METHOD FOR CONSTITUTING NEURAL NETWORK AND LEARNING/ RECOLLECTING SYSTEM

• Main Authors: ABE NOBORU, INOUE HARUKI, KOIDE MINORU, YAHIRO MASAKAZU
• Format: Patent
• Language: English
• Published: 02.04.1993
• Subjects: ANALOGUE COMPUTERS; CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; CONTROL OR REGULATING SYSTEMS IN GENERAL; CONTROLLING; COUNTING; ELECTRIC DIGITAL DATA PROCESSING; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS ORELEMENTS; PHYSICS; REGULATING

PURPOSE:To optimize a sampling distance and a sampling time in the forecasting of time sequential data by evaluating the forecasting based upon a correlation coefficient between a measured pattern and a forecasted pattern. CONSTITUTION:The number of input neurons corresponding to the sampling distance (d) and the sampling tithe (T) is previously and temporarily determined. Time sequential data formed by a teacher pattern forming means 1 are stored in teacher pattern memories 6, 7 and learning is executed by a learning means 4 based upon the stored contents of the memories 6, 7. After converging the learning, a forecasting accuracy evaluating means 3 calculates a correlation coefficient between a forecasted value based upon a neural network and a value corresponding to a teacher pattern. When the correlation coefficient satisfies required forecasting accuracy, a time sequential neural network having the distance (d) and the time (T) is determined by a time sequential neural network defining means 2. When the accuracy is not satisfied, at least one of the values (d), (T) is sequentially changed and the learning and the forecasting accuracy evaluation are repeated.