Prediction of flow stress of Ti-15-3 alloy with artificial neural network

Hot compression experiments were conducted on Ti 15 3 alloy specimens using Gleeble 1500 Thermal Simulator.These tests were focused to obtain the flow stress data under various conditions of strain,strain rate and temperature. On the basis of these data, the predicting model for the nonlinear relation between flow stress and deformation strain,strain rate and temperature for Ti 15 3 alloy was developed with a back propagation artificial neural network method. Results show that the neural network can reproduce the flow stress in the sampled data and predict the nonsampled data well. Thus the neural network method has been verified to be used to tackle hot deformation problems of Ti 15 3 alloy. [

Chaotic Neural Network Technique for "0-1" Programming Problems

0-1 programming is a special case of the integer programming, which is commonly encountered in many optimization problems. Neural network and its general energy function are presented for 0-1 optimization problem. Then, the 0-1 optimization problems are solved by a neural network model with transient chaotic dynamics (TCNN). Numerical simulations of two typical 0-1 optimization problems show that TCNN can overcome HNN's main drawbacks that it suffers from the local minimum and can search for the global optimal solutions in to solveing 0-1 optimization problems.

Parameter Self - Learning of Generalized Predictive Control Using BP Neural Network

This paper describes the self—adjustment of some tuning-knobs of the generalized predictive controller(GPC).A three feedforward neural network was utilized to on line learn two key tuning-knobs of GPC,and BP algorithm was used for the training of the linking-weights of the neural network.Hence it gets rid of the difficulty of choosing these tuning-knobs manually and provides easier condition for the wide applications of GPC on industrial plants.Simulation results illustrated the effectiveness of the method.

O(t^(-β))-SYNCHRONIZATION AND ASYMPTOTIC SYNCHRONIZATION OF DELAYED FRACTIONAL ORDER NEURAL NETWORKS

This article explores the O(t^(-β))synchronization and asymptotic synchronization for fractional order BAM neural networks(FBAMNNs)with discrete delays,distributed delays and non-identical perturbations.By designing a state feedback control law and a new kind of fractional order Lyapunov functional,a new set of algebraic sufficient conditions are derived to guarantee the O(t^(-β))Synchronization and asymptotic synchronization of the considered FBAMNNs model;this can easily be evaluated without using a MATLAB LMI control toolbox.Finally,two numerical examples,along with the simulation results,illustrate the correctness and viability of the exhibited synchronization results.

Optimization of Process Parameters of Continuous Microwave Drying Raspberry Puree Based on RSM and ANN-GA

To improve drying uniformity and anthocyanin content of the raspberry puree dried in a continuous microwave dryer,the effects of process parameters(microwave intensity,air velocity,and drying time)on evaluation indexes(average temperature,average moisture content,average retention rate of the total anthocyanin content,temperature contrast value,and moisture dispersion value)were investigated via the response surface method(RSM)and the artificial neural network(ANN)with genetic algorithm(GA).The results showed that the microwave intensity and drying time dominated the changes of evaluation indexes.Overall,the ANN model was superior to the RSM model with better estimation ability,and higher drying uniformity and anthocyanin retention rate were achieved for the ANN-GA model compared with RSM.The optimal parameters were microwave intensity of 5.53 W•g^(-1),air velocity of 1.22 m·s^(-1),and drying time of 5.85 min.This study might provide guidance for process optimization of microwave drying berry fruits.

MicroRNA regulatory pattern in spinal cord ischemia-reperfusion injury

After spinal cord injury, dysregulated miRNAs appear and can participate in inflammatory responses, as well as the inhibition of apoptosis and axon regeneration through multiple pathways. However, the functions of miRNAs in spinal cord ischemia-reperfusion injury progression remain unclear. miRCURY LNATM Arrays were used to analyze miRNA expression profiles of rats after 90 minutes of ischemia followed by reperfusion for 24 and 48 hours. Furthermore, subsequent construction of aberrantly expressed miRNA regulatory patterns involved cell survival, proliferation, and apoptosis. Remarkably, the mitogen-activated protein kinase(MAPK) signaling pathway was the most significantly enriched pathway among 24-and 48-hour groups. Bioinformatics analysis and quantitative reverse transcription polymerase chain reaction confirmed the persistent overexpression of miR-22-3 p in both groups. These results suggest that the aberrant miRNA regulatory network is possibly regulated MAPK signaling and continuously affects the physiological and biochemical status of cells, thus participating in the regulation of spinal cord ischemia-reperfusion injury. As such, miR-22-3 p may play sustained regulatory roles in spinal cord ischemia-reperfusion injury. All experimental procedures were approved by the Animal Ethics Committee of Jilin University, China [approval No. 2020(Research) 01].

