Bootstrap decentralized identification of large-scale interconnected systems

The proposed method extends the two-stage bootstrap identification method to large-scale interconnected systems. It removes the requirement for information exchange between the subsystems by imbedding the estimation of interaction variables into the state estimation algorithm. Three schemes have been proposed for divergence minimization. An algorithm based on extended Kalman filter has also been proposed for comparison. Numerical results are presented through simulation of a two-machine power system.     

A string-wise CRDT algorithm for smart and large-scale collaborative editing systems

With the development of big data and cloud computing, real-time collaborative editing systems have to face new challenges. How to support string-wise operations for smart and large-scale collaborations is one of the key issues in next generation of collaborative editing systems, which is both the core topic of collaborative computing area and the fundamental research of many collaborative systems in science and engineering. However, string-wise operations have troubled the existing collaborative editing algorithms, including Operational Transformation (OT) and Commutative Replicated Data Type (CRDT), for many years. This paper proposes a novel and efficient CRDT algorithm that integrates string-wise operations for smart and massive-scale collaborations. Firstly, the proposed algorithm ensures the convergence and maintains operation intentions of collaborative users under an integrated string-wise framework. Secondly, formal proofs are provided to prove both the correctness of the proposed algorithm and the intentions preserving of string-wise operations. Thirdly, the time complexity of the proposed algorithm has been analyzed in theory to be lower than that of the state of the art OT algorithm and CRDT algorithm. Fourthly, experiment evaluations show that the proposed algorithm outperforms the state of the art OT algorithm and CRDT algorithm.     

Nonlinear identification of systems with parametric excitation

In this paper, the incremental harmonic balance nonlinear identification (IHBNID) is presented for modelling and parametric identification of nonlinear systems. The effects of harmonic balance nonlinear identification (HBNID) and IHBNID are also studied and compared by using numerical simulation. The effectiveness of the IHBNID is verified through the Mathieu-Duffing equation as an example. With the aid of the new method, the derivation procedure of the incremental harmonic balance method is simplified. The...     

Nonlinear system identification using wavelet networks

A novel identification scheme using wavelet networks is presented for nonlinear dynamical systems. Based on fixed wavelet networks, parameter adaptation laws are developed using a Lyapunov synthesis approach. This guarantees the stability of the overall identification scheme and the convergence of both the parameters and the state errors, even in the presence of modelling errors. Using the decomposition and reconstruction techniques of multiresolution decompositions, variable wavelet networks are introduced to achieve a desired estimation accuracy and a suitable sized network, and to adapt to variations of the characteristics and operating points in nonlinear systems. B-spline wavelets are used to form the wavelet networks and the identification scheme is illustrated using a simulated example.     

A recursive algorithm for identification and control of large-scale systems

The paper presents new recursive algorithms for identification and control of linear time invariant discrete time systems of large size. A variant of the observable canonical form (Weinert and Anton 1972) of the state model is chosen for developing solution of the identification problem. The proposed solution turns out to be computationally much simpler than the existing solutions. It is also shown that centralized and decentralized output feedback controllers can be found by a simple extension of the identification algorithms. The results are illustrated for a system obtained by interconnecting three smaller subsystems.     

Nonlinear system identification with limited time data

With disturbances modeled by arbitrary solutions to a linear homogeneous differential equation, a least squares-equation error method is developed for parameter identification using data over a limited time interval which has application to certain classes of nonlinear and time varying systems. Examples include the Duffing, Hammerstein, Mathieu and Van der Pol equations together with a class of bilinear systems. The technique seeks to determine the parameters characterizing the disturbance modes in addition to the system parameters, based on the input-output data collected over the finite time interval. The approach circumvents the need to estimate unknown initial conditions through the use of a certain projection operator. Computational considerations are discussed and simulation results are summarized for the Van der Pol equation.     

Nonlinear system identification via direct weight optimization

A general framework for estimating nonlinear functions and systems is described and analyzed in this paper. Identification of a system is seen as estimation of a predictor function. The considered predictor function estimate at a particular point is defined to be affine in the observed outputs and the estimate is defined by the weights in this expression. For each given point, the maximal mean-square error (or an upper bound) of the function estimate over a class of possible true functions is minimized with respect to the weights, which is a convex optimization problem. This gives different types of algorithms depending on the chosen function class. It is shown how the classical linear least squares is obtained as a special case and how unknown-but-bounded disturbances can be handled.Most of the paper deals with the method applied to locally smooth predictor functions. It is shown how this leads to local estimators with a finite bandwidth, meaning that only observations in a neighborhood of the target point will be used in the estimate. The size of this neighborhood (the bandwidth) is automatically computed and reflects the noise level in the data and the smoothness priors.The approach is applied to a number of dynamical systems to illustrate its potential.     

