IMPLEMENTASI RAM BASED NEURAL NETWORKS PADA SISTEM NAVIGASI ROBOT MOBIL DENGAN EFISIENSI DATA MEMORI
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Institusion
Universitas Sriwijaya
Author
ONGKY RAMADONA (STUDENT ID : 09121301004)
Siti Nurmaini (LECTURER ID : 0003126664)
Ahmad Zarkasi (LECTURER ID : 0225087902)
Siti Nurmaini (LECTURER ID : 0003126664)
Ahmad Zarkasi (LECTURER ID : 0225087902)
Subject
T58.4 Managerial control systems Information technology. Information systems (General)
Datestamp
2019-11-14 07:13:31
Abstract :
Robot car navigation systems is desirable to control the movement of the robot system of the car. Car Navigation System robot has been developed for various applications as well as to help in problem solving. Several studies have been conducted on a navigation system that uses RAM-based neural network method. Such as research that addresses the issue arisitektur, memory usage in the learning capacity of neural networks and improve the resilience of the methods necessary to increase the generalizability and speed training and simple hardware implementation, the absence of environmental efficiency pattern classification. This research utilizing neural network-based method that is capable mengefisiensi RAM memory used for the navigation system. This study begins by designing a computer program using the C # language. Program design results are used to test the navigation system using a number of Pattern Classification 5 environment using infrared sensors and showed that the sensors S1, S2 and S4 success rate of 96.47% for the furthest distance and the success rate for the closest distance at 99, 22%. Sensor S3 and S5 to the success rate of 97.65% for the furthest distance and the success rate for the shortest distance is equal to 99.31% as well as the movement of the robot when the robot car better read each environment pattern as had been trained on artificial neural network-based RAM is consistent with the efficient use of memory data by 5 bits where each data adalah10000b, 01110b, 01101b, 01010b.
Robot car navigation systems is desirable to control the movement of the robot system of the car. Car Navigation System robot has been developed for various applications as well as to help in problem solving. Several studies have been conducted on a navigation system that uses RAM-based neural network method. Such as research that addresses the issue arisitektur, memory usage in the learning capacity of neural networks and improve the resilience of the methods necessary to increase the generalizability and speed training and simple hardware implementation, the absence of environmental efficiency pattern classification. This research utilizing neural network-based method that is capable mengefisiensi RAM memory used for the navigation system. This study begins by designing a computer program using the C # language. Program design results are used to test the navigation system using a number of Pattern Classification 5 environment using infrared sensors and showed that the sensors S1, S2 and S4 success rate of 96.47% for the furthest distance and the success rate for the closest distance at 99, 22%. Sensor S3 and S5 to the success rate of 97.65% for the furthest distance and the success rate for the shortest distance is equal to 99.31% as well as the movement of the robot when the robot car better read each environment pattern as had been trained on artificial neural network-based RAM is consistent with the efficient use of memory data by 5 bits where each data adalah10000b, 01110b, 01101b, 01010b.
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