Neural device and method of constructing the device
First Claim
1. A neural device produced according to a method of constructing a neural device for classification of objects, said neural device being trained using a set of learning samples of objects having known classes, each object to be classified being defined by an input vector which is represented by a point in hyperspace, said neural device comprising:
- a layer of input neurons, each of which corresponds to one of the dimensions of the hyperspace;
a layer of hidden neurons having inputs, the inputs being connected exclusively to the input neurons, and activation of each hidden neuron being based on coordinates of a respective reference point of the hyperspace; and
a layer of output neurons, each of which corresponds to a respective class of the objects;
said method comprising;
a) starting from a neural device without a hidden neuron;
b) choosing an arbitrary sample from said set of learning samples;
c) subsequently placing a first hidden neuron in the device, while defining the respective reference point associated with said first neuron as the point in hyperspace representing the sample;
d) establishing an excitatory connection of positive weight between said first neuron and the output neuron corresponding to a class of the sample;
e) taking a new sample from said set of learning samples;
f) applying the new sample to the device for classification;
g) if, as a result of step f), a response of the device to the new sample does not give a correct classification, introducing into the device a new hidden neuron, corresponding to the new sample, by;
I) defining the respective reference point associated with the new hidden neuron as being the point representing the new sample and;
II) establishing an excitatory connection of positive weight between the new hidden neuron and the output neuron corresponding to the class of the new sample;
h) if the response does give the correct classification, skipping step g);
i) treating all remaining samples according to steps f), g) and h) until no samples remain in said set of learning samples; and
j) defining groups of neurons, with a neuron that is representative of each group, as the new hidden neurons are introduced, according to the following process;
I) determining, for each respective new hidden neurons of the new hidden neurons, whether the respective new hidden neuron forms part of a previously defined group;
II) in response to a positive result from the determining step, incorporating the respective hidden neuron into the group of which the respective new hidden neuron forms a part; and
III) in response to a negative result from the determining step, forming a new group for which the respective new hidden neurons constitutes a representative neuron, the first neuron thus being defined as being representative of a first group.
0 Assignments
0 Petitions
Accused Products
Abstract
On the basis of a device without hidden neurons, arbitrary samples are taken from a set of learning samples so as to be presented as objects to be classified. Each time if the response is not correct, a hidden neuron (Hi) is introduced with a connection to the output neuron (Oj) of the class of the sample, whereas if the response is correct, no neuron whatsoever is added. During this introduction phase for hidden neurons, the neurons are subdivided into groups by searching for each of the introduced neurons whether it falls within an existing group, in which case it is incorporated therein; otherwise a new group is created around this neuron. The association with a group is defined as a function of the distance from the "creator" neuron. This device could be applied in character recognition systems as one example.
-
Citations
13 Claims
-
1. A neural device produced according to a method of constructing a neural device for classification of objects, said neural device being trained using a set of learning samples of objects having known classes, each object to be classified being defined by an input vector which is represented by a point in hyperspace, said neural device comprising:
-
a layer of input neurons, each of which corresponds to one of the dimensions of the hyperspace; a layer of hidden neurons having inputs, the inputs being connected exclusively to the input neurons, and activation of each hidden neuron being based on coordinates of a respective reference point of the hyperspace; and a layer of output neurons, each of which corresponds to a respective class of the objects; said method comprising; a) starting from a neural device without a hidden neuron; b) choosing an arbitrary sample from said set of learning samples; c) subsequently placing a first hidden neuron in the device, while defining the respective reference point associated with said first neuron as the point in hyperspace representing the sample; d) establishing an excitatory connection of positive weight between said first neuron and the output neuron corresponding to a class of the sample; e) taking a new sample from said set of learning samples; f) applying the new sample to the device for classification; g) if, as a result of step f), a response of the device to the new sample does not give a correct classification, introducing into the device a new hidden neuron, corresponding to the new sample, by; I) defining the respective reference point associated with the new hidden neuron as being the point representing the new sample and; II) establishing an excitatory connection of positive weight between the new hidden neuron and the output neuron corresponding to the class of the new sample; h) if the response does give the correct classification, skipping step g); i) treating all remaining samples according to steps f), g) and h) until no samples remain in said set of learning samples; and j) defining groups of neurons, with a neuron that is representative of each group, as the new hidden neurons are introduced, according to the following process; I) determining, for each respective new hidden neurons of the new hidden neurons, whether the respective new hidden neuron forms part of a previously defined group; II) in response to a positive result from the determining step, incorporating the respective hidden neuron into the group of which the respective new hidden neuron forms a part; and III) in response to a negative result from the determining step, forming a new group for which the respective new hidden neurons constitutes a representative neuron, the first neuron thus being defined as being representative of a first group. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
-
Specification