Development of low entropy coding in a recurrent network.

In this paper we present an unsupervised neural network which exhibits competition between units via inhibitory feedback. The operation is such as to minimize reconstruction error, both for individual patterns, and over the entire training set. A key difference from networks which perform principal components analysis, or one of its variants, is the ability to converge to non-orthogonal weight values. We discuss the network's operation in relation to the twin goals of maximizing information transfer and minimizing code entropy, and show how the assignment of prior probabilities to network outputs can help to reduce entropy. We present results from two binary coding problems, and from experiments with image coding.

Network integration for international mergers and Acquisitions

Business activities are increasingly taking place across geographical and ownership boundaries. Post-Merger & Acquisition Integration (PMI) processes are more challenging in network organisations due to the extra complexity and interdependency associated with networks. However, network integration issues are not well addressed in the traditional M&A literature or the network organisation literature. Based on ten in-depth case studies across key industry sectors, this research identifies the essential network integration issues for international M&As with a configuration concept, and demonstrates different network integration patterns according to M&A objectives for growth and efficiency. This paper extends the theoretical understanding of PMI for network organisations. It can also provide practical guidance for managers to assess the feasibility of an M&A transition or to go through the PMI process successfully. Copyright © 2010 Inderscience Enterprises Ltd.

Packet error rate and bit error rate non-deterministic relationship in optical network applications

The non-deterministic relationship between Bit Error Rate and Packet Error Rate is demonstrated for an optical media access layer in common use. We show that frequency components of coded, non-random data can cause this relationship. © 2005 Optical Society of America.

The design of an agent for modelling supply chain network dynamics

A useful insight into managerial decision making can be found from simulation of business systems, but existing work on simulation of supply chain behaviour has largely considered non-competitive chains. Where competitive agents have been examined, they have generally had a simple structure and been used for fundamental examination of stability and equilibria rather than providing practical guidance to managers. In this paper, a new agent for the study of competitive supply chain network dynamics is proposed. The novel features of the agent include the ability to select between competing vendors, distribute orders preferentially among many customers, manage production and inventory, and determine price based on competitive behaviour. The structure of the agent is related to existing business models and sufficient details are provided to allow implementation. The agent is tested to demonstrate that it recreates the main results of the existing modelling and management literature on supply chain dynamics. A brief exploration of competitive dynamics is given to confirm that the proposed agent can respond to competition. The results demonstrate that overall profitability for a supply chain network is maximised when businesses operate collectively. It is possible for an individual business to achieve higher profits by adopting a more competitive stance, but the consequence of this is that the overall profitability of the network is reduced. The agent will be of use for a broad range of studies on the long-run effect of management decisions on their network of suppliers and customers.