Dual-orthogonal radial basis function networks for nonlinear time series prediction

A new structure of Radial Basis Function (RBF) neural network called the Dual-orthogonal RBF Network (DRBF) is introduced for nonlinear time series prediction. The hidden nodes of a conventional RBF network compare the Euclidean distance between the network input vector and the centres, and the node responses are radially symmetrical. But in time series prediction where the system input vectors are lagged system outputs, which are usually highly correlated, the Euclidean distance measure may not be appropriate. The DRBF network modifies the distance metric by introducing a classification function which is based on the estimation data set. Training the DRBF networks consists of two stages. Learning the classification related basis functions and the important input nodes, followed by selecting the regressors and learning the weights of the hidden nodes. In both cases, a forward Orthogonal Least Squares (OLS) selection procedure is applied, initially to select the important input nodes and then to select the important centres. Simulation results of single-step and multi-step ahead predictions over a test data set are included to demonstrate the effectiveness of the new approach.

Coordination behavior of symmetrical hexadentate O2N2S2-donor Schiff bases toward zinc (II): synthesis, characterization, and crystal structure

Two series of zinc(II) complexes of two Schiff bases (H2L1 and H2L2) formulated as [Zn(HL1/HL2)]ClO4 (1a and 1b) and [Zn(L1/L2)] (2a and 2b), where H2L1 = 1,8-bis(salicylideneamino)-3,6-dithiaoctane and H2L2 = 1,9-bis(salicylideneamino)-3,7-dithianonane, have been prepared and isolated in pure form by changing the chemical environment. Elemental, spectral, and other physicochemical results characterize the complexes. A single crystal X-ray diffraction study confirms the structure of [Zn(HL1)]ClO4 (1a). In 1a, zinc(II) has a distorted octahedral environment with a ZnO2N2S2 chromophore.

Minimising the latency induced by consistency control, within a large scale multi-user distributed virtual reality system

Where users are interacting in a distributed virtual environment, the actions of each user must be observed by peers with sufficient consistency and within a limited delay so as not to be detrimental to the interaction. The consistency control issue may be split into three parts: update control; consistent enactment and evolution of events; and causal consistency. The delay in the presentation of events, termed latency, is primarily dependent on the network propagation delay and the consistency control algorithms. The latency induced by the consistency control algorithm, in particular causal ordering, is proportional to the number of participants. This paper describes how the effect of network delays may be reduced and introduces a scalable solution that provides sufficient consistency control while minimising its effect on latency. The principles described have been developed at Reading over the past five years. Similar principles are now emerging in the simulation community through the HLA standard. This paper attempts to validate the suggested principles within the schema of distributed simulation and virtual environments and to compare and contrast with those described by the HLA definition documents.

Using zero-norm constraint for sparse probability density function estimation

A new sparse kernel probability density function (pdf) estimator based on zero-norm constraint is constructed using the classical Parzen window (PW) estimate as the target function. The so-called zero-norm of the parameters is used in order to achieve enhanced model sparsity, and it is suggested to minimize an approximate function of the zero-norm. It is shown that under certain condition, the kernel weights of the proposed pdf estimator based on the zero-norm approximation can be updated using the multiplicative nonnegative quadratic programming algorithm. Numerical examples are employed to demonstrate the efficacy of the proposed approach.

Multi-layer radial basis function networks: an extension to the radial basis function

This paper presents the initial research carried out into a new neural network called the multilayer radial basis function network (MRBF). The network extends the radial basis function (RBF) in a similar way to that in which the multilayer perceptron extends the perceptron. It is hoped that by connecting RBFs together in a layered fashion, an equivalent increase in ability can be gained, as is gained from using MLPs instead of single perceptrons. The results of a practical comparison between individual RBFs and MRBF's are also given.

Neural network basis function center selection using cluster analysis

This paper deals with the selection of centres for radial basis function (RBF) networks. A novel mean-tracking clustering algorithm is described as a way in which centers can be chosen based on a batch of collected data. A direct comparison is made between the mean-tracking algorithm and k-means clustering and it is shown how mean-tracking clustering is significantly better in terms of achieving an RBF network which performs accurate function modelling.

Distributed fuzzy logic for building management systems using local operating networks

This paper describes the development of an experimental distributed fuzzy control system for heating and ventilation (HVAC) systems within a building. Each local control loop is affected by a number of local variables, as well as information from neighboring controllers. By including this additional information it is hoped that a more equal allocation of resources can be achieved.

Conditioning and preconditioning of the variational data assimilation problem

Numerical weather prediction (NWP) centres use numerical models of the atmospheric flow to forecast future weather states from an estimate of the current state. Variational data assimilation (VAR) is used commonly to determine an optimal state estimate that miminizes the errors between observations of the dynamical system and model predictions of the flow. The rate of convergence of the VAR scheme and the sensitivity of the solution to errors in the data are dependent on the condition number of the Hessian of the variational least-squares objective function. The traditional formulation of VAR is ill-conditioned and hence leads to slow convergence and an inaccurate solution. In practice, operational NWP centres precondition the system via a control variable transform to reduce the condition number of the Hessian. In this paper we investigate the conditioning of VAR for a single, periodic, spatially-distributed state variable. We present theoretical bounds on the condition number of the original and preconditioned Hessians and hence demonstrate the improvement produced by the preconditioning. We also investigate theoretically the effect of observation position and error variance on the preconditioned system and show that the problem becomes more ill-conditioned with increasingly dense and accurate observations. Finally, we confirm the theoretical results in an operational setting by giving experimental results from the Met Office variational system.

