Rosenblatt

W. J. Lyman, W. F. Reehl, D. H. Rosenblatt, Handbook of Chemical Property Estimation Methods American Chemical Society, Washington (1990). 

A. A. Rosenblatt and J. E. Knapp, Health Industry Manufacturer s Association (HTMAJ Conference Proceedings, 1988, Washington, D.C., pp. 47—50. 

J. B. Hutchison, in Advances in the Study of Behavior, ed. J. Rosenblatt, Academic Press, New York,... 

P.A. Hookham, D. Hatfield, and M. Rosenblatt, Calculation of the Interaction of Three Spherical Blast Waves Over a Planar Surface with an Adaptive-Grid TVD Code, California Research and Technology Report, Chatsworth, CA, 1991. 

Artificial Neural Networks (ANNs) attempt to emulate their biological counterparts. McCulloch and Pitts (1943) proposed a simple model of a neuron, and Hebb (1949) described a technique which became known as Hebbian learning. Rosenblatt (1961), devised a single layer of neurons, called a Perceptron, that was used for optical pattern recognition. 

Rosenblatt, F. (1961) Principles of Neurodynamics Perceptrons and the Theory of Brain Mechanisms, Spartan Press, Washington, DC. 

A much more pronounced vortex formation in expanding combustion products was found by Rosenblatt and Hassig (1986), who employed the DICE code to simulate deflagrative combustion of a large, cylindrical, natural gas-air cloud. DICE is a Eulerian code which solves the dynamic equations of motion using an implicit difference scheme. Its principles are analogous to the ICE code described by Harlow and Amsden (1971). 

With respect to blast effects, Rosenblatt and Hassig s (1986) conclusions are fully in line with those of Raju and Strehlow (1984). Except in a limited area at the cloud s edge, the blast peak overpressures are produced by the very first stage of flame propagation, during which the flame is spherical. 

Rosenblatt, M., and P. J. Hassig. 1986. Numerical simulation of the combustion of an unconfined LNG vapor cloud at a high constant burning velocity. Combust. Science and Tech. 45 245-259. 

G. Gordon, R. G. Kieffer and D. H. Rosenblatt, Progr. Inorg. Chem. 15, 201-86 (1972). The first half of this review deals with the aqueous solution chemistry of chlorous acid and chlorites. 

Fig. 10.2 Schematic representation of connections in Rosenblatt s Photoreceptron [rosenbl58]. The synaptic connections from the S-units to A-units can be either excitatory or inhibitory connections between A-units and R-units may include inhibitory feedback loops. Response layer units are also linked to other R-units with inhibitory connections.

A natural question to ask is whether the basic model can be modified in some way that would enable it to correctly learn the XOR function or, more generally, any other non-linearly-separable problem. The answer is a qualified yes in principle, all that needs to be done is to add more layers between what we have called the A-units and R-units. Doing so effectively generates more separation lines, which when combined can successfully separate out the desired regions of the plane. However, while Rosenblatt himself considered such variants, at the time of his original analysis (and for quite a few years after that see below) no appropriate learning rule was known. 

R. Grinstead K.C. Jonos, Nitrate Removal from Waste Water by Ion Exchange , Rpt 17010 FSJ, US Environmental Protection Agency (1971) 11) D.H. Rosenblatt, G-E. 

Sublimation. During sublimation, the lattice constituents of the solid are directly transferred to the gas phase without the intervention of liquefaction, though there may be mobile intermediates at the surface of the heated solid. Various features of the sublimation process have been reviewed by Somorjai [18] and by Rosenblatt [19] who included consideration of kinetic aspects. Rhead [ 20] has discussed diffusion processes at surfaces. 

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