A radial basis function neurocomputer implemented with analog VLSI circuits

Autor:	S.S. Watkins, Paul M. Chau, R. Tawel
Rok vydání:	2003
Předmět:	Very-large-scale integration Signal processing Digital signal processor Artificial neural network business.industry Computer science Analog computer Transfer function law.invention Computer architecture law Radial basis function business Computer hardware Electronic circuit
Zdroj:	[Proceedings 1992] IJCNN International Joint Conference on Neural Networks.
DOI:	10.1109/ijcnn.1992.226921
Popis:	An electronic neurocomputer which implements a radial basis function neural network (RBFNN) is described. The RBFNN is a network that utilizes a radial basis function as the transfer function. The key advantages of RBFNNs over existing neural network architectures include reduced learning time and the ease of VLSI implementation. This neurocomputer is based on an analog/digital hybrid design and has been constructed with both custom analog VLSI circuits and a commercially available digital signal processor. The hybrid architecture is selected because it offers high computational performance while compensating for analog inaccuracies, and it features the ability to model large problems. >
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::59fe8b155dcede8c2e600f82f9f87d7e https://doi.org/10.1109/ijcnn.1992.226921 Zobrazit plný text záznamu