{"title": "A Hybrid Radial Basis Function Neurocomputer and Its Applications", "book": "Advances in Neural Information Processing Systems", "page_first": 850, "page_last": 857, "abstract": null, "full_text": "A Hybrid Radial Basis Function Neurocomputer \n\nand Its Applications \n\nSteven S. Watkins \nECE Department \n\nUCSD \n\nLa Jolla. CA. 92093 \n\nPaul M. Chau \nECE Department \n\nUCSD \n\nLa Jolla, CA. 92093 \n\nRaoul Tawel \n\nJPL \n\nCaltech \n\nPasadena. CA. 91109 \n\nBjorn Lambrigtsen \n\nJPL \n\nCaltech \n\nPasadena. CA. 91109 \n\nMark Plutowski \nCSE Department \n\nUCSD \n\nLa Jolla. CA. 92093 \n\nAbstract \n\nA neurocomputer was implemented using radial basis functions and a \ncombination of analog and digital VLSI circuits. The hybrid system \nuses custom analog circuits for the input layer and a digital signal \nprocessing board for the hidden and output layers. The system combines \nthe advantages of both analog and digital circuits. featuring low power \nconsumption while minimizing overall system error. The analog circuits \nhave been fabricated and tested, the system has been built, and several \napplications have been executed on the system. One application \nprovides significantly better results for a remote sensing problem than \nhave been previously obtained using conventional methods. \n\n1.0 Introduction \n\nThis paper describes a neurocomputer development system that uses a radial basis \nfunction as the transfer function of a neuron rather than the traditional sigmoid function. \nThis neurocOOlputer is a hybrid system which has been implemented with a combination \nof analog and digital VLSI technologies. It offers the low-power advantage of analog \ncircuits operating in the subthreshold region and the high-precision advantage of digital \ncircuits. The system is targeted for applications that require low-power operation and use \ninput data in analog form, particularly remote sensing and portable computing \napplications. It has already provided significantly better results for a remote sensing \n\n850 \n\n\fA Hybrid Radial Basis Function Neurocomputer and Its Applications \n\n851 \n\n,-------- - ----- - -\n\nNEURON \n\n-\n\nYo \n\n2 \n(c I k - 'k) \n\n:E \n\nEXPONENTlAL \n\n'0 \n\n'(cid:173)\n\n'NPUTS \n\nMUL TlPL Y AND ACCUMULATE \n\nFigure I: Radial Basis Function Network \n\nNEURoN \n\n[~ \n\nFigure 2: Mapping of RBF Network to Hardware \n\nAnalog Board \n\n= \n= \n\nPC \n\nFigure 3: The RBF Neurocomputer Development System \n\n\f852 \n\nWatkins, Chau, Tawel, Lambrigsten, and Plutowski \n\nclimate problem than have been previously obtained using conventional methods. \nFigure 1 illustrates a radial basis functioo (RBF) network. Radial basis functions have \nbeen used to solve mapping and function estimation problems with positive results \n(Moody and Darken. 1989; Lippman, 1991). When coupled with a dynamic neuron \nallocation algorithm such as Platt's RANN (platt. 1991). RBF networks can usually be \ntrained much more quickly than a traditional sigmoidal. back-propagation network. \nRBF networlcs have been implemented with completely-analog (platt, Anderson and Kirk. \n1993), c