H∞ filtering for nonlinear systems via neural networks

A novel H_∞ filter design methodology has been presented for a general class of nonlinear systems. Different from existing nonlinear filtering design, the nonlinearities are approximated using neural networks, and then are modeled based on linear difference inclusions, which makes the structure of the desired filter simpler and parameter turning easier and has the advantages of guaranteed stability, numeral robustness, bounded estimation accuracy. A unified framework is established to solve the addressed H_∞ filtering problem by exploiting linear matrix inequality (LMI) approach. A numerical example shows that the filtering error systems will work well against bounded error between a nonlinear dynamical system and a multilayer neural network.     

基于BP神经网络的管道中泥浆输运模拟研究

BP人工神经网络是神经网络中应用最广泛的一种同络模型,本文详细介绍了BP网络模型的建立和算法过程及一些改进算法.利用BP神经网络建立管道泥浆输运中阻力与泥浆浓度和流速之间关系的预测模型.验证表明,运用BP神经网络模型可以建立精度较高的非线性动力关系.     

Incorporating context in text analysis by interactive activation with competition artificial neural networks

Many segments of modern society, including marketing, politics, government, activism and public safety, desire the ability to find relationships, thus meaning, in public discourse. This can be accomplished by analyzing communication documents according to their content. The increasing use of the Internet for public dialog has made Internet communication a potentially rich source of data in this regard. This study explores the use of an interactive activation with competition (IAC) artificial neural network (ANN) to find relationships in email texts. A modified fully recurrent IAC network was used to process 69 email messages from two threads in the Open Library/Information Science Education Forum using two variations of the self-organizing phase of network formation. These variations were: (1) with and (2) without a linear decay function applied between sentences to the external activation of nodes. The use of the linear decay function, which could be considered a method for including context, produced three positive effects: the entire network was more differentiated from keywords; the keywords were more highly associated with each other, and; roughly half the number of noise strings were highly associated with keywords.     

The solutions of vibration control problems using artificial neural networks

This paper introduces an alternative method artificial neural networks (ANN) used to obtain numerical solutions of mathematical models of dynamic systems, represented by ordinary differential equations (ODEs) and partial differential equations (PDEs). The proposed trial solution of differential equations (DEs) consists of two parts: The initial and boundary conditions (BCs) should be satisfied by the first part. However, the second part is not affected from initial and BCs, but it only tries to satisfy DE. This part involves a feedforward ANN containing adjustable parameters (weight and bias). The proposed solution satisfying boundary and initial condition uses a feedforward ANN with one hidden layer varying the neuron number in the hidden layer according to complexity of the considered problem. The ANN having appropriate architecture has been trained with backpropagation algorithm using an adaptive learning rate to satisfy DE. Moreover, we have, first, developed the general formula for the numerical solutions of nth-order initial-value problems by using ANN.For numerical applications, the ODEs that are the mathematical models of linear and non-linear mass-damper-spring systems and the second- and fourth-order PDEs that are the mathematical models of the control of longitudinal vibrations of rods and lateral vibrations of beams have been considered. Finally, the responses of the controlled and non-controlled systems have been obtained. The obtained results have been graphically presented and some conclusion remarks are given.     

石油专用设备投资评价的神经网络模型及实证研究

建立基于人工神经网络的石油专用设备投资评价模型,并利用大庆油田的历史数据对模型进行训练和模拟,以向石油企业提供科学合理的石油采油设备投资决策方法。     

Decentralized neural identification and control for uncertain nonlinear systems: Application to planar robot

This paper presents a discrete-time decentralized neural identification and control for large-scale uncertain nonlinear systems, which is developed using recurrent high order neural networks (RHONN); the neural network learning algorithm uses an extended Kalman filter (EKF). The discrete-time control law proposed is based on block control and sliding mode techniques. The control algorithm is first simulated, and then implemented in real time for a two degree of freedom (DOF) planar robot.     

基于神经网络的风险投资风险预警研究

构建风险投资家在方案评估阶段的风险预警准则体系,并运用神经网络建立风险预警模型,对风险投资的潜在风险进行科学的评判。实证分析表明,该模型能较好地对风险投资面临的风险进行评估和预警。     

Stabilization effect of diffusion in delayed neural networks systems with Dirichlet boundary conditions

In this paper, reaction–diffusion neural networks with unbounded time-varying delays and Dirichlet boundary conditions is studied. A new concept of global μ-stability in the sense of L² norm is introduced, and sufficient conditions are given to guarantee global μ-stability of the equilibrium point. The results obtained not only improve those in the earlier findings, but also show diffusion terms contribute to stabilization of neural networks systems.     