The variability of tropical ice cloud properties as a function of the large-scale context from ground-based radar-lidar observations over Darwin, Australia

The high complexity of cloud parameterizations now held in models puts more pressure on observational studies to provide useful means to evaluate them. One approach to the problem put forth in the modelling community is to evaluate under what atmospheric conditions the parameterizations fail to simulate the cloud properties and under what conditions they do a good job. It is the ambition of this paper to characterize the variability of the statistical properties of tropical ice clouds in different tropical "regimes" recently identified in the literature to aid the development of better process-oriented parameterizations in models. For this purpose, the statistical properties of non-precipitating tropical ice clouds over Darwin, Australia are characterized using ground-based radar-lidar observations from the Atmospheric Radiation Measurement (ARM) Program. The ice cloud properties analysed are the frequency of ice cloud occurrence, the morphological properties (cloud top height and thickness), and the microphysical and radiative properties (ice water content, visible extinction, effective radius, and total concentration). The variability of these tropical ice cloud properties is then studied as a function of the large-scale cloud regimes derived from the International Satellite Cloud Climatology Project (ISCCP), the amplitude and phase of the Madden-Julian Oscillation (MJO), and the large-scale atmospheric regime as derived from a long-term record of radiosonde observations over Darwin. The vertical variability of ice cloud occurrence and microphysical properties is largest in all regimes (1.5 order of magnitude for ice water content and extinction, a factor 3 in effective radius, and three orders of magnitude in concentration, typically). 98 % of ice clouds in our dataset are characterized by either a small cloud fraction (smaller than 0.3) or a very large cloud fraction (larger than 0.9). In the ice part of the troposphere three distinct layers characterized by different statistically-dominant microphysical processes are identified. The variability of the ice cloud properties as a function of the large-scale atmospheric regime, cloud regime, and MJO phase is large, producing mean differences of up to a factor 8 in the frequency of ice cloud occurrence between large-scale atmospheric regimes and mean differences of a factor 2 typically in all microphysical properties. Finally, the diurnal cycle of the frequency of occurrence of ice clouds is also very different between regimes and MJO phases, with diurnal amplitudes of the vertically-integrated frequency of ice cloud occurrence ranging from as low as 0.2 (weak diurnal amplitude) to values in excess of 2.0 (very large diurnal amplitude). Modellers should now use these results to check if their model cloud parameterizations are capable of translating a given atmospheric forcing into the correct statistical ice cloud properties.

Communication mechanisms and middleware for distributed video surveillance

A new generation of advanced surveillance systems is being conceived as a collection of multi-sensor components such as video, audio and mobile robots interacting in a cooperating manner to enhance situation awareness capabilities to assist surveillance personnel. The prominent issues that these systems face are: the improvement of existing intelligent video surveillance systems, the inclusion of wireless networks, the use of low power sensors, the design architecture, the communication between different components, the fusion of data emerging from different type of sensors, the location of personnel (providers and consumers) and the scalability of the system. This paper focuses on the aspects pertaining to real-time distributed architecture and scalability. For example, to meet real-time requirements, these systems need to process data streams in concurrent environments, designed by taking into account scheduling and synchronisation. The paper proposes a framework for the design of visual surveillance systems based on components derived from the principles of Real Time Networks/Data Oriented Requirements Implementation Scheme (RTN/DORIS). It also proposes the implementation of these components using the well-known middleware technology Common Object Request Broker Architecture (CORBA). Results using this architecture for video surveillance are presented through an implemented prototype.

Defining user perception of distributed multimedia quality

This article presents the results of a study that explored the human side of the multimedia experience. We propose a model that assesses quality variation from three distinct levels: the network, the media and the content levels; and from two views: the technical and the user perspective. By facilitating parameter variation at each of the quality levels and from each of the perspectives, we were able to examine their impact on user quality perception. Results show that a significant reduction in frame rate does not proportionally reduce the user's understanding of the presentation independent of technical parameters, that multimedia content type significantly impacts user information assimilation, user level of enjoyment, and user perception of quality, and that the device display type impacts user information assimilation and user perception of quality. Finally, to ensure the transfer of information, low-level abstraction (network-level) parameters, such as delay and jitter, should be adapted; to maintain the user's level of enjoyment, high-level abstraction quality parameters (content-level), such as the appropriate use of display screens, should be adapted.

Epidemic K-Means clustering

The K-Means algorithm for cluster analysis is one of the most influential and popular data mining methods. Its straightforward parallel formulation is well suited for distributed memory systems with reliable interconnection networks. However, in large-scale geographically distributed systems the straightforward parallel algorithm can be rendered useless by a single communication failure or high latency in communication paths. This work proposes a fully decentralised algorithm (Epidemic K-Means) which does not require global communication and is intrinsically fault tolerant. The proposed distributed K-Means algorithm provides a clustering solution which can approximate the solution of an ideal centralised algorithm over the aggregated data as closely as desired. A comparative performance analysis is carried out against the state of the art distributed K-Means algorithms based on sampling methods. The experimental analysis confirms that the proposed algorithm is a practical and accurate distributed K-Means implementation for networked systems of very large and extreme scale.

The causal surface and its effect on distribution transparency in a distributed virtual environment

One goal in the development of distributed virtual environments (DVEs) is to create a system such that users are unaware of the distribution-the distribution should be transparent. The paper begins by discussing the general issues in DVEs that might make this possible, and a system that allows some level of distribution transparency is described. The system described suffers from effects of inconsistency, which in turn cause undesirable visual effects. The causal surface is introduced as a solution that removes these visual effects. The paper then introduces two determining factors of distribution transparency relating to user perception and performance. With regard to these factors, two hypotheses are stated relating to the causal surface. A user-trial on forty-five subjects is used to validate the hypotheses. A discussion of the results of the trial concludes that the causal surface solution does significantly improve the distribution transparency in a DVE.