Nonlinear system identification of a small-scale unmanned helicopter

This paper presents a comprehensive method for identifying the nonlinear model of a small-scale unmanned helicopter. The model structure is obtained by first principles derivation, and the model parameters are determined by direct measurement and system identification. A new adaptive genetic algorithm is proposed to identify the parameters that cannot be directly measured. To simplify the identification process, the overall system is divided into two subsystems for identification: the heave–yaw dynamics and the lateral–longitudinal dynamics. On the basis of the input–output data collected from actual flight experiments, these two subsystems are identified using the proposed algorithm. The effectiveness of the identified model is verified by comparing the response of the simulation model with the actual response during the flight experiments. Results show that the identified model can accurately predict the response of the small-scale helicopter. Furthermore, the identified model is used for the design of an attitude controller. The experiment results show that the identified model is suitable for controller design.     

Nonlinear systems identification using dynamic multi-time scale neural networks

In this paper, two Neural Network (NN) identifiers are proposed for nonlinear systems identification via dynamic neural networks with different time scales including both fast and slow phenomena. The first NN identifier uses the output signals from the actual system for the system identification. The on-line update laws for dynamic neural networks have been developed using the Lyapunov function and singularly perturbed techniques. In the second NN identifier, all the output signals from nonlinear system are replaced with the state variables of the neuron networks. The on-line identification algorithm with dead-zone function is proposed to improve nonlinear system identification performance. Compared with other dynamic neural network identification methods, the proposed identification methods exhibit improved identification performance. Three examples are given to demonstrate the effectiveness of the theoretical results.     

基于结构自适应滤波方法的非线性系统辨识

针对非线性系统辨识中定结构参数辨识局限性高和辨识率低的问题,将结构自适应引入辨识的优化,提出一种基于子系统的结构自适应滤波（SSAF）方法。该方法的模型由若干子系统级联而成,每一个子系统均为线性-非线性混合结构。子系统的线性部分是一个一阶或二阶可选的无限脉冲响应滤波器（IIR）,非线性部分则是一个静态的非线性函数。初始化中,子系统的参数随机产生,生成的若干子系统按照设定的连接规则进行随机连接,而不含反馈的连接机制确保了非线性系统的有效性。采用一种自适应多精英引导的复合差分进化（AMECoDEs）算法用于自适应模型循环优化,直至找到最优的结构和参数,即全局最优。实验结果表明,SSAF方法在非线性测试函数以及真实数据集上的表现优异,辨识率高且收敛性好,与聚焦时滞递归神经网络（FTLRNN）相比,它所用参数的个数仅为FTLRNN的1/10,且适应值精度提高了7%,验证了所提方法的有效性。     

支持大规模智能电网的数据存储方法

针对大规模智能电网中的监测数据具有海量性、实时性、动态性等特点,提出一种以数据为中心的支持大规模智能电网的数据存储方法:海量动态数据的分层扩展存储机制。首先,采用扩展哈希编码方法动态增加存储节点,避免突发、频发事件数据的丢失,增强系统的可用性;然后,采用多阈值级别方法将数据分散到多个存储节点上,避免出现存储热点问题,实现负载均衡。实验结果表明,分层扩展存储机制能够最大限度地满足海量数据的存储需求,获得较好的负载均衡,并且使总能耗最低,有效地延长了网络的生命周期。     

智能高压开关柜自动识别系统研究

开关柜误操作造成的事故越来越成为影响电力行业安全生产的主要事故类型之一。鉴于此,文章研究了一套智能高压开关柜自动识别系统。本系统中开关柜侧的装置在柜门被打开时可播放语音提示信息,并将开关柜开门关门等事件通过PT2262编码,再无线发送传输到后台。后台接收后,经F330单片机软件解码,并在触摸屏上记录事件的类型和时间。该系统对防止由于工作人员选错开关柜造成的误操作有一定的效果。     

On a two-level identification scheme of large-scale systems for open-loop steady-state optimization

In this paper an attempt is made to combine the problems of the control and modelling of large-scale systems. Starting from the idea of the model-based (open-loop) solution of the two-layer steady-state optimization problem for the noiseless large-scale system, or one corrupted by slow-varying disturbances, an appropriate two-level identification scheme is derived and discussed. The problems connected with its numerical realization are also stated. The considerations are confined to systems with the cascade structure—typical of most production processes—and to the case when the observations of system input/output signals, from which the system model is determined, are noise-free.     

