Neural decoding based on probabilistic neural network

Brain-machine interface (BMI) has been developed due to its possibility to cure severe body paralysis. This technology has been used to realize the direct control of prosthetic devices,such as robot arms,computer cursors,and paralyzed muscles. A variety of neural decoding algorithms have been designed to explore relationships between neural activities and movements of the limbs. In this paper,two novel neural decoding methods based on probabilistic neural network (PNN) in rats were introduced,the PNN decode...     

Nonlinear model predictive control with guaranteed stability based on pseudolinear neural networks

~~identificationis7-7-1,where3nm==,andtheRPEalgorithmisusedtoupdatetheweightingofPNN.Thewholetrainingprocessuses800iterations.InordertoovercometheinaccuracyofPNNmodel,thecontrollerstructureisacompositeoneasfbff()()()ututut=+,(37)wherefb()utistheoutputoffeedbackcontroller,ff()utistheoutputofpredictivecontrollerdescribedbyEq.(14),with0.20=,0.40=,andmax5K=.Insimulatedclosedloopcontrol,ufb(t)isaproportionalcontroller,fb()()Putket=and5.0Pk=.Theset-pointofthesystemisd0.15,if040,and120()0.24,if4…     

基于模式匹配识别和神经网络的股指预测研究

股票市场是个非线性系统,由于受到多方面因素的影响,对于股指的预测一直是个难题。各种建模方法都有自身的缺点,如模式匹配识别系统过分依赖历史数据,缺乏自身变化。 BP神经网络容易陷入局部最优,而且训练时间较长。文章从模式匹配识别和BP神经网络相结合的角度来进行股票指数预测分析,预测系统克服了单一神经网络预测系统和单一模式匹配识别预测系统的各自缺点,能有效地预测股指。     

在视频对象平面生成中使用细胞神经网络进行初始对象的分割

1　Introduction　MPEG 4videocodingstandard providesobject basedfunctionalitiesbyintroducingtheconceptofvideoobject plane (VOP) .Withtheextractionofvideoobjectsandallocatingdifferentnumberofbitsordifferentframe ratesfordifferentobjects ,thestan dardcansupportobject basedscalabilitythatisusefulinmanypracticalapplications[1] .However,MPEG 4alwaysassumesthatthevideocontentstobecodedarewellrepresentedinvideoobjectswithoutmandatinganyspecifictechniques;sovideoobjectsegmentationbecomesanimportant…     

Bayesian networks modeling for thermal error of numerical control machine tools

The interaction between the heat source location, its intensity, thermal expansion coefficient, the machine system configuration and the running environment creates complex thermal behavior of a machine tool, and also makes thermal error prediction difficult. To address this issue, a novel prediction method for machine tool thermal error based on Bayesian networks （BNs） was presented. The method described causal relationships of factors inducing thermal deformation by graph theory and. estimated the thermal error by Bayesian statistical techniques. Due to the effective combination of domain knowledge and sampled data, the BN method could adapt to the change of running state of machine, and obtain satisfactory prediction accuracy. Experiments on spindle thermal deformation were conducted to evaluate the modeling performance. Experimental results indicate that the BN method performs far better than the least squares （LS） analysis in terms of modeling estimation accuracy.     

基于参数自调整模糊神经网络的直接甲醇燃料电池电特性分析

This paper introduces the effects of cell operating temperature, methanol concentration and airflow rate, respectively, on the performance of direct methanol fuel cell （DMFC）. A novel method based on fuzzy neural networks identification technique is proposed to establish the performance model of DMFC. Three dynamic performance models of DMFC under the influences of cell operating temperature, methanol concentration, and airflow rate are identified and established separately. Simulation results show that modeling using fuzzy neural networks identification is satisfactory with high accuracy. It is applicable to DMFC control systems.     

Robust exponential stability analysis of a larger class of discrete-time recurrent neural networks

The robust exponential stability of a larger class of discrete-time recurrent neural networks (RNNs) is explored in this paper. A novel neural network model, named standard neural network model (SNNM), is introduced to provide a general framework for stability analysis of RNNs. Most of the existing RNNs can be transformed into SNNMs to be analyzed in a unified way. Applying Lyapunov stability theory method and S-Procedure technique, two useful criteria of robust exponential stability for the discrete-time SNNMs are derived. The conditions presented are formulated as linear matrix inequalities (LMIs) to be easily solved using existing efficient convex optimization techniques. An example is presented to demonstrate the transformation procedure and the effectiveness of the results.     

