<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN"> <HTML ><HEAD ><TITLE >fann_init_weights</TITLE ><link href="../style.css" rel="stylesheet" type="text/css"><META NAME="GENERATOR" CONTENT="Modular DocBook HTML Stylesheet Version 1.7"><LINK REL="HOME" TITLE="Fast Artificial Neural Network Library" HREF="index.html"><LINK REL="UP" TITLE="API Reference" HREF="x2553.html"><LINK REL="PREVIOUS" TITLE="fann_randomize_weights" HREF="r2688.html"><LINK REL="NEXT" TITLE="fann_get_MSE" HREF="r2740.html"></HEAD ><BODY CLASS="refentry" BGCOLOR="#FFFFFF" TEXT="#000000" LINK="#0000FF" VLINK="#840084" ALINK="#0000FF" ><DIV CLASS="NAVHEADER" ><TABLE SUMMARY="Header navigation table" WIDTH="100%" BORDER="0" CELLPADDING="0" CELLSPACING="0" ><TR ><TH COLSPAN="3" ALIGN="center" >Fast Artificial Neural Network Library</TH ></TR ><TR ><TD WIDTH="10%" ALIGN="left" VALIGN="bottom" ><A HREF="r2688.html" ACCESSKEY="P" >Prev</A ></TD ><TD WIDTH="80%" ALIGN="center" VALIGN="bottom" ></TD ><TD WIDTH="10%" ALIGN="right" VALIGN="bottom" ><A HREF="r2740.html" ACCESSKEY="N" >Next</A ></TD ></TR ></TABLE ><HR ALIGN="LEFT" WIDTH="100%"></DIV ><H1 ><A NAME="function.fann_init_weights" ></A >fann_init_weights</H1 ><DIV CLASS="refnamediv" ><A NAME="AEN2715" ></A ><H2 >Name</H2 >fann_init_weights -- Initialize the weight of each connection.</DIV ><DIV CLASS="refsect1" ><A NAME="AEN2718" ></A ><H2 >Description</H2 ><code class="methodsynopsis" > <span class="type" >void </span >fann_init_weights(<span class="methodparam" ><span class="type" >resource </span ><span class="parameter" >ann</span ></span ><span class="methodparam" >, <span class="type" >mixed </span ><span class="parameter" >training_data</span ></span >); </code ><P > This function behaves similarly to <A HREF="r2688.html" ><CODE CLASS="function" >fann_randomize_weights</CODE ></A >. It will use the algorithm developed by Derrick Nguyen and Bernard Widrow [<A HREF="b3048.html#bib.nguyen_1990" ><I >Nguyen and Widrow, 1990</I ></A >] to set the weights in such a way as to speed up training. </P ><P > The algorithm requires access to the range of the input data (ie, largest and smallest input), and therefore accepts a second argument, <VAR CLASS="parameter" >data</VAR >, which is the training data that will be used to train the network. </P ><P > See also: <A HREF="c104.html#adv.adj" ><I >Adjusting Parameters</I ></A >, <A HREF="r2688.html" ><CODE CLASS="function" >fann_randomize_weights</CODE ></A > </P ><P >This function appears in FANN-PHP >= 0.1.0.</P ></DIV ><DIV CLASS="NAVFOOTER" ><HR ALIGN="LEFT" WIDTH="100%"><TABLE SUMMARY="Footer navigation table" WIDTH="100%" BORDER="0" CELLPADDING="0" CELLSPACING="0" ><TR ><TD WIDTH="33%" ALIGN="left" VALIGN="top" ><A HREF="r2688.html" ACCESSKEY="P" >Prev</A ></TD ><TD WIDTH="34%" ALIGN="center" VALIGN="top" ><A HREF="index.html" ACCESSKEY="H" >Home</A ></TD ><TD WIDTH="33%" ALIGN="right" VALIGN="top" ><A HREF="r2740.html" ACCESSKEY="N" >Next</A ></TD ></TR ><TR ><TD WIDTH="33%" ALIGN="left" VALIGN="top" >fann_randomize_weights</TD ><TD WIDTH="34%" ALIGN="center" VALIGN="top" ><A HREF="x2553.html" ACCESSKEY="U" >Up</A ></TD ><TD WIDTH="33%" ALIGN="right" VALIGN="top" >fann_get_MSE</TD ></TR ></TABLE ></DIV ></BODY ></HTML >
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