Morphological transformations are efficient methods for shape analysis and representation. In this paper two morphological shape descriptors are described for object feature representation. Neural networks are then employed for object recognition and classification. Various coding schemes and...

The use of n-tuple or weightless neural networks as pattern recognition devices is well known (Aleksander and Stonham, 1979). They have some significant advantages over the more common and biologically plausible networks, such as multi-layer perceptrons; for example, n-tuple networks have been...

In this paper we present a modification of the strongly diluted Hopfield model in which the dilution scheme, instead of being random, is inspired on biological grounds. We also show that the resulting sparsely connected neural network shows striking features in terms of performance as an...

The paper presents some aspects regarding the use of pattern recognition techniques and neural networks for the activity evolution diagnostication and prediction by means of a set of indicators. Starting from the indicators set there is defined a measure on the patterns set, measure representing...

An ANN (artificial neural network) approach was used to model the fuel consumption of wheat production. This study was conducted over 35,300 ha of irrigated and dry land wheat fields in Canterbury in the 2007–2008 harvest year. From an extensive data collection involving 40 farms, the total...

We introduce a method for dynamic classification of vector time series data into different regimes. A hidden Markov regime-switching model is used in classification. Past regimes are determined in advance and characterized by first and second moments of the observation vector. In estimation and...

Financial markets, such as the global foreign exchange (FX) market, often exhibit trending behaviour. Within such trends, the market level oscillates with changes in market consensus. Continued oscillations of this type result in the formation of wave patterns within the underlying trend known...

Using historical time-series data, we test for convergence and common trends in real per capita output for New Zealand and her four major trading partners. Both bivariate and multivariate time-series methods are used, and we also implement the fuzzy c-means clustering algorithm as a new method...

We study connectivity of stones on a Go Board, including influence and heuristics-based soft connectivity and pattern- and search-based hard connectivity. Stone connectivity provides a Go program with an understanding of a fundamental aspect of board configurations. It is essential in positional...