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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN""http://www.w3.org/TR/html4/loose.dtd"><HTML><HEAD><TITLE>Neural Network Theory</TITLE><link href="../style.css" rel="stylesheet" type="text/css"><METANAME="GENERATOR"CONTENT="Modular DocBook HTML Stylesheet Version 1.79"><LINKREL="HOME"TITLE="Fast Artificial Neural Network Library"HREF="index.html"><LINKREL="PREVIOUS"TITLE="Precision of a Fixed Point ANN"HREF="x217.html"><LINKREL="NEXT"TITLE="Artificial Neural Networks"HREF="x241.html"></HEAD><BODYCLASS="chapter"BGCOLOR="#FFFFFF"TEXT="#000000"LINK="#0000FF"VLINK="#840084"ALINK="#0000FF"><DIVCLASS="NAVHEADER"><TABLESUMMARY="Header navigation table"WIDTH="100%"BORDER="0"CELLPADDING="0"CELLSPACING="0"><TR><THCOLSPAN="3"ALIGN="center">Fast Artificial Neural Network Library</TH></TR><TR><TDWIDTH="10%"ALIGN="left"VALIGN="bottom"><AHREF="x217.html"ACCESSKEY="P">Prev</A></TD><TDWIDTH="80%"ALIGN="center"VALIGN="bottom"></TD><TDWIDTH="10%"ALIGN="right"VALIGN="bottom"><AHREF="x241.html"ACCESSKEY="N">Next</A></TD></TR></TABLE><HRALIGN="LEFT"WIDTH="100%"></DIV><DIVCLASS="chapter"><H1><ANAME="theory"></A>Chapter 4. Neural Network Theory</H1><P> This section will briefly explain the theory of neural networks (hereafter known as NN) and artificial neural networks (hereafter known as ANN). For a more in depth explanation of these concepts please consult the literature; [<AHREF="b3048.html#bib.hassoun_1995"><I>Hassoun, 1995</I></A>] has good coverage of most concepts of ANN and [<AHREF="b3048.html#bib.hertz_1991"><I>Hertz et al., 1991</I></A>] describes the mathematics of ANN very thoroughly, while [<AHREF="b3048.html#bib.anderson_1995"><I>Anderson, 1995</I></A>] has a more psychological and physiological approach to NN and ANN. For the pragmatic I (SN) could recommend [<AHREF="b3048.html#bib.tettamanzi_2001"><I>Tettamanzi and Tomassini, 2001</I></A>], which has a short and easily understandable introduction to NN and ANN. </P><DIVCLASS="section"><H1CLASS="section"><ANAME="theory.neural_networks">4.1. Neural Networks</A></H1><P> The human brain is a highly complicated machine capable of solving very complex problems. Although we have a good understanding of some of the basic operations that drive the brain, we are still far from understanding everything there is to know about the brain. </P><P> In order to understand ANN, you will need to have a basic knowledge of how the internals of the brain work. The brain is part of the central nervous system and consists of a very large NN. The NN is actually quite complicated, so the following discussion shall be relegated to the details needed to understand ANN, in order to simplify the explanation. </P><P> The NN is a network consisting of connected neurons. The center of the neuron is called the nucleus. The nucleus is connected to other nucleuses by means of the dendrites and the axon. This connection is called a synaptic connection. </P><P> The neuron can fire electric pulses through its synaptic connections, which is received at the dendrites of other neurons. </P><P> When a neuron receives enough electric pulses through its dendrites, it activates and fires a pulse through its axon, which is then received by other neurons. In this way information can propagate through the NN. The synaptic connections change throughout the lifetime of a neuron and the amount of incoming pulses needed to activate a neuron (the threshold) also change. This behavior allows the NN to learn. </P><P> The human brain consists of around 10^11 neurons which are highly interconnected with around 10^15 connections [<AHREF="b3048.html#bib.tettamanzi_2001"><I>Tettamanzi and Tomassini, 2001</I></A>]. These neurons activates in parallel as an effect to internal and external sources. The brain is connected to the rest of the nervous system, which allows it to receive information by means of the five senses and also allows it to control the muscles. </P></DIV></DIV><DIVCLASS="NAVFOOTER"><HRALIGN="LEFT"WIDTH="100%"><TABLESUMMARY="Footer navigation table"WIDTH="100%"BORDER="0"CELLPADDING="0"CELLSPACING="0"><TR><TDWIDTH="33%"ALIGN="left"VALIGN="top"><AHREF="x217.html"ACCESSKEY="P">Prev</A></TD><TDWIDTH="34%"ALIGN="center"VALIGN="top"><AHREF="index.html"ACCESSKEY="H">Home</A></TD><TDWIDTH="33%"ALIGN="right"VALIGN="top"><AHREF="x241.html"ACCESSKEY="N">Next</A></TD></TR><TR><TDWIDTH="33%"ALIGN="left"VALIGN="top">Precision of a Fixed Point ANN</TD><TDWIDTH="34%"ALIGN="center"VALIGN="top"> </TD><TDWIDTH="33%"ALIGN="right"VALIGN="top">Artificial Neural Networks</TD></TR></TABLE></DIV></BODY></HTML>⌨️ 快捷键说明
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