Hierarchical Identification Method Research Articles

A Hierarchical Identification Method for Lithium-Ion Battery SOC Based on the Hammerstein Model

Two-input one-output Hammerstein model consists of two parallel nonlinear static blocks followed by a linear dynamic part. By using Hammerstein structure to map relation between a battery State of Charge (SOC) and its terminal voltage/current, a hierarchical stochastic gradient algorithm is studied to estimate parameters of Hammerstein SOC model, so as to predict battery SOC. Firstly, the Hammerstein model is transformed into a bilinear parameter system with the least number of required parameters. Then, a hierarchical stochastic gradient algorithm with a forgetting factor is used to update the two sets of model parameters of the bilinear parameter system, so as to realize SOC estimation. Furthermore, the experiment platform of lithium-ion battery was built and the data of the urban dynamometer driving schedule (UDDS) profile and the Los Angeles 92 (LA92) profile were collected. Finally, the MATLAB simulation results show that the proposed parameter optimized method based Hammerstein model has the advantages of fast convergence speed and high SOC estimation accuracy.

Hierarchical recursive signal modeling for multifrequency signals based on discrete measured data

AbstractThis paper studies the problem of parameter estimation for the multifrequency sine signals, which have multiple characteristic parameters such as the amplitudes, phases, and frequencies. It is noted that the signal output is nonlinear with respect to the phase and frequency parameters while it is linear with respect to the amplitude parameters. This feature inspires us to separate all of the characteristic parameters into a linear parameter set and a nonlinear parameter set, where the linear set is composed of the amplitude parameters and the nonlinear set is composed of the phase parameters and the frequency parameters. After the parameter separation, two identification submodels are constructed for optimizing the linear parameter set and the nonlinear parameter set. Then the nonlinear identification model becomes a linear identification submodel and a nonlinear identification submodel. Therefore, the nonlinear optimization for minimizing the objective function is converted into the combination of the quadratic optimization and nonlinear optimization. Based on the separable identification submodels, a recursive least squares subalgorithm and a recursive gradient subalgorithm are proposed for identifying the linear parameters and nonlinear parameters, respectively. Moreover, an interactive estimation algorithm is designed to remove the related parameter sets between the subalgorithms and a hierarchical identification method is presented by combining the subalgorithms. For the purpose of tracking the time‐varying, a forgetting factor is introduced to improve the convergence speed. The numerical examples are provided to qualify the performance of the proposed method based on some performance measures.

An Efficient Hierarchical Identification Method With Kernel-Based SVM for Equivalent Systems of Aircrafts

The identification of low order equivalent system (LOES) models from measured test data is critical to assessing system qualities and design of control law. This paper focuses on the situations with finite data records and the time-response method is employed. In such a case, although least squares (LS) algorithm is efficient for the identification of linear models, it suffers low accuracy in ill-conditioned scenarios. To address this issue, a hierarchical identification method based on the LS algorithm is designed and the optimal solution is attained while maintaining the low computational complexity and small memory requirements. In particular, an LS support vector machine (SVM)-based identification scheme is developed, where the system parameters are estimated in a reproducing kernel Hilbert space. The theoretical analysis and pertaining simulation results demonstrate the effectiveness of the proposed method.

FORMULAIC SEQUENCE(FS) CANNOT BE AN UMBRELLA TERM IN SLA

The termformulaic sequence(FS) is used with a multiplicity of meanings in the SLA literature, some overlapping but others not, and researchers are not always clear in defining precisely what they are investigating, or in limiting the implicational domain of their findings to the type of formulaicity they focus on. The first part of the article provides a conceptual framework focusing on the contrast between linguistic or learner-external definitions, that is, what is formulaic in the language the learner is exposed to, such as idiomatic expressions or collocations, and psycholinguistic or learner-internal definitions, that is, what is formulaic within an individual learner because it presents a processing advantage. The second part focuses on the methodological consequences of adopting a learner-internal approach to the investigation of FSs, and examines the challenges presented by the identification of psycholinguistic formulaicity in advanced L2 learners, proposing a tool kit based on a hierarchical identification method.

Identification of nitrogen, phosphorus, and potassium deficiencies in rice based on static scanning technology and hierarchical identification method.

