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.

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.

A comparative study of multilayered and single layered neural network based predictive controllers

The authors compare the performance of two types of controllers one based on the multilayered network and the other based on the single layered CMAC network (cerebellar model articulator controller). The neurons (information processing units) in the multi-layered network use Gaussian activation functions. The control scheme which is considered is a predictive control algorithm, along the lines used by Willis et al. (1991), Kambhampati and Warwick (1991). The process selected as a test bed is a continuous stirred tank reactor. The reaction taking place is an irreversible exothermic reaction in a constant volume reactor cooled by a single coolant stream. This reactor is a simplified version of the first tank in the two tank system given by Henson and Seborg (1989).

Power transmission network simulation for fault diagnosis

The authors discuss an implementation of an object oriented (OO) fault simulator and its use within an adaptive fault diagnostic system. The simulator models the flow of faults around a power network, reporting switchgear indications and protection messages that would be expected in a real fault scenario. The simulator has been used to train an adaptive fault diagnostic system; results and implications are discussed.

Neural network based control in food processing

A number of commonly encountered simple neural network types are discussed, with particular attention being paid to their applicability in automation and control when applied to food processing. In the first instance n-tuple networks are considered, these being particularly useful for high speed production checking operations. Subsequently backpropagation networks are discussed, these being useful both in a more familiar feedback control arrangement and also for such things as recipe prediction.

Multiscale evolving complex network model of functional connectivity in neuronal cultures

Cultures of cortical neurons grown on multielectrode arrays exhibit spontaneous, robust and recurrent patterns of highly synchronous activity called bursts. These bursts play a crucial role in the development and topological selforganization of neuronal networks. Thus, understanding the evolution of synchrony within these bursts could give insight into network growth and the functional processes involved in learning and memory. Functional connectivity networks can be constructed by observing patterns of synchrony that evolve during bursts. To capture this evolution, a modelling approach is adopted using a framework of emergent evolving complex networks and, through taking advantage of the multiple time scales of the system, aims to show the importance of sequential and ordered synchronization in network function.

Automated organizational network analysis for enterprise 2.0

Social Networking Sites have recently become a mainstream communications technology for many people around the world. Major IT vendors are releasing social software designed for use in a business/commercial context. These Enterprise 2.0 technologies have impressive collaboration and information sharing functionality, but so far they do not have any organizational network analysis (ONA) features that reveal any patterns of connectivity within business units. This paper shows the impact of organizational network analysis techniques and social networks on organizational performance, we also give an overview on current enterprise social software, and most importantly, we highlight how Enterprise 2.0 can help automate an organizational network analysis.

The system identification and control of Hammerstein system using non-uniform rational B-spline neural network and particle swarm optimization

In this paper a new system identification algorithm is introduced for Hammerstein systems based on observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a non-uniform rational B-spline (NURB) neural network. The proposed system identification algorithm for this NURB network based Hammerstein system consists of two successive stages. First the shaping parameters in NURB network are estimated using a particle swarm optimization (PSO) procedure. Then the remaining parameters are estimated by the method of the singular value decomposition (SVD). Numerical examples including a model based controller are utilized to demonstrate the efficacy of the proposed approach. The controller consists of computing the inverse of the nonlinear static function approximated by NURB network, followed by a linear pole assignment controller.