Alpha M.o.s.

Sa&iuml;d Labreche

Hicham Amine

Fran&ccedil;ois Loubet

Tze Tsung Tan

A classification apparatus, notably for uses in recognition or characterisation of odors, comprises a plurality of sensors for generating raw data representing a plurality of instances of a plurality of different classes; and a processing unit for processing said raw data so as to determine an identification model. The identification model comprises definitions of the classes and a particular allocation rule selected dependent upon the application. The class definitions are established by analysing data obtained during a learning phase, the analysis being performed according to a particular information extraction method. During a later identification phase, the identification model enables an instance of unknown class to be allocated to an appropriate class amongst those defined during the learning phase. The information extraction method can be selected dependent upon the application. Preferably, a percentage of the data obtained during the learning phase is used to establish a plurality of candidate identification models, and the identification model which is selected for use is the one which gives the highest number of correct classifications when use to classify the other data obtained during the learning phase.

Classifying apparatus designed in particular for odor recognition

The invention concerns a classifying apparatus, used in particular for recognising or characterising odours, applying a combination of statistical methods and neuronal networks to classify into the appropriate class instances of a plurality of different classes presented to the apparatus. The apparatus comprises a processing unit for determining for each class (j) a subspace (SEj) wherein the instances of said class are best separated from instances of other classes, said subspace being defined by synthetic variables (VDj), and for determining a discriminating subspace (SED) defined by the whole set of synthetic variables identified for the whole set of classes. Each neuron in the neuronal input layer corresponds to one of the variables defining the discriminating space (SED) and each neuron of the output layer corresponds to one of the classes.

Growing community of inventors

Toulouse, France

Saïd Labreche

Growing community of inventors

Toulouse, France

Sa&iuml;d Labreche

Saïd Labreche