A practical approach for training dynamic recurrent neural networks: use of Apriori information

Presents a technique for incorporating a priori knowledge from a state space system into a neural network training algorithm. The training algorithm considered is that of chemotaxis and the networks being trained are recurrent neural networks. Incorporation of the a priori knowledge ensures that the resultant network has behaviour similar to the system which it is modelling.

Dealing with complexity: an overview of the neural network approach

The problem of complexity is particularly relevant to the field of control engineering, since many engineering problems are inherently complex. The inherent complexity is such that straightforward computational problem solutions often produce very poor results. Although parallel processing can alleviate the problem to some extent, it is artificial neural networks (in various forms) which have recently proved particularly effective, even in dealing with the causes of the problem itself. This paper presents an overview of the current neural network research being undertaken. Such research aims to solve the complex problems found in many areas of science and engineering today.

Internal model control of nonlinear systems through the inversion of recurrent neural networks

Recurrent neural networks can be used for both the identification and control of nonlinear systems. This paper takes a previously derived set of theoretical results about recurrent neural networks and applies them to the task of providing internal model control for a nonlinear plant. Using the theoretical results, we show how an inverse controller can be produced from a neural network model of the plant, without the need to train an additional network to perform the inverse control.

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.

A combined engineering and statistical model of UK domestic appliance electrical load profiles

The development of a combined engineering and statistical Artificial Neural Network model of UK domestic appliance load profiles is presented. The model uses diary-style appliance use data and a survey questionnaire collected from 51 suburban households and 46 rural households during the summer of 2010 and2011 respectively. It also incorporates measured energy data and is sensitive to socioeconomic, physical dwelling and temperature variables. A prototype model is constructed in MATLAB using a two layer feed forward network with back propagation training which has a 12:10:24 architecture. Model outputs include appliance load profiles which can be applied to the fields of energy planning (microrenewables and smart grids), building simulation tools and energy policy.

Real-time application of a constrained predictive controller based on dynamic neural networks with feedback linearization

This paper describes an experimental application of constrained predictive control and feedback linearisation based on dynamic neural networks. It also verifies experimentally a method for handling input constraints, which are transformed by the feedback linearisation mappings. A performance comparison with a PID controller is also provided. The experimental system consists of a laboratory based single link manipulator arm, which is controlled in real time using MATLAB/SIMULINK together with data acquisition equipment.

Parkinson’s Disease tremor classification – a comparison between Support Vector Machines and neural networks

Deep Brain Stimulation has been used in the study of and for treating Parkinson’s Disease (PD) tremor symptoms since the 1980s. In the research reported here we have carried out a comparative analysis to classify tremor onset based on intraoperative microelectrode recordings of a PD patient’s brain Local Field Potential (LFP) signals. In particular, we compared the performance of a Support Vector Machine (SVM) with two well known artificial neural network classifiers, namely a Multiple Layer Perceptron (MLP) and a Radial Basis Function Network (RBN). The results show that in this study, using specifically PD data, the SVM provided an overall better classification rate achieving an accuracy of 81% recognition.

Fractional order modeling of large three-dimensional RC networks

An incidence matrix analysis is used to model a three-dimensional network consisting of resistive and capacitive elements distributed across several interconnected layers. A systematic methodology for deriving a descriptor representation of the network with random allocation of the resistors and capacitors is proposed. Using a transformation of the descriptor representation into standard state-space form, amplitude and phase admittance responses of three-dimensional random RC networks are obtained. Such networks display an emergent behavior with a characteristic Jonscher-like response over a wide range of frequencies. A model approximation study of these networks is performed to infer the admittance response using integral and fractional order models. It was found that a fractional order model with only seven parameters can accurately describe the responses of networks composed of more than 70 nodes and 200 branches with 100 resistors and 100 capacitors. The proposed analysis can be used to model charge migration in amorphous materials, which may be associated to specific macroscopic or microscopic scale fractal geometrical structures in composites displaying a viscoelastic electromechanical response, as well as to model the collective responses of processes governed by random events described using statistical mechanics.

Statistical analysis of eight surface ozone measurement series for various sites in Ireland

Data from various stations having different measurement record periods between 1988 and 2007 are analyzed to investigate the surface ozone concentration, long-term trends, and seasonal changes in and around Ireland. Time series statistical analysis is performed on the monthly mean data using seasonal and trend decomposition procedures and the Box-Jenkins approach (autoregressive integrated moving average). In general, ozone concentrations in the Irish region are found to have a negative trend at all sites except at the coastal sites of Mace Head and Valentia. Data from the most polluted Dublin city site have shown a very strong negative trend of −0.33 ppb/yr with a 95% confidence limit of 0.17 ppb/yr (i.e., −0.33 ± 0.17) for the period 2002−2007, and for the site near the city of Cork, the trend is found to be −0.20 ± 0.11 ppb/yr over the same period. The negative trend for other sites is more pronounced when the data span is considered from around the year 2000 to 2007. Rural sites of Wexford and Monaghan have also shown a very strong negative trend of −0.99 ± 0.13 and −0.58 ± 0.12, respectively, for the period 2000−2007. Mace Head, a site that is representative of ozone changes in the air advected from the Atlantic to Europe in the marine planetary boundary layer, has shown a positive trend of about +0.16 ± 0.04 ppb per annum over the entire period 1988−2007, but this positive trend has reduced during recent years (e.g., in the period 2001−2007). Cluster analysis for back trajectories are performed for the stations having a long record of data, Mace Head and Lough Navar. For Mace Head, the northern and western clean air sectors have shown a similar positive trend (+0.17 ± 0.02 ppb/yr for the northern sector and +0.18 ± 0.02 ppb/yr for the western sector) for the whole period, but partial analysis for the clean western sector at Mace Head shows different trends during different time periods with a decrease in the positive trend since 1988 indicating a deceleration in the ozone trend for Atlantic air masses entering Europe.

Population based models of cortical drug response: insights from anaesthesia

A great explanatory gap lies between the molecular pharmacology of psychoactive agents and the neurophysiological changes they induce, as recorded by neuroimaging modalities. Causally relating the cellular actions of psychoactive compounds to their influence on population activity is experimentally challenging. Recent developments in the dynamical modelling of neural tissue have attempted to span this explanatory gap between microscopic targets and their macroscopic neurophysiological effects via a range of biologically plausible dynamical models of cortical tissue. Such theoretical models allow exploration of neural dynamics, in particular their modification by drug action. The ability to theoretically bridge scales is due to a biologically plausible averaging of cortical tissue properties. In the resulting macroscopic neural field, individual neurons need not be explicitly represented (as in neural networks). The following paper aims to provide a non-technical introduction to the mean field population modelling of drug action and its recent successes in modelling anaesthesia.