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一个功能强大的神经网络分析程序

><A
HREF="r1323.html"
>fann_set_rprop_decrease_factor</A
>&nbsp;--&nbsp;Set the decrease factor used by RPROP training.</DT
><DT
><A
HREF="r1341.html"
>fann_get_rprop_delta_min</A
>&nbsp;--&nbsp;Get the minimum step-size used by RPROP training.</DT
><DT
><A
HREF="r1356.html"
>fann_set_rprop_delta_min</A
>&nbsp;--&nbsp;Set the minimum step-size used by RPROP training.</DT
><DT
><A
HREF="r1374.html"
>fann_get_rprop_delta_max</A
>&nbsp;--&nbsp;Get the maximum step-size used by RPROP training.</DT
><DT
><A
HREF="r1389.html"
>fann_set_rprop_delta_max</A
>&nbsp;--&nbsp;Set the maximum step-size used by RPROP training.</DT
><DT
><A
HREF="r1407.html"
>fann_get_num_input</A
>&nbsp;--&nbsp;Get the number of neurons in the input layer.</DT
><DT
><A
HREF="r1422.html"
>fann_get_num_output</A
>&nbsp;--&nbsp;Get number of neurons in the output layer.</DT
><DT
><A
HREF="r1437.html"
>fann_get_total_neurons</A
>&nbsp;--&nbsp;Get the total number of neurons in a network.</DT
><DT
><A
HREF="r1452.html"
>fann_get_total_connections</A
>&nbsp;--&nbsp;Get the total number of connections in a network.</DT
><DT
><A
HREF="r1467.html"
>fann_get_decimal_point</A
>&nbsp;--&nbsp;Get the position of the decimal point.</DT
><DT
><A
HREF="r1483.html"
>fann_get_multiplier</A
>&nbsp;--&nbsp;Get the multiplier.</DT
></DL
></DD
><DT
>5.6. <A
HREF="x1499.html"
>Error Handling</A
></DT
><DD
><DL
><DT
><A
HREF="r1501.html"
>fann_get_errno</A
>&nbsp;--&nbsp;Return the numerical representation of the last error.</DT
><DT
><A
HREF="r1516.html"
>fann_get_errstr</A
>&nbsp;--&nbsp;Return the last error.</DT
><DT
><A
HREF="r1533.html"
>fann_reset_errno</A
>&nbsp;--&nbsp;Reset the last error number.</DT
><DT
><A
HREF="r1547.html"
>fann_reset_errstr</A
>&nbsp;--&nbsp;Reset the last error string.</DT
><DT
><A
HREF="r1561.html"
>fann_set_error_log</A
>&nbsp;--&nbsp;Set the error log to a file descriptor.</DT
><DT
><A
HREF="r1580.html"
>fann_print_error</A
>&nbsp;--&nbsp;Print the last error to the error log.</DT
></DL
></DD
><DT
>5.7. <A
HREF="x1595.html"
>Data Structures</A
></DT
><DD
><DL
><DT
><A
HREF="r1597.html"
>struct fann</A
>&nbsp;--&nbsp;Describes a neural network.</DT
><DT
><A
HREF="r1837.html"
>struct fann_train_data</A
>&nbsp;--&nbsp;Describes a set of training data.</DT
><DT
><A
HREF="r1900.html"
>struct fann_error</A
>&nbsp;--&nbsp;Describes an error.</DT
><DT
><A
HREF="r1936.html"
>struct fann_neuron</A
>&nbsp;--&nbsp;Describes an individual neuron.</DT
><DT
><A
HREF="r1970.html"
>struct fann_layer</A
>&nbsp;--&nbsp;Describes a layer in a network.</DT
></DL
></DD
><DT
>5.8. <A
HREF="x1994.html"
>Constants</A
></DT
><DD
><DL
><DT
><A
HREF="r1996.html"
>Training algorithms</A
>&nbsp;--&nbsp;Constants representing training algorithms.</DT
><DT
><A
HREF="r2030.html"
>Activation Functions</A