Nonlinear modelling of a SOFC stack by improved neural networks identification

The solid oxide fuel cell (SOFC) is a nonlinear system that is hard to model by conventional methods. So far,most existing models are based on conversion laws,which are too complicated to be applied to design a control system. To facilitate a valid control strategy design,this paper tries to avoid the internal complexities and presents a modelling study of SOFC per-formance by using a radial basis function (RBF) neural network based on a genetic algorithm (GA). During the process of mod-elling,the GA aims to optimize the parameters of RBF neural networks and the optimum values are regarded as the initial values of the RBF neural network parameters. The validity and accuracy of modelling are tested by simulations,whose results reveal that it is feasible to establish the model of SOFC stack by using RBF neural networks identification based on the GA. Furthermore,it is possible to design an online controller of a SOFC stack based on this GA-RBF neural network identification model.     

Application of principal component-radial basis function neural networks (PC-RBFNN) for the detection of water-adulterated bayberry juice by near-infrared spectroscopy

Near-infrared (NIR) spectroscopy combined with chemometrics techniques was used to classify the pure bayberry juice and the one adulterated with 10% (w/w) and 20% (w/w) water. Principal component analysis (PCA) was applied to reduce the dimensions of spectral data, give information regarding a potential capability of separation of objects, and provide principal component (PC) scores for radial basis function neural networks (RBFNN). RBFNN was used to detect bayberry juice adulterant. Multiplicative scatter correction (MSC) and standard normal variate (SNV) transformation were used to preprocess spectra. The results demonstrate that PC-RBFNN with optimum parameters can separate pure bayberry juice samples from water-adulterated bayberry at a recognition rate of 97.62%, but cannot clearly detect water levels in the adulterated bayberry juice. We conclude that NIR technology can be successfully applied to detect water-adulterated bayberry juice.     

Stability analysis of discrete-time BAM neural networks based on standard neural network models

To facilitate stability analysis of discrete-time bidirectional associative memory (BAM) neural networks, they were converted into novel neural network models, termed standard neural network models (SNNMs), which interconnect linear dynamic systems and bounded static nonlinear operators. By combining a number of different Lyapunov functionals with S-procedure, some useful criteria of global asymptotic stability and global exponential stability of the equilibrium points of SNNMs were derived. These stability conditions were formulated as linear matrix inequalities (LMIs). So global stability of the discrete-time BAM neural networks could be analyzed by using the stability results of the SNNMs. Compared to the existing stability analysis methods, the proposed approach is easy to implement, less conservative, and is applicable to other recurrent neural networks.     

Nonlinear model predictive control with guaraneed stability based on pesudolinear neural networks

A nonlinear model predictive control problem based on pseudo-linear neural network (PNN) is discussed, in which the second order on-line optimization method is adopted. The recursive computation of Jacobian matrix is investigated. The stability of the closed loop model predictive control system is analyzed based on Lyapunov theory to obtain the sufficient condition for the asymptotical stability of the neural predictive control system. A simulation was carried out for an exothermic first-order reaction in a continuous stirred tank reactor. It is demonstrated that the proposed control strategy is applicable to some of nonlinear systems.     

基于神经网络的转换GPS高程的新方法

GPS高程是相对于WGS-84椭球体的大地高,因此,在工程应用中,GPS高程需要转换为正常高.转换GPS高程通常采用二次曲面拟合法(CFM)和神经网络方法(NNM),但这2种方法各有优缺点.在研究了这2种方法之后,提出了一种转换GPS高程的新方法,该方法综合了上述2种方法的优点,故取名为“CF&NNM”方法.介绍了CF&NNM方法的思路和计算过程.通过一个工程实例,列出了上述3种方法的数据处理结果,新方法效果最好.对CF&NNM方法进行了理论分析.     

