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The study of artificial neural networks (ANNs) has been inspired in part by the observation that biological learning systems are built of very complex webs of interconnected neurons In rough analogy, artificial neural networks are built out of a densely interconnected set of simple units, where each unit takes a number of real-valued inputs (possibly the outputs of other units) and produces a single real-valued output (which may become the input to many other units) To develop a feel for this analogy, let us consider a few facts from neurobiology The human brain, for example, is estimated to contain a densely interconnected network of approximately 1011 neurons, each connected, on average, to lo4 others Neuron activity is typically excited or inhibited through connections to other neurons The fastest neuron switching times are known to be on the order of loe3 seconds--quite slow compared to computer switching speeds of 10-lo seconds Yet humans are able to make surprisingly complex decisions, surprisingly quickly For example, it requires approximately lo-' seconds to visually recognize your mother Notice the sequence of neuron firings that can take place during this 10-'-second interval cannot possibly be longer than a few hundred steps, given the switching speed of single neurons This observation has led many to speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons One motivation for ANN systems is to capture this kind of highly parallel computation based on distributed representations Most ANN software runs on sequential machines emulating distributed processes, although faster versions of the algorithms have also been implemented on highly parallel machines and on specialized hardware designed specifically for ANN applications While ANNs are loosely motivated by biological neural systems, there are many complexities to biological neural systems that are not modeled by ANNs, and many features of the ANNs we discuss here are known to be inconsistent with biological systems For example, we consider here ANNs whose individual units output a single constant value, whereas biological neurons output a complex time series of spikes Historically, two groups of researchers have worked with artificial neural networks One group has been motivated by the goal of using ANNs to study and model biological learning processes A second group has been motivated by the goal of obtaining highly effective machine learning algorithms, independent of whether these algorithms mirror biological processes Within this book our interest fits the latter group, and therefore we will not dwell further on biological modeling For more information on attempts to model biological systems using ANNs, see, for example, Churchland and Sejnowski (1992); Zornetzer et al (1994); Gabriel and Moore (1990).

The resizeTo() method is used to resize a window to a specific dimension in pixels based on the parameters sent in the method call The syntax and usage are the same as for the moveTo() method; resizeTo() just performs a resize instead You input the new width and height in place of the coordinates used in the moveTo() method

The scrollBy() method is used to scroll a window by the number of pixels given in the parameters sent in the method call The syntax and usage are the same as for the moveBy() method; scrollBy() just performs a scroll instead To make the window larger, use positive numbers To make it smaller, use negative numbers

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trains a network by beginning with near-zero weights, then iteratively modifying these weights until an acceptable hypothesis is found Thus, BACKPROPAGATION with a cross-validation stopping criterion exhibits an inductive bias in favor of networks with small weights This inductive bias, which reduces the effective VC dimension, is not captured by the above analysis

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The scrollTo() method is used to scroll a window to a specific destination in pixels based on the parameters sent in the method call The syntax and usage are the same as for the moveTo() method; scrollTo() just performs a scroll to the specified point instead

The setInterval() method is used to execute a JavaScript function repeatedly at a set interval The following is the general syntax for using this method:

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windowsetInterval("function()",time);

4, or 5 wire will carry either the 4 Mbps or 16 Mbps data rates, although the Category 5e standards offer future growth to 100 Mbps networking The standard distance limits for station cable in TIA/EIA-568-C will take care of Token-Ring networks in most instances, because each workstation acts as a repeater to the next workstation However, if you wish to calculate the actual working distances according to IBM s recommendations, you must include a complex series of derating factors that take into account the number of wiring closets and MSAUs These calculations yield the maximum allowable station cable lengths and the allowable cable lengths between wiring closets Table 21 shows a typical planning table for type 1 STP cable at 16 Mbps For a relatively simple network that involves only one or two wiring closets or, if you use repeaters or bridges between closets, you may be able to bypass the calculations However, if your network is complex, you should consult one of the network planning guides from IBM and other Token-Ring MSAU component vendors Because Token-Ring may be run on either 150-ohm STP or 100-ohm UTP, potential impedance-matching problems will exist if you have a mixed-media network For example, the Token-Ring network interface card (NIC) generally has a 9pin d-shell connector with a 150-ohm interface If your station cable (from the wall jack to the wiring closet) is 100-ohm UTP, you will have to use a media filter cable from the NIC to the wall jack to translate from the 150-ohm NIC interface to the 100-ohm cable Figure 210 shows a typical media filter The media filter actually contains an impedance transformer (balun) that compensates for the impedance difference Operating without a media filter can cause serious problems that will limit lobe distances and cause unwanted signal reflections Wiring-related problems that may be encountered with Token-Ring networks include excessive distance between workstations and MSAUs or between wiring.

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