Fractal Characteristics and Prediction of Ti-15-3 Alloy Recrystallized Microstructure

Grain shape of the hot deforming alloy is an important of material. The fractal theory was applied to analyze index to character the microstructure and performance the recrystallized microstructure of Ti-15-3 alloy after hot deformation and solution treatment. The fractal dimensions of recrystallized grains were calculated by slit island method. The influence of processing parameters on fractal dimension and grain size was studied, It has been shown that the shapes of recrystallized grain boundaries are self-similar, and the fractal dimension varies from 1 to 2. With increasing deformation degree and strain rate or decreasing deformation temperature, the fractal dimension of grain boundaries increased and the grain size decreased. So the fractal dimension could characterize the grain shape and size. A neural network model was trained to predict the fractal dimension of recrystallized microstructure and the result is in excellent agreement with the experimental data.

基于混合双层自组织径向基函数神经网络的优化学习算法

针对传统方法采用先训练后测试两阶段学习机制极易导致的过拟合或欠拟合问题,提出一种基于混合双层自组织径向基函数神经网络的优化学习(hybrid bilevel self-organizing radial basis function neural network optimization learning,Hb-SRBFNN-OL)算法。首先,将训练过程和测试过程集成到一个统一的框架中,规避过拟合或欠拟合问题。其次,基于进化学习机制,提出上下2层的交互式优化学习算法,上层基于网络复杂度和测试误差自组织调整网络结构,下层采用列文伯格-马夸尔特(Levenberg Marquardt,LM)算法作为优化器对自组织径向基函数神经网络(self-organizing radial basis function neural network,SO-RBFNN)的连接权值进行优化。最后,利用来自多个子网络的综合信息生成模型的最终输出,加速网络全局收敛。为验证所提方法的可行性,分别在多个分类和预测任务中进行了测试实验。结果表明,在与传统神经网络结构相似甚至更好的测试和分类精度下,该方法不仅能实现更快的训练收敛,而且能进化成更精简紧凑的径向基函数神经网络(radial basis function neural network,RBFNN)模型。尤其在污水处理过程中总磷的质量浓度预测实验中,测试集中均方根误差(root mean squared error,RMSE)最高可降低48.90%,实际场景实验结果验证了所提算法的精确性更佳且泛化能力更强。

From masses and radii of neutron stars to EOS of nuclear matter through neural network

The equation of state(EOS)of dense nuclear matter is a key factor for determining the internal structure and properties of neutron stars.However,the EOS of high-density nuclear matter has great uncertainty,mainly because terrestrial nuclear experiments cannot reproduce matter as dense as that in the inner core of a neutron star.Fortunately,continuous improvements in astronomical observations of neutron stars provide the opportunity to inversely constrain the EOS of high-density nuclear matter.Several methods have been proposed to implement this inverse constraint,including the Bayesian analysis algorithm,the Lindblom’s approach,and so on.Neural network algorithm is an effective method developed in recent years.By employing a set of isospin-dependent parametric EOSs as the training sample of a neural network algorithm,we set up an effective way to reconstruct the EOS with relative accuracy using a few mass-radius data.Based on the obtained neural network algorithms and according to the NICER observations on masses and radii of neutron stars with assumed precision,we obtain the inversely constrained EOS and further calculate the corresponding macroscopic properties of the neutron star.The results are basically consistent with the constraint on EOS in Huth et al.[Nature 606,276(2022)]based on Bayesian analysis.Moreover,the results show that even though the neural network algorithm was obtained using the finite parameterized EOS as the training set,it is valid for any rational parameter combination of the parameterized EOS model.

W^(1,2)(Ω)-and X^(1,2)(Ω)-stability of reactiondiffusion cellular neural networks with delay

With Poincare＇s inequality and auxiliary function applied in a class of retarded cellular neural networks with reaction-diffusion, the conditions of the systems＇ W^1,2（Ω）-exponential and X^1,2（Ω）-asmptotic stability are obtained. The stability conditions containing diffusion term are different from those obtained in the previous papers in their exponential stability conditions. One example is given to illustrate the feasibility of this method in the end.

HYPERSTATIC STRUCTURE MAPPING MODEL BUILDING AND OPTIMIZING DESIGN

Hyperstatic structure plane model being built by structural mechanics is studied. Space model precisely reflected in real stress of the structure is built by finite element method （FEM） analysis commerce software. Mapping model of complex structure system is set up, with convenient calculation just as in plane model and comprehensive information as in space model. Plane model and space model are calculated under the same working condition. Plane model modular construction inner force is considered as input data; Space model modular construction inner force is considered as output data. Thus specimen is built on input data and output dam. Character and affiliation are extracted through training specimen, with the employment of nonlinear mapping capability of the artificial neural network. Mapping model with interpolation and extrpolation is gained, laying the foundation for optimum design. The steel structure of high-layer parking system （SSHLPS） is calculated as an instance. A three-layer back-propagation （BP） net including one hidden layer is constructed with nine input nodes and eight output nodes for a five-layer SSHLPS. The three-layer structure optimization result through the mapping model interpolation contrasts with integrity re-analysis, and seven layers structure through the mapping model extrpulation contrasts with integrity re-analysis. Any layer SSHLPS among 1-8 can be calculated with much accuracy. Amount of calculation can also be reduced if it is appfied into the same topological structure, with reduced distortion and assured precision.

A NEW ALGORITHM FOR PURX O-1 LINEAR PROGRAMS WITH INEQUALITY CONSTRAINTS

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