A Robust Algorithm in Sequentially Selecting Subset Time-Series Systems Using Neural Networks

Abstract

In this paper a numerically robust lattice-ladder learning alogorithm is presented that sequentially selects the best specification of a subset time series system using neural networks. We have been able to extend the relevance of multi-layered neural networks and so more effectively model a greater array of time series situations. We have recognised that many connections between nodes in layers are unnecessary and can be deleted. So we have introduced inhibitor arcs - reflecting inhibitive synapses. We also allow for connections between nodes in layers which have variable strengths at different points of time by introducing additionally excitatory arcs - reflecting excitatory synapses. The resolving of both time and order updating leads to the optimal synaptic weight updating and allows for the optimal dynamic node creation/deletion within the extended neural network. The paper presents two applications that demonstrate the usefulness of the process.