mhjensen
199 lines
9.0 KiB
HTML
199 lines
9.0 KiB
HTML
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs001.html#___sec0" style="font-size: 80%;"><b>Neural networks</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs002.html#___sec1" style="font-size: 80%;"><b>Artificial neurons</b></a></li>
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<!-- navigation toc: --> <li><a href="#___sec5" style="font-size: 80%;"><b>Other types of networks</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs009.html#___sec8" style="font-size: 80%;"><b>Mathematical model</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs012.html#___sec11" style="font-size: 80%;"><b>Mathematical model</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs013.html#___sec12" style="font-size: 80%;"><b>Mathematical model</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs014.html#___sec13" style="font-size: 80%;"> Matrix-vector notation</a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs015.html#___sec14" style="font-size: 80%;"> Matrix-vector notation and activation</a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs016.html#___sec15" style="font-size: 80%;"> Activation functions</a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs017.html#___sec16" style="font-size: 80%;"> Activation functions, Logistic and Hyperbolic ones</a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs018.html#___sec17" style="font-size: 80%;"> Relevance</a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs019.html#___sec18" style="font-size: 80%;"><b>Setting up a Multi-layer perceptron model</b></a></li>
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<!-- navigation toc: --> <li><a href="._NeuralNet-bs020.html#___sec19" style="font-size: 80%;"><b>Two-layer Neural Network</b></a></li>
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<a name="part0006"></a>
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<!-- !split -->
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<h2 id="___sec5" class="anchor">Other types of networks </h2>
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<p>
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There are many other kinds of NNs that have been developed. One type that is specifically designed for interpolation
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in multidimensional space is the radial basis function (RBF) network. RBFs are typically made up of three layers:
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an input layer, a hidden layer with non-linear radial symmetric activation functions and a linear output layer (''linear'' here
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means that each node in the output layer has a linear activation function). The layers are normally fully-connected and
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there are no cycles, thus RBFs can be viewed as a type of fully-connected FFNN. They are however usually treated as
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a separate type of NN due the unusual activation functions.
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<p>
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<li><a href="._NeuralNet-bs005.html">«</a></li>
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<li><a href="._NeuralNet-bs000.html">1</a></li>
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<li><a href="._NeuralNet-bs001.html">2</a></li>
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<li><a href="._NeuralNet-bs002.html">3</a></li>
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<li><a href="._NeuralNet-bs003.html">4</a></li>
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<li><a href="._NeuralNet-bs004.html">5</a></li>
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<li><a href="._NeuralNet-bs005.html">6</a></li>
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<li class="active"><a href="._NeuralNet-bs006.html">7</a></li>
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<li><a href="._NeuralNet-bs007.html">8</a></li>
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<li><a href="._NeuralNet-bs008.html">9</a></li>
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<li><a href="._NeuralNet-bs009.html">10</a></li>
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<li><a href="._NeuralNet-bs010.html">11</a></li>
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<li><a href="._NeuralNet-bs011.html">12</a></li>
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<li><a href="._NeuralNet-bs012.html">13</a></li>
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<li><a href="._NeuralNet-bs013.html">14</a></li>
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<li><a href="._NeuralNet-bs014.html">15</a></li>
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<li><a href="._NeuralNet-bs015.html">16</a></li>
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<li><a href="">...</a></li>
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<li><a href="._NeuralNet-bs020.html">21</a></li>
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