Abstract: A system for monitoring the discharging/charging cycles of a rechargeable battery which includes adaptive calculation means for providing a predictive indication of when the battery will reach a critical discharge voltage. The adaptive calculation means includes parameters which can be modified by other adaptive calculation means so as to optimize the monitor's performance depending on the battery's actual use. The adaptive calculation means may be neural networks formed by a microprocessor and memory, and the monitor system may be coupled to a host system.

Abstract: A system (22) for synthesis of textured images, comprising a texture analyser (24) and a texture synthesizer (28). The texture analyser (24) comprises an analyzing neural network (30) which learns to characterize a texture by calculating synaptic coefficients (C.sub.ab), utilizing at least one proximity function which characterizes a neighbourhood around pixels of said texture. The texture synthesizer (28) comprises a synthesizing neural network (40) which receives the synaptic coefficients (C.sub.ab) thus calculated and which, utilizing a relaxation mechanism, synthesizes a replica of the texture learned. The neural networks (30), (40) may have a tree-type structure.

Abstract: Classification method implemented in a layered neural network, comprising learning steps during which at least one layer is constructed by the addition of the successive neurons necessary for operating, by successive dichotomies, a classification of examples distributed over classes. In order to create at least one layer starting with a group of examples distributed over more than two classes, each successive neuron tends to distinguish its input data according to two predetermined sub-groups of classes peculiar to the said neuron according to a principal components analysis of the distribution of the said input data subjected to the learning of the neuron of the layer in question.

Abstract: Classification procedure implemented in a tree-like neural network which, in the course of learning steps, determines with the aid of a tree-like structure the number of neurons and their synaptic coefficients required for the processing of problems of classification of multi-class examples. Each neuron tends to distinguish, from the examples, two groups of examples approximating as well as possible to a division into two predetermined groups of classes. This division can be obtained through a principal component analysis of the distribution of examples. The neural network comprises a directory of addresses of successor neurons which is loaded in learning mode then read in exploitation mode. A memory stores example classes associated with the ends of the branches of the tree.