FIXED/PREASSIGNED-TIME SYNCHRONIZATION OF QUATERNION-VALUED NEURAL NETWORKS INVOLVING DELAYS AND DISCONTINUOUS ACTIVATIONS: A DIRECT APPROACH

The fixed-time synchronization and preassigned-time synchronization are investigated for a class of quaternion-valued neural networks with time-varying delays and discontinuous activation functions. Unlike previous efforts that employed separation analysis and the real-valued control design, based on the quaternion-valued signum function and several related properties, a direct analytical method is proposed here and the quaternion-valued controllers are designed in order to discuss the fixed-time synchronization for the relevant quaternion-valued neural networks. In addition, the preassigned-time synchronization is investigated based on a quaternion-valued control design, where the synchronization time is preassigned and the control gains are finite. Compared with existing results, the direct method without separation developed in this article is beneficial in terms of simplifying theoretical analysis, and the proposed quaternion-valued control schemes are simpler and more effective than the traditional design, which adds four real-valued controllers. Finally, two numerical examples are given in order to support the theoretical results.

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.

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.

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.

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.

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.

Ship Fuel and Carbon Emission Estimation Utilizing Artificial Neural Network and Data Fusion Techniques

Ship energy consumption and emission prediction are the main concern of the shipping industry for ship energy efficiency management and pollution gas emission control. And they are attracting more global attention and research interests because of the increase in global shipping trade volume. As the core of maritime transportation, a large volume of data is collected around ships such as voyage data. Due to the rapid development of computational power and the widely equipped AIS device on ships, the use of maritime big data for improving and monitoring ship’s energy efficiency is becoming possible. In this paper, a fuel consumption and carbon emission model using the artificial neural network (ANN) framework is proposed by using AIS, ship machinery, and weather data. The proposed work is a complete framework including data collection, data cleaning, data clustering and model-building methodology. To obtain the suitable parameters of the model, the number of neurons, data inputs and activate functions were tested on both AIS-based data and MRV-based data for comparison. The results show that the proposed method can provide a solid prediction of ship’s fuel consumption and carbon emissions under varying weather conditions.

Mittag-Leffler stability analysis of multiple equilibrium points in impulsive fractional-order quaternion-valued neural networks

In this study,we investigate the problem of multiple Mittag-Leffler stability analysis for fractional-order quaternion-valued neural networks(QVNNs)with impulses.Using the geometrical properties of activation functions and the Lipschitz condition,the existence of the equilibrium points is analyzed.In addition,the global Mittag-Leffler stability of multiple equilibrium points for the impulsive fractional-order QVNNs is investigated by employing the Lyapunov direct method.Finally,simulation is performed to illustrate the effectiveness and validity of the main results obtained.

Almost Automorphic Solutions for Quaternion-Valued Hopfield Neural Networks with Mixed Time-Varying Delays and Leakage Delays

This paper considers a class of quaternion-valued Hopfield neural networks with mixed time-varying delays and leakage delays.By utilizing the exponential dichotomy of linear differential equations,Banach’s fixed point theorem and differential inequality techniques,the authors obtain some sufficient conditions to ensure the existence and global exponential stability of almost automorphic solutions for this class of quaternion-valued neural networks.The results are completely new.Finally,the authors give an example to illustrate the feasibility of the results.

基于信号图像化和CNN-ResNet的配电网单相接地故障选线方法

配电网发生单相接地故障时,零序电流呈现较强的非线性与非平稳性,故障选线较为困难,针对此问题,提出一种基于信号图像化和卷积神经网络-残差网络的配电网单相接地故障选线方法。首先,利用排列熵优化变分模态分解算法的参数,将零序电流信号分解成一系列固有模态函数;其次,引入新的数据预处理方式,将固有模态函数转成二维图像,获得零序电流信号的时频特征图;最后,利用一维卷积神经网络提取零序电流信号的相关性和特征,利用残差网络提取时频特征图的特征,将两个网络融合,构建混合卷积神经网络结构,实现故障选线。仿真与实验结果表明,该方法能够在高阻接地、采样时间不同步、强噪声等情况下准确地选择出故障线路,可满足配电网对故障选线准确性和可靠性的需求。

基于事件触发控制的中立型神经网络簇同步

研究了事件触发下带有Lévy噪声的中立型耦合神经网络的簇同步问题。首先,在混合时滞和不连续噪声的条件下,提出了一种事件触发机制与牵制控制方法结合的控制策略,应用于神经网络的簇同步中,以提高簇同步效率,缓解网络负担。然后,通过构建新的Lyapunov泛函,以及使用线性矩阵不等式(linear matrix inequalities,LMI)分析技术和广义Dynkin公式,得到误差系统的簇同步准则,进一步保证了耦合神经网络的簇同步。最后,通过一个数值仿真,验证了所得结论的可行性。

Exponential stability analysis for neutral BAM neural networks with time-varying delays and stochastic disturbances

This paper is concerned with the global exponential stability analysis problem for a class of neutral bidi- rectional associative memory （BAM） neural networks with time-varying delays and stochastic disturbances. The stochastic disturbances are described by state-dependent stochastic processes. By utilizing an appropriately constructed Lyapunov- Krasovskii functional （LKF） and some stochastic analysis approaches, novel delay-dependent conditions are established in terms of linear matrix inequalities （LMIs）, which can be easily solved by existing convex optimization techniques. Further- more, the exponential convergence rate can be estimated based on the obtained results. An illustrate example is given to demonstrate the effectiveness of the proposed methods.

