An Efficient Parameterization of Dynamic Neural Networks for Nonlinear System Identification

Dynamic neural networks (DNNs), which are also known as recurrent neural networks, are often used for nonlinear system identification. The main contribution of this letter is the introduction of an efficient parameterization of a class of DNNs. Having to adjust less parameters simplifies the training problem and leads to more parsimonious models. The parameterization is based on approximation theory dealing with the ability of a class of DNNs to approximate finite trajectories of nonautonomous systems. The use of the proposed parameterization is illustrated through a numerical example, using data from a nonlinear model of a magnetic levitation system.

Identification of nonlinear systems with a dynamic recurrent neural network

Two approaches are presented to calculate the weights for a Dynamic Recurrent Neural Network (DRNN) in order to identify the input-output dynamics of a class of nonlinear systems. The number of states of the identified network is constrained to be the same as the number of states of the plant.

An integrated dynamic whole life costing analysis for air distribution systems

Air distribution systems are one of the major electrical energy consumers in air-conditioned commercial buildings which maintain comfortable indoor thermal environment and air quality by supplying specified amounts of treated air into different zones. The sizes of air distribution lines affect energy efficiency of the distribution systems. Equal friction and static regain are two well-known approaches for sizing the air distribution lines. Concerns to life cycle cost of the air distribution systems, T and IPS methods have been developed. Hitherto, all these methods are based on static design conditions. Therefore, dynamic performance of the system has not been yet addressed; whereas, the air distribution systems are mostly performed in dynamic rather than static conditions. Besides, none of the existing methods consider any aspects of thermal comfort and environmental impacts. This study attempts to investigate the existing methods for sizing of the air distribution systems and proposes a dynamic approach for size optimisation of the air distribution lines by taking into account optimisation criteria such as economic aspects, environmental impacts and technical performance. These criteria have been respectively addressed through whole life costing analysis, life cycle assessment and deviation from set-point temperature of different zones. Integration of these criteria into the TRNSYS software produces a novel dynamic optimisation approach for duct sizing. Due to the integration of different criteria into a well- known performance evaluation software, this approach could be easily adopted by designers in busy nature of design. Comparison of this integrated approach with the existing methods reveals that under the defined criteria, system performance is improved up to 15% compared to the existing methods. This approach is interpreted as a significant step forward reaching to the net zero emission building in future.

Closed loop design for a simple nonlinear aircraft model using eigenstructure assignment

Aircraft systems are highly nonlinear and time varying. High-performance aircraft at high angles of incidence experience undesired coupling of the lateral and longitudinal variables, resulting in departure from normal controlled flight. The aim of this work is to construct a robust closed-loop control that optimally extends the stable and decoupled flight envelope. For the study of these systems nonlinear analysis methods are needed. Previously, bifurcation techniques have been used mainly to analyze open-loop nonlinear aircraft models and investigate control effects on dynamic behavior. In this work linear feedback control designs calculated by eigenstructure assignment methods are investigated for a simple aircraft model at a fixed flight condition. Bifurcation analysis in conjunction with linear control design methods is shown to aid control law design for the nonlinear system.

First order k-th moment finite element analysis of nonlinear operator equations with stochastic data

We develop and analyze a class of efficient Galerkin approximation methods for uncertainty quantification of nonlinear operator equations. The algorithms are based on sparse Galerkin discretizations of tensorized linearizations at nominal parameters. Specifically, we consider abstract, nonlinear, parametric operator equations J(\alpha ,u)=0 for random input \alpha (\omega ) with almost sure realizations in a neighborhood of a nominal input parameter \alpha _0. Under some structural assumptions on the parameter dependence, we prove existence and uniqueness of a random solution, u(\omega ) = S(\alpha (\omega )). We derive a multilinear, tensorized operator equation for the deterministic computation of k-th order statistical moments of the random solution's fluctuations u(\omega ) - S(\alpha _0). We introduce and analyse sparse tensor Galerkin discretization schemes for the efficient, deterministic computation of the k-th statistical moment equation. We prove a shift theorem for the k-point correlation equation in anisotropic smoothness scales and deduce that sparse tensor Galerkin discretizations of this equation converge in accuracy vs. complexity which equals, up to logarithmic terms, that of the Galerkin discretization of a single instance of the mean field problem. We illustrate the abstract theory for nonstationary diffusion problems in random domains.