Nonlinear thermal system identification using fractional Volterra series

Linear fractional differentiation models have already proven their efficacy in modeling thermal diffusive phenomena for small temperature variations involving constant thermal parameters such as thermal diffusivity and thermal conductivity. However, for large temperature variations, encountered in plasma torch or in machining in severe conditions, the thermal parameters are no longer constant, but vary along with the temperature. In such a context, thermal diffusive phenomena can no longer be modeled by linear fractional models. In this paper, a new class of nonlinear fractional models based on the Volterra series is proposed for modeling such nonlinear diffusive phenomena. More specifically, Volterra series are extended to fractional derivatives, and fractional orthogonal generating functions are used as Volterra kernels. The linear coefficients are estimated along with nonlinear fractional parameters of the Volterra kernels by nonlinear programming techniques. The fractional Volterra series are first used to identify thermal diffusion in an iron sample with data generated using the finite element method and temperature variations up to 700 K. For that purpose, the thermal properties of the iron sample have been characterized. Then, the fractional Volterra series are used to identify the thermal diffusion with experimental data obtained by injecting a heat flux generated by a 200 W laser beam in the iron sample with temperature variations of 150 K. It is shown that the identified model is always more accurate than the finite element model because it allows, in a single experiment, to take into account system uncertainties.     

Nonlinear system identification using optimized dynamic neural network

In this paper, both off-line architecture optimization and on-line adaptation have been developed for a dynamic neural network (DNN) in nonlinear system identification. In the off-line architecture optimization, a new effective encoding scheme—Direct Matrix Mapping Encoding (DMME) method is proposed to represent the structure of neural network by establishing connection matrices. A series of GA operations are applied to the connection matrices to find the optimal number of neurons on each hidden layer and interconnection between two neighboring layers of DNN. The hybrid training is adopted to evolve the architecture, and to tune the weights and input delays of DNN by combining GA with the modified adaptation laws. The modified adaptation laws are subsequently used to tune the input time delays, weights and linear parameters in the optimized DNN-based model in on-line nonlinear system identification. The effectiveness of the architecture optimization and adaptation is extensively tested by means of two nonlinear system identification examples.     

Reasons for bottlenecks in very large-scale system of systems development

ContextSystem of systems (SoS) is a set or arrangement of systems that results when independent and useful systems are to be incorporated into a larger system that delivers unique capabilities. Our investigation showed that the development life cycle (i.e. the activities transforming requirements into design, code, test cases, and releases) in SoS is more prone to bottlenecks in comparison to single systems.ObjectiveThe objective of the research is to identify reasons for bottlenecks in SoS, prioritize their significance according to their effect on bottlenecks, and compare them with respect to different roles and different perspectives, i.e. SoS view (concerned with integration of systems), and systems view (concerned with system development and delivery).MethodThe research method used is a case study at Ericsson AB.ResultsResults show that the most significant reasons for bottlenecks are related to requirements engineering. All the different roles agree on the significance of requirements related factors. However, there are also disagreements between the roles, in particular with respect to quality related reasons. Quality related hinders are primarily observed and highly prioritized by quality assurance responsibles. Furthermore, SoS view and system view perceive different hinders, and prioritize them differently.ConclusionWe conclude that solutions for requirements engineering in SoS context are needed, quality awareness in the organization has to be achieved end to end, and views between SoS and system view need to be aligned to avoid sub optimization in improvements.     

An efficient low vision plant leaf shape identification system for smart phones

Multimedia Tools and Applications - In computer vision research, the first most important step is to represent the captured object into some mathematical transformed feature vector describing the...     

Nonlinear system identification with continuous piecewise linear neural network

This paper considers system identification using domain partition based continuous piecewise linear neural network (DP-CPLNN), which is newly proposed. DP-CPLNN has the capability of representing any continuous piecewise linear (CPWL) function, hence its identification performance can be expected. Another attractive feature of DP-CPLNN is the geometrical property of its parameters. Applying this property, this paper proposes an identification method including domain partition and parameter training. In numerical experiments, DP-CPLNN with this method outperforms hinging hyperplanes and high-level canonical piecewise linear representation, which are two widely used CPWL models, showing the flexibility of DP-CPLNN and the effectiveness of the proposed algorithm in nonlinear identification.     

Nonlinear system identification using a Gabor/Hopfield network

This paper presents a method of nonlinear system identification using a new Gabor/Hopfield network. The network can identify nonlinear discrete-time models that are affine linear in the control. The system need not be asymptotically stable but must be bounded-input-bounded-output (BIBO) stable for the identification results to be valid in a large input-output range. The network is a considerable improvement over earlier work using Gabor basis functions (GBF's) with a back-propagation neural network. Properties of the Gabor model and guidelines for achieving a global error minimum are derived. The new network and its use in system identification are investigated through computer simulation. Practical problems such as local minima, the effects of input and initial conditions, the model sensitivity to noise, the sensitivity of the mean square error (MSE) to the number of basis functions and the order of approximation, and the choice of forcing function for training data generation are considered.     

Nonlinear system identification using BBO-based multilayer perceptron network method

Microsystem Technologies - Recently, nonlinear system identification has received increasingly more attention due to its promising applications in engineering fields. It has become a challenging...