LMI-based approach for global asymptotic stability analysis of continuous BAM neural networks

Studies on the stability of the equilibrium points of continuous bidirectional associative memory (BAM) neural network have yielded many useful results. A novel neural network model called standard neural network model (SNNM) is ad- vanced. By using state affine transformation, the BAM neural networks were converted to SNNMs. Some sufficient conditions for the global asymptotic stability of continuous BAM neural networks were derived from studies on the SNNMs’ stability. These co…     

神经网络的滚动轴承故障诊断

文章通过旋转机械故障实验平台,采集旋转机械故障实验台轴承的3种工作状态分别是轴承正常、轴承内圈裂缝、轴承外圈裂缝的振动加速度信号．对信号进行零均值化处理后,选择频率成分幅值较大的频率进行信号重组,提取其时域量纲特征值,利用神经网络进行故障类型的识别;通过实验,取得了很好的诊断结果．     

A comparison of conventional linear regression methods and neural networks for forecasting educational spending

This study presents an application of neural network methods for forecasting per pupil expenditures in public elementary and secondary schools in the United States. Using annual historical data from 1959 through 1990, forecasts were prepared for the period from 1991 through 1995. Forecasting models included the multivariate regression model developed by the National Center for Education Statistics for their annual Projections of Education Statistics Series, and three neural architectures: (1) recurrent backpropagation; (2) Generalized Regression; and (3) Group Method of Data Handling. Forecasts were compared for accuracy against actual values for educational spending for the period. Regarding prediction accuracy, neural network results ranged from comparable to superior with respect to the NCES model. Contrary to expectations, the most successful neural network procedure yielded its results with an even simpler linear form than the NCES model. The findings suggest the potential value of neural algorithms for strengthening econometric models as well as producing accurate forecasts. [JEL C45, C53, I21]     

3D brain glioma segmentation in MRI through integrating multiple densely connected 2D convolutional neural networks

To overcome the computational burden of processing three-dimensional(3 D) medical scans and the lack of spatial information in two-dimensional(2 D) medical scans, a novel segmentation method was proposed that integrates the segmentation results of three densely connected 2 D convolutional neural networks(2 D-CNNs). In order to combine the lowlevel features and high-level features, we added densely connected blocks in the network structure design so that the low-level features will not be missed as the network layer increases during the learning process. Further, in order to resolve the problems of the blurred boundary of the glioma edema area, we superimposed and fused the T2-weighted fluid-attenuated inversion recovery(FLAIR) modal image and the T2-weighted(T2) modal image to enhance the edema section. For the loss function of network training, we improved the cross-entropy loss function to effectively avoid network over-fitting. On the Multimodal Brain Tumor Image Segmentation Challenge(BraTS) datasets, our method achieves dice similarity coefficient values of 0.84,0.82, and 0.83 on the BraTS2018 training; 0.82, 0.85, and 0.83 on the BraTS2018 validation; and 0.81, 0.78, and 0.83 on the BraTS2013 testing in terms of whole tumors, tumor cores, and enhancing cores, respectively. Experimental results showed that the proposed method achieved promising accuracy and fast processing, demonstrating good potential for clinical medicine.     

A maximum power point tracker for photovoltaic energy systems based on fuzzy neural networks

To extract the maximum power from a photovoltaic （PV） energy system, the real-time maximum power point （MPP） of the PV array must be tracked closely. The non-linear and time-variant characteristics of the PV array and the non-linear and non-minimum phase characteristics of a boost converter make it difficult to track the MPP for traditional control strategies. We propose a fuzzy neural network controller （FNNC）, which combines the reasoning capability of fuzzy logical systems and the learning capability of neural networks, to track the MPP. With a derived learning algorithm, the parameters of the FNNC are updated adaptively. A gradient estimator based on a radial basis function neural network is developed to provide the reference information to the FNNC. Simulation results show that the proposed control algorithm provides much better tracking performance compared with the filzzy logic control algorithm.     

BP神经网络计算法及其应用研究

研究了BP神经网络的算法,提出了权值、阈值调整的双动量算法和学习率调整的批处理半恢复自适应调整法,使网络总体收敛性较好,用所设软件计算了15种含氢原子共价键的键长。     

Global exponential stability of Cohen-Grossberg neural networks with time-varying delays and impulses

In this paper, the Cohen-Grossberg neural networks with time-varying delays and impulses are considered. New sufficient conditions for the existence and global exponential stability of a unique equilibrium point are established by using the fixed point theorem and Lyapunov functional. An example is given to demonstrate the effectiveness of our results.     

一类二元离散神经网络周期解的存在性

研究具有两段常数不连续信号传递函数的二元离散神经网络模型周期解的存在性和吸引性，可获得存在性与吸引性的一个充分条件。