Modular Weightless Neural Network Architecture for Intelligent Navigation

Nurmaini, Siti and Hashim, Siti Zaiton Mohd and Jawawi, Dayang Norhayati Abang (2009) Modular Weightless Neural Network Architecture for Intelligent Navigation. Int. J. Advance. Soft Comput. Appl., 1 (1). ISSN 2074-8523

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Abstract

The standard multi layer perceptron neural network (MLPNN) type has various drawbacks, one of which is training requires repeated presentation of training data, which often results in very long learning time. An alternative type of network, almost unique, is the Weightless Neural Network (WNNs) this is also called n-tuple networks or RAM based networks. In contrast to the weighted neural models, there are several one-shot learning algorithms for WNNs where training takes only one epoch. This paper describes WNNs for recognizes and classifies the environment in mobile robot using a simple microprocessor system. We use a look-up table to minimize the execution time, and that output stored into the robot RAM memory and becomes the current controller that drives the robot. This functionality is demonstrated on a mobile robot using a simple, 8 bit microcontroller with 512 bytes of RAM. The WNNs approach is code efficient only 500 bytes of source code, works well, and the robot was able to successfully recognize the obstacle in real time.

Item Type: Article
Additional Information: Dr. Ir. Siti Nurmaini, M.T Place/Date of Birth: Palembang, 2 Agustus 1969 Department of Computer Engineering, Faculty of Computer Science, Sriwijaya University Phone: +62711379249, +627117072729 Fax: +62711379248 Email: siti_nurmaini@unsri.ac.id Official Blog: http://sitinurmaini.unsri.ac.id
Uncontrolled Keywords: Weightless neural network, environmental recognition, microprocessor system, embedded application
Subjects: Q Science > QA Mathematics > QA1-43 General
Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76 Computer software
Divisions: 09-Faculty of Computer Science > 56401-Computer Engineering (D3)
Depositing User: Dr. Ir. MT Siti Nurmaini
Date Deposited: 30 Sep 2019 02:51
Last Modified: 30 Sep 2019 02:51
URI: http://repository.unsri.ac.id/id/eprint/8839

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