In this paper, we introduce a general modeling technique, called vector-quantized temporal associative memory (VQTAM), which uses Kohonen's self-organizing map (SOM) as an alternative to multilayer perceptron (MLP) and radial basis function (RBF) neural models for dynamical system identification and control. We demonstrate that the estimation errors decrease as the SOM training proceeds, allowing the VQTAM scheme to be understood as a self-supervised gradient-based error reduction method. The performance of the proposed approach is evaluated on a variety of complex tasks, namely: i) time series prediction; ii) identification of SISO/MIMO systems; and iii) nonlinear predictive control. For all tasks, the simulation results produced by the SOM are as accurate as those produced by the MLP network, and better than those produced by the RBF network. The SOM has also shown to be less sensitive to weight initialization than MLP networks. We conclude the paper by discussing the main properties of the VQTAM and their relationships to other well established methods for dynamical system identification. We also suggest directions for further work.

This paper reports results of an investigation on the degree of influence of short-term memory mechanisms on the performance of neural classifiers when applied to robot navigation tasks. In particular, we deal with the well-known strategy of navigating by ¿wall-following¿. For this purpose, four standard neural architectures (Logistic Perceptron, Multilayer Percep-tron, Mixture of Experts and Elman network) are used to associate different spatiotemporal sensory input patterns with four predetermined action categories. All stages of the experiments-data acquisition, selection and training of the architectures in a simulator and their execution on a real mobile robot-are described. The obtained results suggest that the wall-following task, formulated as a pattern classification problem, is nonlinearly separable, a result that favors the MLP network if no memory of input patters are taken into account. If short-term memory mechanisms are used, then even a linear network is able to perform the same task successfully.

This paper reports results from a comprehensive performance comparison among standalone machine learning algorithms (SVM, MLP and GRNN) and their combinations in ensembles of classifiers when applied to a medical diagnosis problem in the field of orthopedics. All the aforementioned learning strategies, which currently comprises the classification module of the SINPATCO platform, are evaluated according to their ability in discriminating patients as belonging to one out of three categories: normal, disk hernia and spondylolisthesis. Confusion matrices of all learning algorithms are also reported, as well as a study of the effect of diversity in the design of the ensembles. The obtained results clearly indicate that the ensembles of classifiers have better generalization performance than standalone classifiers.