>&nbsp;--&nbsp;Constants representing activation functions.</DT
><DT
><A
HREF="r2077.html"
>Training Error Functions</A
>&nbsp;--&nbsp;Constants representing errors functions.</DT
><DT
><A
HREF="r2099.html"
>Error Codes</A
>&nbsp;--&nbsp;Constants representing errors.</DT
></DL
></DD
><DT
>5.9. <A
HREF="x2169.html"
>Internal Functions</A
></DT
><DD
><DL
><DT
>5.9.1. <A
HREF="x2169.html#api.sec.create_destroy.internal"
>Creation And Destruction</A
></DT
><DT
>5.9.2. <A
HREF="x2169.html#api.sec.io.internal"
>Input/Output</A
></DT
><DT
>5.9.3. <A
HREF="x2169.html#api.sec.train_data.internal"
>Training Data</A
></DT
><DT
>5.9.4. <A
HREF="x2169.html#api.sec.io.errors"
>Error Handling</A
></DT
><DT
>5.9.5. <A
HREF="x2169.html#api.sec.options.internal"
>Options</A
></DT
></DL
></DD
><DT
>5.10. <A
HREF="x2399.html"
>Deprecated Functions</A
></DT
><DD
><DL
><DT
>5.10.1. <A
HREF="x2399.html#api.sec.error.deprecated"
>Mean Square Error</A
></DT
><DT
>5.10.2. <A
HREF="x2399.html#api.sec.steepness.deprecated"
>Get and set activation function steepness.</A
></DT
></DL
></DD
></DL
></DD
><DT
>6. <A
HREF="c2519.html"
>PHP Extension</A
></DT
><DD
><DL
><DT
>6.1. <A
HREF="c2519.html#php.install"
>Installation</A
></DT
><DD
><DL
><DT
>6.1.1. <A
HREF="c2519.html#php.install.pear"
>Using PEAR</A
></DT
><DT
>6.1.2. <A
HREF="c2519.html#php.install.ext"
>Compiling into PHP</A
></DT
></DL
></DD
><DT
>6.2. <A
HREF="x2553.html"
>API Reference</A
></DT
><DD
><DL
><DT
><A
HREF="r2555.html"
>fann_create</A
>&nbsp;--&nbsp;Creates an artificial neural network.</DT
><DT
><A
HREF="r2597.html"
>fann_train</A
>&nbsp;--&nbsp;Train an artificial neural network.</DT
><DT
><A
HREF="r2641.html"
>fann_save</A
>&nbsp;--&nbsp;Save an artificial neural network to a file.</DT
><DT
><A
HREF="r2664.html"
>fann_run</A
>&nbsp;--&nbsp;Run an artificial neural network.</DT
><DT
><A
HREF="r2688.html"
>fann_randomize_weights</A
>&nbsp;--&nbsp;Randomize the weights of the neurons in the network.</DT
><DT
><A
HREF="r2714.html"
>fann_init_weights</A
>&nbsp;--&nbsp;Initialize the weight of each connection.</DT
><DT
><A
HREF="r2740.html"
>fann_get_MSE</A
>&nbsp;--&nbsp;Get the mean squared error.</DT
><DT
><A
HREF="r2756.html"
>fann_get_num_input</A
>&nbsp;--&nbsp;Get the number of input neurons.</DT
><DT
><A
HREF="r2777.html"
>fann_get_num_output</A
>&nbsp;--&nbsp;Get the number of output neurons.</DT
><DT
><A
HREF="r2798.html"
>fann_get_total_neurons</A
>&nbsp;--&nbsp;Get the total number of neurons.</DT
><DT
><A
HREF="r2819.html"
>fann_get_total_connections</A
>&nbsp;--&nbsp;Get the total number of connections.</DT
><DT
><A
HREF="r2835.html"
>fann_get_learning_rate</A
>&nbsp;--&nbsp;Get the learning rate.</DT
><DT
><A
HREF="r2854.html"
>fann_get_activation_function_hidden</A
>&nbsp;--&nbsp;Get the activation function of the hidden neurons.</DT
><DT
><A
HREF="r2873.html"