A general-method for tracking analysis and its application to meteorological data

Recent interest in the validation of general circulation models (GCMs) has been devoted to objective methods. A small number of authors have used the direct synoptic identification of phenomena together with a statistical analysis to perform the objective comparison between various datasets. This paper describes a general method for performing the synoptic identification of phenomena that can be used for an objective analysis of atmospheric, or oceanographic, datasets obtained from numerical models and remote sensing. Methods usually associated with image processing have been used to segment the scene and to identify suitable feature points to represent the phenomena of interest. This is performed for each time level. A technique from dynamic scene analysis is then used to link the feature points to form trajectories. The method is fully automatic and should be applicable to a wide range of geophysical fields. An example will be shown of results obtained from this method using data obtained from a run of the Universities Global Atmospheric Modelling Project GCM.

Language processing with dynamic fields

We construct a mapping from complex recursive linguistic data structures to spherical wave functions using Smolensky's filler/role bindings and tensor product representations. Syntactic language processing is then described by the transient evolution of these spherical patterns whose amplitudes are governed by nonlinear order parameter equations. Implications of the model in terms of brain wave dynamics are indicated.

Organizational analysis of building projects

The principles of organization theory are applied to the organization of construction projects. This is done by proposing a framework for modelling the whole process of building procurement. This consists of a framework for describing the environments within which construction projects take place. This is followed by the development of a series of hypotheses about the organizational structure of construction projects. Four case studies are undertaken, and the extent to which their organizational structure matches the model is compared to the level of success achieved by each project. To this end there is a systematic method for evaluating the success of building project organizations, because any conclusions about the adequacy of a particular organization must be related to the degree of success achieved by that organization. In order to test these hypotheses, a mapping technique is developed. The technique offered is a development of a technique known as Linear Responsibility Analysis, and is called "3R analysis" as it deals with roles, responsibilities and relationships. The analysis of the case studies shows that they tended to suffer due to inappropriate organizational structure. One of the prevailing problems of public sector organization is that organizational structures are inadequately defined, and too cumbersome to respond to environmental demands on the project. The projects tended to be organized as rigid hierarchies, particularly at decision points, when what was required was a more flexible, dynamic and responsive organization. The study concludes with a series of recommendations; including suggestions for increasing the responsiveness of construction project organizations, and reducing the lead-in times for the inception periods.

Dynamic behavior of Arabidopsis eif4a-iii, putative core protein of exon junction complex: fast relocation to nucleolus and splicing speckles under hypoxia

Here, we identify the Arabidopsis thaliana ortholog of the mammalian DEAD box helicase, eIF4A-III, the putative anchor protein of exon junction complex (EJC) on mRNA. Arabidopsis eIF4A-III interacts with an ortholog of the core EJC component, ALY/Ref, and colocalizes with other EJC components, such as Mago, Y14, and RNPS1, suggesting a similar function in EJC assembly to animal eIF4A-III. A green fluorescent protein (GFP)-eIF4A-III fusion protein showed localization to several subnuclear domains: to the nucleoplasm during normal growth and to the nucleolus and splicing speckles in response to hypoxia. Treatment with the respiratory inhibitor sodium azide produced an identical response to the hypoxia stress. Treatment with the proteasome inhibitor MG132 led to accumulation of GFP-eIF4A-III mainly in the nucleolus, suggesting that transition of eIF4A-III between subnuclear domains and/or accumulation in nuclear speckles is controlled by proteolysis-labile factors. As revealed by fluorescence recovery after photobleaching analysis, the nucleoplasmic fraction was highly mobile, while the speckles were the least mobile fractions, and the nucleolar fraction had an intermediate mobility. Sequestration of eIF4A-III into nuclear pools with different mobility is likely to reflect the transcriptional and mRNA processing state of the cell.