cooling, filament voltage, and power supply, etc. The NBICS, consisting mainly of a Programmable Logic Controller (PLC) subsystem, data acquisition and processing subsystem and cryopump and vacuum pressure monitoring subsystem, has successfully been used on a NBI device. In this article, the design of NBICS on HT-7 is discussed and each subsystem is described in particular. In addition, some experimental results are reported which are very important data for further research related to the HT-7 tokamak.

Synchronization and Exponential Estimates of Complex Networks with Mixed Time-varying Coupling Delays

Exponential estimates and sufficient conditions for the exponential synchronization of complex dynamical networks with bounded time-varying delays are given in terms of linear matrix inequalities （LMIs）. A generalized complex networks model involving both neutral delays and retarded ones is presented. The exponential synchronization problem of the complex networks is converted equivalently into the exponential stability problem of a group of uncorrelated delay functional differential equations with mixed timevarying delays. By utilizing the free weighting matrix technique, a less conservative delay-dependent synchronization criterion is derived. An illustrative example is provided to demonstrate the effectiveness of the proposed method.

Novel delay-dependent stability analysis of Takagi-Sugeno fuzzy uncertain neural networks with time varying delays

This paper presents the stability analysis for a class of neural networks with time varying delays that are represented by the Takagi^ugeno IT-S） model. The main results given here focus on the stability criteria using a new Lyapunov functional. New relaxed conditions and new linear matrix inequality-based designs are proposed that outperform the previous results found in the literature. Numerical examples are provided to show that the achieved conditions are less conservative than the existing ones in the literature.

Multimodal Emotion Recognition with Transfer Learning of Deep Neural Network

Due to the lack of large-scale emotion databases,it is hard to obtain comparable improvement in multimodal emotion recognition of the deep neural network by deep learning,which has made great progress in other areas.We use transfer learning to improve its performance with pretrained models on largescale data.Audio is encoded using deep speech recognition networks with 500 hours’speech and video is encoded using convolutional neural networks with over 110,000 images.The extracted audio and visual features are fed into Long Short-Term Memory to train models respectively.Logistic regression and ensemble method are performed in decision level fusion.The experiment results indicate that 1)audio features extracted from deep speech recognition networks achieve better performance than handcrafted audio features;2)the visual emotion recognition obtains better performance than audio emotion recognition;3)the ensemble method gets better performance than logistic regression and prior knowledge from micro-F1 value further improves the performance and robustness,achieving accuracy of 67.00%for“happy”,54.90%for“an?gry”,and 51.69%for“sad”.

Height prediction of water flowing fractured zones based on BP artificial neural network

Factures caused by deformation and destruction of bedrocks over coal seams can easily lead to water flooding(inrush)in mines,a threat to safety production.Fractures with high hydraulic conductivity are good watercourses as well as passages for inrush in mines and tunnels.An accurate height prediction of water flowing fractured zones is a key issue in today's mine water prevention and control.The theory of leveraging BP artificial neural network in height prediction of water flowing fractured zones is analysed and applied in Qianjiaying Mine as an example in this paper.Per the comparison with traditional calculation results,the BP artificial neural network better reflects the geological conditions of the research mine areas and produces more objective,accurate and reasonable results,which can be applied to predict the height of water flowing fractured zones.

Parasite-host network analysis provides insights into the evolution of two mistletoe lineages(Loranthaceae and Santalaceae)

Mistletoes are ecologically important parasitic plants,with> 1600 species from five lineages worldwide.Mistletoe lineages exhibit distinct patterns of species diversification and host specificity,however,the mechanisms underlying these differences are poorly understood.In this study,we analysed a comprehensive parasite-host network,including 280 host species from 60 families and 22 mistletoe species from two lineages(Santalaceae and Loranthaceae) in Xishuangbanna,located in a biodiversity hotspot of tropical Asia.We identified the factors that predict the infection strength of mistletoes.We also detected host specificity and the phylogenetic signal of mistletoes and their hosts.We found that this interaction network could be largely explained by a model based on the relative abundance of species.Host infection was positively correlated with diameter at breast height and tree coverage,but negatively correlated with wood density.Overall,closely related mistletoe species tend to interact more often with similar hosts.However,the two lineages showed a significantly different network pattern.Rates of host generality were higher in Loranthaceae than in Santalaceae,although neither lineage showed phylogenetic signal for host generality.This study demonstrates that the neutral interaction hypothesis provides suitable predictions of the mistletoe-host interaction network,and mistletoe species show significant phylogenetic signals for their hosts.Our findings also indicate that high species diversification in Loranthaceae may be explained by high rates of host generality and the evolutionary history shared by Loranthaceae species with diverse host plants in the tropics.