>fann_get_activation_function_output</A
>&nbsp;--&nbsp;Get the activation function of the output neurons.</DT
><DT
><A
HREF="r2892.html"
>fann_get_activation_steepness_hidden</A
>&nbsp;--&nbsp;Get the steepness of the activation function for the hidden neurons.</DT
><DT
><A
HREF="r2911.html"
>fann_get_activation_steepness_output</A
>&nbsp;--&nbsp;Get the steepness of the activation function for the output neurons.</DT
><DT
><A
HREF="r2930.html"
>fann_set_learning_rate</A
>&nbsp;--&nbsp;Set the learning rate.</DT
><DT
><A
HREF="r2949.html"
>fann_set_activation_function_hidden</A
>&nbsp;--&nbsp;Set the activation function for the hidden neurons.</DT
><DT
><A
HREF="r2971.html"
>fann_set_activation_function_output</A
>&nbsp;--&nbsp;Set the activation function for the output neurons.</DT
><DT
><A
HREF="r2993.html"
>fann_set_activation_steepness_hidden</A
>&nbsp;--&nbsp;Set the steepness of the activation function for the hidden neurons.</DT
><DT
><A
HREF="r3015.html"
>fann_set_activation_steepness_output</A
>&nbsp;--&nbsp;Set the steepness of the activation function for the output neurons.</DT
></DL
></DD
></DL
></DD
><DT
>7. <A
HREF="c3037.html"
>Python Bindings</A
></DT
><DD
><DL
><DT
>7.1. <A
HREF="c3037.html#python.install"
>Python Install</A
></DT
></DL
></DD
><DT
><A
HREF="b3048.html"
>Bibliography</A
></DT
></DL
></DIV
><DIV
CLASS="LOT"
><DL
CLASS="LOT"
><DT
><B
>List of Examples</B
></DT
><DT
>1-1. <A
HREF="x68.html#example.simple_train"
>Simple training example</A
></DT
><DT
>1-2. <A
HREF="x68.html#example.simple_exec"
>Simple execution example</A
></DT
><DT
>2-1. <A
HREF="x161.html#example.train_on_file_internals"
>The internals of the <CODE
CLASS="function"
>fann_train_on_file</CODE
> function, without writing the status line.</A
></DT
><DT
>2-2. <A
HREF="x161.html#example.calc_mse"
>Test all of the data in a file and calculates the mean square error.</A
></DT
><DT
>3-1. <A
HREF="c189.html#example.train_fixed"
>An example of a program written to support training in both fixed point and floating point numbers</A
></DT
><DT
>3-2. <A
HREF="x203.html#example.exec_fixed"
>An example of a program written to support both fixed point and floating point numbers</A
></DT
><DT
>5-1. <A
HREF="r285.html#example.api.fann_create_array"
><CODE
CLASS="function"
>fann_create_array</CODE
> example</A
></DT
><DT
>6-1. <A
HREF="r2555.html#example.php.fann_create.scratch"
><CODE
CLASS="function"
>fann_create</CODE
> from scratch</A
></DT
><DT
>6-2. <A
HREF="r2555.html#example.php.fann_create.load"
><CODE
CLASS="function"
>fann_create</CODE
> loading from a file</A
></DT
><DT
>6-1. <A
HREF="r2597.html#example.php.fann_train"
><CODE
CLASS="function"
>fann_create</CODE
> from training data</A
></DT
><DT
>6-1. <A
HREF="r2664.html#example.php.fann_run"
><CODE
CLASS="function"
>fann_run</CODE
>Example</A
></DT
></DL
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