# US 2018/0024510 A1 · Machine learning model construction device, numerical control, machine learning model construction method, and non-transitory computer readable medium encoded with a machine learning model construction program > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2018/0024510 A1 - Document ID: US20180024510A1 - Status: Published application - Kind: A1 - Application number: 15/648,753 - Filed: 2017-07-13 - Published: 2018-01-25 - Earliest claimed: 2016-07-22 - Assignees: Fanuc Corporation - Applicants: Fanuc Corporation - Inventors: Takahiko MATSUSHIMA; Yoshito MIYAZAKI - Web page: https://patentgrep.com/patent/US20180024510A1 ## CPC - G05B 13/027: {using neural networks only} - G06N 3/063: using electronic means - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Various neural network models are constructed flexibly on a. learning circuit. A machine learning model construction device includes a learning circuit (80) capable of constructed a neural network model according to a setting value, and a control means (11) capable of adjusting the setting value so as to become a value for constructing a predetermined neural network model in the learning circuit (80). ## Claims ### Claim 1 A machine learning model construction device comprising: a learning circuit capable of constructing a neural network model according to a setting value; and a control means for adjusting the setting value so as to be a value for constructing a predetermined neural network model in the learning circuit. ### Claim 2 (depends on 1) A machine learning model construction device according to claim 1, wherein the control means receives input of a parameter value, defines a neural network model corresponding to the received parameter value as the predetermined neural network model, and adjusts the setting value so as to be a value for constructing the predetermined neural network model in the learning circuit. ### Claim 3 (depends on 2) A machine learning model construction device according to claim 2, wherein the received parameter value is a value designating a layer number of the neural network model and a dimension number of each layer. ### Claim 4 (depends on 2) A machine learning model construction device according to claim 2, wherein the control means reads the parameter value from a setting file. ### Claim 5 (depends on 1) A machine learning model construction device according to claim 1, wherein the control means draws a connection relationship of perceptrons according to the parameter value, and a graphical user interface for performing adjustment of the parameter value while a user references the connection relationship of the perceptrons, on a display means. ### Claim 6 (depends on 1) A machine learning model construction device according to claim 1, wherein the control means allows a user to select a neural network model prepared in advance, defines the neural network model selected as the predetermined neural network model, and adjusts the setting value so as to be a value for constructing the predetermined neural network model in the learning circuit. ### Claim 7 (depends on 1) A machine learning model construction device according to claim 1, wherein the control means constructs a neural network model in the learning circuit by way of controlling output of each perceptron included in the learning circuit according to the setting value. ### Claim 8 (depends on 7) A machine learning model construction device according to claim 7, wherein controlling output of each perceptron included in the learning circuit according to the setting value includes setting output of a multiplier within each perceptron included in the learning circuit according to the setting value, as any value among a value arrived at by multiplying a weighting value by input of the multiplier, a value as inputted to the multiplier, and a value of zero, and sets a sum of outputs of multipliers within each perceptron as an output of each perceptron. ### Claim 9 (depends on 1) A numerical control for controlling a machine tool, comprising the machine learning model construction device according to claim 1, wherein the control means receives input of a parameter value for constructing a neural network model that is related to control of the machine tool in the learning circuit, sets a neural network model corresponding to the received parameter value as the predetermined neural network model, and adjusts the setting value so as to be a value for constructing the predetermined neural network model in the learning circuit. ### Claim 10 A non-transitory computer readable medium encoded with a machine learning model construction program for causing a computer to function as a machine learning construction device, comprising: a learning circuit capable of constructing a neural network model according to a setting value; and a control means for adjusting the setting value so as to be a value for constructing a predetermined neural network model in the learning circuit. ### Claim 11 A method of constructing a machine learning model performed by a machine learning model construction device including a learning circuit capable of constructing a neural network model according to a setting value, the method comprising: a step of controlling to adjust the setting value so as to be a value for constructing a predetermined neural network model in the learning circuit.