Neuronal network for modeling a physical system, and a method for forming such a neuronal network

• US 20030163436A1
• Filed: 01/13/2003
• Published: 08/28/2003
• Est. Priority Date: 01/11/2002
• Status: Abandoned Application

• Current Assignee: Jost Seifert
• Original Assignee: Jost Seifert
• Inventors: Seifert, Jost
• Application Number:
  US10/340,847
• Publication Number:
  US 20030163436A1
• Time in Patent Office: Days
• US Class Current: 706/26
• CPC Class Codes: G06N 3/045 Combinations of networks

Abstract

A neuronal network for modeling an output function that describes a physical system using functionally linked neurons (2), each of which is assigned a transfer function, allowing it to transfer an output value determined from said neuron to the next neuron that is functionally connected to it in series in the longitudinal direction (6) of the network (1), as an input value. The functional relations necessary for linking the neurons are provided within only one of at least two groups (21, 22, 23) of neurons arranged in a transverse direction (7) and between one input layer (3) and one output layer (5). The groups (21, 22, 23) include at least two intermediate layers (11, 12, 13) arranged sequentially in a longitudinal direction (5), each with at least one neuron.

Claims

1. A neuronal network for use in modeling a physical system mathematically defined by a functional equation having summation terms having subfunctions and subfunction coefficients, said network comprising:

an input layer;

an output layer; and

a group layer, said group layer including at least two groups of neurons, wherein the number of groups of neurons is equal to the number of subfunctions in the functional equation being used to describe the system being modeled, and wherein the subfunction coefficients are arranged in the form of untrainable input links, after an output neuron in a respective group.

2. The neuronal network in accordance with claim 1, wherein the input and output layers include respective input and output neurons which are linear, in order to allow a transfer of input values, unchanged, to the neurons in a first layer of a group, and in order to avoid limiting the value range for the output of the neuronal network.

3. The neuronal network in accordance with claim 1, further including fixed value untrainable links between the neurons in the input layer and the first layer in a group, wherein said fixed value is determined during the training of the neuronal network.

4. The neuronal network in accordance with claim 1, further including an untrainable input link for the multiplication of the output of a group with a predetermined factor.

5. The neuronal network in accordance with claim 1, wherein the neuronal network is used to set up a simulation model.

6. The neuronal network in accordance with claim 1, wherein the neuronal network is analyzed by viewing one group as an isolated, neuronal network, wherein a first intermediate layer becomes the input layer and a last intermediate layer becomes the output layer.

7. The neuronal network in accordance with claim 1, wherein one group is trained in isolation, in which only link weights of a group are modified, using a training data set and an optimization process.

8. The neuronal network in accordance with claim 1, wherein a value range of a group is defined via a suitable selection of a transfer function for the output neuron of a group.

12. A method for setting up a neuronal network in accordance with claim 1, comprising:

adopting the values for the input into the network from a training data set;

registering the input neurons and the input links with said adopted values;

calculating the neuronal network from the input layer up to the output layer, wherein an activation of each neuron is calculated dependent upon preceding neurons and links;

comparing said neurons with a reference value from the training data set, and calculating the network error from the difference in order to activate the output neurons, wherein the error for each neuron is calculated from the network error, in layers from the back to the front;

calculating the weight change in links to adjacent neurons, dependent upon the error of one neuron and its activation, wherein one of untrainable input links and the untrainable links are excluded; and

adding the calculated weight changes to proper link weights, wherein the untrainable input links and the untrainable links are excluded.

9. A neuronal network for use in modeling an output function that describes a physical system, said network comprising functionally connected neurons, each of which is assigned a transfer function, allowing transfer of a determined output value as an input value to a next neuron functionally connected in series, in the longitudinal direction of the network, wherein functional relations for linking the neurons are provided within only one of at least two groups of neurons, arranged in a transverse direction between an input layer and an output layer, and wherein each of the at least two the groups of neurons comprise at least two intermediate layers, arranged sequentially in a longitudinal direction, each of said at least two intermediate layers having at least one neuron, wherein the subfunction coefficients are considered in the form of untrainable links between a neuron group and the output layer neurons in the entire neuronal network, and are provided as links between the input layer and each one of a group of untrainable input links.

10. The neuronal network for modeling an output function that describes a physical system in accordance with claim 9, wherein a number of neuronal groups is equal to a number of subfunctions in a functional equation that describes the system being simulated.

11. The neuronal network for modeling an output function that describes a physical system according to claim 9, wherein the input layer of neurons and the output layer of neurons of the neuronal network are linear.

13. The method for training a neuronal network in accordance with claim 9 for implementation in a computer program system, wherein the input and output values of the system are measured and a training data set for the neuronal network is established from the measured data, with these data being formed from a number of value pairs, each comprising four input values (α

, Mα

, η

, q) and one output value (C_M).

14. The method of training, in accordance with claim 13, wherein the method of descent by degree is used.

15. The optimization process for adjusting the link weights of a neuronal network in accordance with claim 9 for implementation in a computer program system, wherein trainable link weights w_y are adjusted such that the neuronal network supplies an optimal output for all measured data, wherein the values for the inputs to the network are taken from the training data set, comprising:

assigning random values to the link weights;

setting the input links to the input values from the training data set;

calculating the network from the input layer up to the output layer, wherein the activation of each neuron is calculated independent of the preceding neurons and links;

comparing the activation of the output neurons with the reference value from the training data set, and calculating the network error from the difference;

calculating for each layer of the error at each neuron from the network error, against the longitudinal orientation, wherein the links function as inputs;

calculating the weight change in the links to adjacent neurons, dependent upon the error of one neuron and its activation;

adding the weight changes to the proper link weights, wherein the weight changes are not added to the untrainable links and the untrainable input links.

16. The optimization process for adjusting the link weights of a neuronal network in accordance with claim 15, wherein the link weights are set to random values within the range of [−

1.0 to +1.0].

17. The optimization process for adjusting the link weights of a neuronal network in accordance with claim 15, wherein the optimization process is conducted for only one group in the neuronal network.