Bogdanov–Takens Bifurcation in a Neutral Delayed Hopfield Neural Network with Bidirectional Connection

In this paper,a neutral Hopfield neural network with bidirectional connection is considered.In the first step,by choosing the connection weights as parameters bifurcation,the critical point at which a zero root of multiplicity two occurs in the characteristic equation associated with the linearized system.In the second step,we studied the zeros of a third degree exponential polynomial in order to make sure that except the double zero root,all the other roots of the characteristic equation have real parts that are negative.Moreover,we find the critical values to guarantee the existence of the Bogdanov–Takens bifurcation.In the third step,the normal form is obtained and its dynamical behaviors are studied after the use of the reduction on the center manifold and the theory of the normal form.Furthermore,for the demonstration of our results,we have given a numerical example.

航空碳市场与可持续航空燃油协同减排效益研究

研究旨在通过综合分析可持续航空燃料的应用潜力和航空碳市场机制,评估二者协同作用下的减排效益,以为中国民航业的绿色转型提供策略和政策支持。首先,利用遗传算法优化的反向传播神经网络(Genetic Algorithm Optimized Backpropagation Neural Network,GA-BP)模型对我国民航2025—2060年航空燃油需求进行分阶段预测;其次,在此基础上构建基于未来能源需求的CO_(2)排放模型;最后,构建航空碳市场减排模型并分析不同碳市场与SAF配额组合下的协同减排效益。结果显示:(1)在高应用情景下,到2060年我国可持续航空燃油(Sustainable Aviation Fuel,SAF)需求量将达到6900万t,同时,相比于基准情景最高可减少87.4%的碳排放量;(2)SAF应用的增加会对民航业造成巨大的减排成本压力,在2025—2060年,航空公司为使用SAF脱碳需付出的额外减排成本将达到124719亿元;(3)在免费碳配额比例为0.8、碳价达到500元/t时,航空公司通过碳市场减排可以获得13148.83亿元。当碳配额过低时,航空公司无法通过碳市场减排获利,甚至为了满足碳市场减排要求还会付出额外的减排成本。因此,SAF应用的增加可以有效降低民航业的碳排放量,但也会给行业带来显著的经济压力;在合适的碳市场机制下,航空公司可以通过SAF减排获利。建议政府在提供必要的财政补贴和税收优惠的同时,优化碳市场政策,以确保航空公司在减排过程中的经济可行性。

Global Exponential Convergence of Neutral Type Competitive Neural Networks with D Operator and Mixed Delay

The models of competitive neural network(CNN)was in recent past proposed to describe the dynamics of cortical cognitive maps with unsupervised synaptic modifications,where there are two types of memories:Long-term memories(LTM)and short-term memories(STM),LTM presents unsupervised and slow synaptic modifications and STM characterize the fast neural activity.This paper is concerned with a class of neutral type CNN’s with mixed delay and D operator.By employing the appropriate differential inequality theory,some sufficient conditions are given to ensure that all solutions of the model converge exponentially to zero vector.Finally,an illustrative example is also given at the end of this paper to show the effectiveness of the proposed results.

具有比例时滞和D算子的BAM神经网络概周期解的存在性与稳定性

为了探讨一类含比例时滞和D算子的中立型BAM神经网络的概周期解的存在性与广义指数稳定性,首先,通过构造概周期函数空间,并利用压缩不动点原理,得到确保系统概周期解存在的充分条件,其次,使用微分不等式技巧得到确保系统概周期解广义指数稳定的充分条件.发现若系统的参数满足一定的条件,那么系统的概周期解是存在的,且是广义指数稳定的,而且所得结果与比例时滞无关.所得结论推进了现有文献中的相关工作.

基于二次曲面和BP神经网络组合模型的GPS高程异常拟合

将二次曲面模型和BP神经网络的组合模型应用于高程异常拟合中,其组合方式分别基于方差倒数法和广义回归神经网络。利用某地区实测的GPS高程数据进行比较分析,结果表明,组合模型逼近高程异常的精度和可靠性均优于单一模型,并且基于广义回归神经网络的组合模型的拟合精度高于基于方差倒数法的组合模型。

模糊神经网络在小电流接地系统选线中的应用

用模糊神经网络理论重点改进了小电流接地选线原理中传统的“零序电流比幅”、“零序有功分量比幅”、“能量法”等方法 ,用仿真结果代入算法公式计算出样本 ,将其送入模糊极大—极小神经网络进行训练。训练结果表明 ,提出的方法对中性点非直接接地系统发生的单相直接接地和经过渡电阻接地故障都可正确选线 ,且判别依据不受系统结构和运行方式变化的影响 ,选线的正确度和可靠性均有明显的提高。

基于混沌DNA遗传算法的模糊递归神经网络建模

本文受生物DNA分子遗传机制和混沌优化算法的启发,提出了一种混沌DNA遗传算法,用于优化T-S模糊递归神经网络(FRNN).该方法使用碱基序列表示T-S模糊递归神经网络的前件部分参数,包括模糊规则数,隶属度函数中心点和宽度;设计更为复杂的遗传操作算子来改进遗传算法的寻优性能;利用混沌优化算法优化种群中的较差个体.同时使用递推最小二乘法(RLS)来辨识T-S模糊递归神经网络的后件部分参数.最后,采用基于混沌DNA遗传算法的T-S模糊递归神经网络对一种典型的pH中和过程进行建模。通过与其他建模方法的比较,仿真实验结果表明了所建模型的有效性.

岩体微裂隙注浆量预测分析的遗传神经网络方法

提出一种用于岩体微裂隙注浆预测新方法———遗传神经网络方法 ,即用遗传算法优化神经网络结构 ,提高神经网络预测能力的新方法。实际工程实例表明 ,该方法具有预测速度快、精度高、实用性强的特点 。