Decentralized adaptive output-feedback control of interconnected nonlinear time-delay systems using minimal neural networks

This paper presents a minimal-neural-networks-based design approach for the decentralized output-feedback tracking of uncertain interconnected strict-feedback nonlinear systems with unknown time-varying delayed interactions unmatched in control inputs. Compared with existing approximation-based decentralized output-feedback designs using multiple neural networks for each subsystem in lower triangular form, the main contribution of this paper is to provide a new recursive backstepping strategy for a local memoryless output-feedback controller design using only one neural network for each subsystem regardless of the order of subsystems, unmeasurable states, and unknown unmatched and delayed nonlinear interactions. In the proposed strategy, error surfaces are designed using unmeasurable states instead of measurable states and virtual controllers are regarded as intermediate signals for designing a local control law at the last step. Using Lyapunov stability theorem and the performance function technique, it is shown that all signals of the total controlled closed-loop system are bounded and the transient and steady-state performance bounds of local tracking errors can be preselected by adjusting design parameters independent of delayed interactions.     

基于人工神经网络的公路工程投资预测模型的研究

针对目前我国的高等级公路建设过程中普遍存在着投资失控、决算超预算、预算超概算、概算超估算现象越来越严重的问题。提出了从介绍公路工程投资原理和现行的公路投资体系存在的问题出发，将国内现在使用的公路工程投资预测模型加以对比和分析，探索了利用人工神经网络在公路投资预测领域建立新的模型，以此提高预测精确度，改变投资失控的现状。     

Distributed cooperative learning for a group of uncertain systems via output feedback and neural networks

This paper studies the problem of adaptive neural network (NN) output-feedback control for a group of uncertain nonlinear multi-agent systems (MASs) from the viewpoint of cooperative learning. It is assumed that all MASs have identical unknown nonlinear dynamic models but carry out different periodic control tasks, i.e., each agent system has its own periodic reference trajectory. By establishing a network topology among systems, we propose a new consensus-based distributed cooperative learning (DCL) law for the unknown weights of radial basis function (RBF) neural networks appearing in output-feedback control laws. The main advantage of such a learning scheme is that all estimated weights converge to a small neighborhood of the optimal value over the union of all system estimated state orbits. Thus, the learned NN weights have better generalization ability than those obtained by traditional NN learning laws. Our control approach also guarantees the convergence of tracking errors and the stability of closed-loop system. Under the assumption that the network topology is undirected and connected, we give a strict proof by verifying the cooperative persisting excitation condition of RBF regression vectors. This condition is defined in our recent work and plays a key role in analyzing the convergence of adaptive parameters. Finally, two simulation examples are provided to verify the effectiveness and advantages of the control scheme proposed in this paper.     

Distributed consensus control of periodically time-varying multi-agent systems using neural networks and fourier series expansion

This paper studies the consensus of nonlinear multi-agent systems with periodic disturbances and uncertain dynamics based on matrix theory, adaptive control, neural networks and fourier series expansion. Firstly, fourier series expansion and neural networks are used to describe the unknown periodic time-varying parameter and uncertain nonlinear dynamic, respectively. Secondly, based on adaptive control technology and reparameterization method, two new fully distributed control protocols are designed based on symbolic function and smooth hyperbolic tangent function, respectively, so that all agents can reach asymptotic consensus. Thirdly, a new positive integral bounded function is introduced to compensate for the approximation error caused by the smooth hyperbolic tangent function instead of the symbolic function, so that all network nodes achieve the same consensus effect. Finally, a simulation example is given to verify the effectiveness of the two algorithms and to illustrate their advantages and disadvantages.     

Solving initial-boundary value problems for systems of partial differential equations using neural networks and optimization techniques

A general system of the time-dependent partial differential equations containing several arbitrary initial and boundary conditions is considered. A hybrid method based on artificial neural networks, minimization techniques and collocation methods is proposed to determine a related approximate solution in a closed analytical form. The optimal values for the corresponding adjustable parameters are calculated. An accurate approximate solution is obtained, that works well for interior and exterior points of the original domain. Numerical efficiency and accuracy of the hybrid method are investigated by two-test problems including an initial value and a boundary value problem for the two-dimensional biharmonic equation.     

