LEARNING IN THE COMBINATORIAL NEURAL MODEL

The combinatorial neural model (CNM) is a type of fuzzy neural network for classification problems and, more generally, for the mapping betw een fuzzy multidimensional spaces. Learning in CNM is a complex task s panning the learning of input-neuron membership functions, of the netw ork topology, and of connection weights. In this paper we are concerne d with these various aspects of learning in CNM, most notably with the learning of connection weights, whose complexity comes from the exist ence of nondifferentiable, nonconvex error functions associated with t he learning process. We introduce several algorithms for weight learni ng, most based on subgradient techniques borrowed from the held of non differentiable optimization. All algorithms are based on essentially ' 'local'' rules, and are therefore amenable to distributed/parallel imp lementations. Experimental results are provided on the large-scale pro blem of monitoring the deforestation of the Amazon region on satellite images. What these results indicate is that a hybrid CNM system outpe rforms previous results obtained with variations of error backpropagat ion techniques. In addition, this hybrid system has demonstrated robus tness in the contest under consideration, therefore constituting an at tractive alternative.