Establishing an accurate, fast, and operable method for diagnosing crop nutrition is very important for crop nutrient management. In this study, static scanning technology was used to collect images of a rice sample's fully expanded top three leaves and corresponding sheathes. From these images, 32 spectral and shape characteristic parameters were extracted using an RGB mean value function and using the Regionprops function in MATLAB. Hierarchical identification was used to identify NPK deficiencies. First, the normal samples and non-normal (NPK deficiencies) samples were identified. Then, N deficiency and PK deficiencies were identified. Finally, P deficiency and K deficiency were identified. In the identification of every hierarchy, SVFS was used to select the optimal characteristic set for different deficiencies in a targeted manner, and Fisher discriminant analysis was used to build the diagnosis model. In the first hierarchy, the selected characteristics were the leaf sheath R, leaf sheath G, leaf sheath B, leaf sheath length, leaf tip R, leaf tip G, leaf area and leaf G. In the second hierarchy, the selected characteristics were the leaf sheath G, leaf sheath B, white region of the leaf sheath, leaf B, and leaf G. In the third hierarchy the selected characteristics were the leaf G, leaf sheath length, leaf area/leaf length, leaf tip G, difference between the 2nd and 3rd leaf lengths, leaf sheath G, and leaf lightness. The results showed that the overall identification accuracies of NPK deficiencies were 86.15, 87.69, 90.00 and 89.23% for the four growth stages. Data from multiple years were used for validation, and the identification accuracies were 83.08, 83.08, 89.23 and 90.77%.

The Hierarchical Risk Source Identification Method Connected with Event Causal Chain in the Emergency Process of Fire Accident of Petroleum Storage and Transportation

The fire accident of petroleum storage and transportation has the characteristics of difficult disposal, wide range of influence, and a long-term hazard. Many derivative accidents or secondary accidents caused in the emergency process show that there are some potential risk sources in the process of accident emergency disposal. Effectively identifying these potential risk sources is the key for the active protective design in advance, and it is a minimum cost mean to prevent and reduce accident harm. In the paper, from the perspective of analyzing the nature of accident’s development and evolution process, the causal relationship between events is studied based on the accident-causing theory. The event causal chain is built in according with the order of event occurrence. The risk sources of accident are mined and identify from the node of event chain, and the hierarchical identification method oriented accident’s risk sources is established. For accident’s risk sources, the possible design measures in advance can be given from the perspective of principle design, structural design and control design. For design measure’s risk sources in emergency process can be mined by using man-human engineering method, and the graded risk sources identification method oriented secondary accidents is established. Combining the two methods, the hierarchical risk source identification method connected with event causal chain is put forward so as to effectively identify these potential risk sources in emergency process. Finally, oil pipeline explosion accident of dalian’s xinggang harbor on July 16, 2010 is taken as a case to verify the feasibility of proposed method.

A shift-splitting hierarchical identification method for solving Lyapunov matrix equations

In the present paper, we propose a hierarchical identification method (SSHI) for solving Lyapunov matrix equations, which is based on the symmetry and skew-symmetry splitting of the coefficient matrix. We prove that the iterative algorithm consistently converges to the true solution for any initial values with some conditions, and illustrate that the rate of convergence of the iterative solution can be enhanced by choosing the convergence factors appropriately. Furthermore, we show that the method adopted can be easily extended to study iterative solutions of other matrix equations, such as Sylvester matrix equations. Finally, we test the algorithms and show their effectiveness using numerical examples.

A hierarchical identification method based on improved hand geometry and regional content feature for low-resolution hand images

Low-quality images are taking an increasing amount in real-life biometrics identification systems, especially when the working environment is gradually becoming complex and open. Low resolution is a crucial component of low quality. We propose a hierarchical identification method based on improved hand geometry and regional content feature for low-resolution hand images without region of interest (ROI) cropping. At coarse level, angle information is added as a complement to line-based hand geometry. At fine level, relying on the assumption that gradient value of each pixel presents the gray-level changing rate, we develop a simple sequence labeling segmentation method, and choose conditional regions that are relatively steady in segmentation through region area constraint. Because distinctive lines and dense textures always have lower gray-levels than their surrounding areas, regions with lower average gray-levels are selected from conditional regions. Regional centroid coordinates are extracted as feature vectors. Finally, regional spatiality relationship matrix is built up to measure distances between feature vectors with various dimensions. Compared with ROI-based state-of-the-art algorithms, the proposed method utilizes more available information in low-resolution hand images. Performance comparisons between algorithms prove that our method is effective and robust to rotation and translation.