基于BP神经网络的中国沿海地区海洋经济系统脆弱性时空分异研究

海洋经济系统脆弱性研究的最终目的是降低脆弱性,以实现海洋经济的可持续发展。基于海洋经济系统脆弱性的内涵,本文从敏感性与应对能力两方面构建海洋经济系统脆弱性指标体系,运用BP人工神经网络模型、脆弱性评价指数模型、障碍度评价公式等对2006-2011年中国沿海地区海洋经济系统的脆弱性时空演变特征、影响因素进行评价分析,结果表明：①中国海洋经济系统敏感性与应对能力走势一致,呈现出整体微降增加、局部突变,脆弱性变化平缓的特征;②中国沿海地区的脆弱性类型空间分布整体分散、部分连片集中,空间分异明显,其中东北沿海地区与华东沿海地区脆弱性逐渐降低,华北沿海地区与华南沿海地区呈现内部两极分异的格局;③2006- 2011年,中国海洋经济系统脆弱性呈现出三个阶段性特征,即脆弱性渐高型（广西、河北、福建）、脆弱性渐低型（广东、天津、上海、辽宁）、脆弱性平稳型（山东、江苏、海南、浙江）。最后本文通过对沿海各地区海洋经济系统脆弱性影响因子的分析,提出针对性的海洋经济发展方向。     

Quasi-synchronization of coupled neural networks with reaction-diffusion terms driven by fractional brownian motion

This paper investigates the quasi-synchronization of reaction-diffusion neural networks with hybrid coupling and parameter mismatches via sampled-data control technology. First, the models of neural networks with switching parameter and fraction Brownian motion are given. As a result of parameter mismatches, synchronization is normally not possible to realize directly, then the improved Halanay’s inequality is introduced, which is an important lemma to prove that the considered networks realize quasi-synchronization. Furthermore, based on stochastic theory, Lyapunov function method and inequality techniques, some sufficient conditions are derived to guarantee the quasi-synchronization of hybrid coupled neural networks with reaction-diffusion terms driven by fractional Brownian motion. Finally, two simulation examples are given to prove the efficiency of the developed criteria.     

Adaptive neural finite-time control of nonlinear systems subject to sensor hysteresis

In this paper, an adaptive neural control scheme is proposed for a class of unknown nonlinear systems with unknown sensor hysteresis. The radial basis function neural networks are employed to approximate the unknown nonlinearities and the backstepping technique is implemented to construct controllers. The difficulty of the control design lies in that the genuine states of the system are not available for feedback, which is caused by sensor hysteresis. The proposed control scheme eventually ensures the practical finite-time stability of the closed-loop system, which is proved by the Lyapunov theory. A numerical simulation example is included to verify the effectiveness of the developed approach.     

Filtering search results using an optimal set of terms identified by an artificial neural network

Information filtering (IF) systems usually filter data items by correlating a set of terms representing the user’s interest (a user profile) with similar sets of terms representing the data items. Many techniques can be employed for constructing user profiles automatically, but they usually yield large sets of term. Various dimensionality-reduction techniques can be applied in order to reduce the number of terms in a user profile. We describe a new terms selection technique including a dimensionality-reduction mechanism which is based on the analysis of a trained artificial neural network (ANN) model. Its novel feature is the identification of an optimal set of terms that can classify correctly data items that are relevant to a user. The proposed technique was compared with the classical Rocchio algorithm. We found that when using all the distinct terms in the training set to train an ANN, the Rocchio algorithm outperforms the ANN based filtering system, but after applying the new dimensionality-reduction technique, leaving only an optimal set of terms, the improved ANN technique outperformed both the original ANN and the Rocchio algorithm.     

Global h-synchronization of stochastic delayed high-order inertial neural networks subject to Markovian jump parameters

As a first exploration, this paper proposed the second-order response system (SORS) method to study the global h-synchronization (SGhS) for high-order stochastic delayed inertial neural networks subject to Markovian jumping parameters. Different from previous studies, this paper avoids reducing the order of the original drive system via substitution of variables, and directly gives the corresponding SORS. In the following, under the framework of the SORS method, a regulation function dependent Lyapunov–Krasovskii functional (RFD–LKF) is designed to realize that the considered dynamics are globally mean square h-synchronous. Particularly worth mentioning is that the method proposed in this paper can greatly reduce the amount of calculation and control cost. Ultimately, via a typical example, the superiority and validity of the derived h-synchronous criterion can be well demonstrated.