Experimental design and model construction algorithms for radial basis function networks

New construction algorithms for radial basis function (RBF) network modelling are introduced based on the A-optimality and D-optimality experimental design criteria respectively. We utilize new cost functions, based on experimental design criteria, for model selection that simultaneously optimizes model approximation, parameter variance (A-optimality) or model robustness (D-optimality). The proposed approaches are based on the forward orthogonal least-squares (OLS) algorithm, such that the new A-optimality- and D-optimality-based cost functions are constructed on the basis of an orthogonalization process that gains computational advantages and hence maintains the inherent computational efficiency associated with the conventional forward OLS approach. The proposed approach enhances the very popular forward OLS-algorithm-based RBF model construction method since the resultant RBF models are constructed in a manner that the system dynamics approximation capability, model adequacy and robustness are optimized simultaneously. The numerical examples provided show significant improvement based on the D-optimality design criterion, demonstrating that there is significant room for improvement in modelling via the popular RBF neural network.

Cultured neural networks

This special section contains papers addressing various aspects associated with the issue Of Cultured neural networks. These are networks, that are formed through the monitored growth of biological neural tissue. In keeping with the aims of the International Journal of Adaptive Control and Signal Processing, the key focus of these papers is to took at particular aspects of signal processing in terms of both stimulating such a network and in assigning intent to signals collected as network outputs. Copyright (C) 2009 John Wiley & Sons, Ltd.

Generalized matching networks and their properties

In this paper, we introduce two kinds of graphs: the generalized matching networks (GMNs) and the recursive generalized matching networks (RGMNs). The former generalize the hypercube-like networks (HLNs), while the latter include the generalized cubes and the star graphs. We prove that a GMN on a family of k-connected building graphs is -connected. We then prove that a GMN on a family of Hamiltonian-connected building graphs having at least three vertices each is Hamiltonian-connected. Our conclusions generalize some previously known results.

Global exponential periodicity of a class of bidirectional associative memory networks with finite distributed delays

In this paper, we study the periodic oscillatory behavior of a class of bidirectional associative memory (BAM) networks with finite distributed delays. A set of criteria are proposed for determining global exponential periodicity of the proposed BAM networks, which assume neither differentiability nor monotonicity of the activation function of each neuron. In addition, our criteria are easily checkable. (c) 2005 Elsevier Inc. All rights reserved.

Global exponential periodicity of a class of neural networks with recent-history distributed delays

In this paper, we propose to study a class of neural networks with recent-history distributed delays. A sufficient condition is derived for the global exponential periodicity of the proposed neural networks, which has the advantage that it assumes neither the differentiability nor monotonicity of the activation function of each neuron nor the symmetry of the feedback matrix or delayed feedback matrix. Our criterion is shown to be valid by applying it to an illustrative system. (c) 2005 Elsevier Ltd. All rights reserved.

An oblivious shortest-path routing algorithm for fully connected cubic networks

Fully connected cubic networks (FCCNs) are a class of newly proposed hierarchical interconnection networks for multicomputer systems, which enjoy the strengths of constant node degree and good expandability. The shortest path routing in FCCNs is an open problem. In this paper, we present an oblivious routing algorithm for n-level FCCN with N = 8(n) nodes, and prove that this algorithm creates a shortest path from the source to the destination. At the costs of both an O(N)-parallel-step off-line preprocessing phase and a list of size N stored at each node, the proposed algorithm is carried out at each related node in O(n) time. In some cases the proposed algorithm is superior to the one proposed by Chang and Wang in terms of the length of the routing path. This justifies the utility of our routing strategy. (C) 2006 Elsevier Inc. All rights reserved.

Mining gene networks with application to GAW15 problem 1

The genetic analysis workshop 15 (GAW15) problem 1 contained baseline expression levels of 8793 genes in immortalised B cells from 194 individuals in 14 Centre d’Etude du Polymorphisme Humane (CEPH) Utah pedigrees. Previous analysis of the data showed linkage and association and evidence of substantial individual variations. In particular, correlation was examined on expression levels of 31 genes and 25 target genes corresponding to two master regulatory regions. In this analysis, we apply Bayesian network analysis to gain further insight into these findings. We identify strong dependences and therefore provide additional insight into the underlying relationships between the genes involved. More generally, the approach is expected to be applicable for integrated analysis of genes on biological pathways.

Models for evolving networks: with applications in telecommunication and online activities

Driven by a range of modern applications that includes telecommunications, e-business and on-line social interaction, recent ideas in complex networks can be extended to the case of time-varying connectivity. Here we propose a general frame- work for modelling and simulating such dynamic networks, and we explain how the long time behaviour may reveal important information about the mechanisms underlying the evolution.

Implementing innovation: designers, users and actor-networks

The role of users is an often-overlooked aspect of studies of innovation and diffusion. Using an actor-network theory (ANT) approach, four case studies examine the processes of implementing a piece of CAD (computer aided design) software, BSLink, in different organisations and describe the tailoring done by users to embed the software into working practices. This not only results in different practices of use at different locations, but also transforms BSLink itself into a proliferation of BSLinks-in-use. A focus group for BSLink users further reveals the gaps between different users' expectations and ways of using the software, and between different BSLinks-in-use. It also demonstrates the contradictory demands this places on its further development. The ANT-informed approach used treats both innovation and diffusion as processes of translation within networks. It also emphasises the political nature of innovation and implementation, and the efforts of various actors to delegate manoeuvres for increased influence onto technological artefacts.

Prediction of Parkinson’s disease tremor onset using radial basis function neural networks

The possibility of using a radial basis function neural network (RBFNN) to accurately recognise and predict the onset of Parkinson’s disease tremors in human subjects is discussed in this paper. The data for training the RBFNN are obtained by means of deep brain electrodes implanted in a Parkinson disease patient’s brain. The effectiveness of a RBFNN is initially demonstrated by a real case study.

Full interference cancellation for two-path relay cooperative networks

This paper proposes the full interference cancellation (FIC) algorithm to cancel the inter-relay interference (IRI) in the two-path cooperative system. Arising from simultaneous data transmission from the source and relay nodes, IRI may significantly decrease the performance if it is not carefully handled. Compared to the existing partial interference cancellation (PIC) scheme, the FIC approach is more robust yet with less complexity. Numerical results are also given to verify the proposed scheme.

Dynamics of brand competition: Effects of unobserved social networks

Brand competition is modelled using an agent based approach in order to examine the long run dynamics of market structure and brand characteristics. A repeated game is designed where myopic firms choose strategies based on beliefs about their rivals and consumers. Consumers are heterogeneous and can observe neighbour behaviour through social networks. Although firms do not observe them, the social networks have a significant impact on the emerging market structure. Presence of networks tends to polarize market share and leads to higher volatility in brands. Yet convergence in brand characteristics usually happens whenever the market reaches a steady state. Scale-free networks accentuate the polarization and volatility more than small world or random networks. Unilateral innovations are less frequent under social networks.

Cultured neural networks

This article looks at the use of cultured neural networks as the decision-making mechanism of a control system. In this case biological neurons are grown and trained to act as an artificial intelligence engine. Such research has immediate medical implications as well as enormous potential in computing and robotics. An experimental system involving closed-loop control of a mobile robot by a culture of neurons has been successfully created and is described here. This article gives a brief overview of the problem area and ongoing research. Questions are asked as to where this will lead in the future.