Detailed explanation of artificial neural network algorithm in PHP

Detailed explanation of artificial neural network algorithm in PHP

Introduction:
Artificial neural network is a mathematical model that simulates the connection of human brain neurons and is widely used in the fields of machine learning and data mining. This article will introduce the artificial neural network algorithm in PHP in detail and provide code examples to help readers better understand.

1. What is artificial neural network?
Artificial Neural Network (ANN) consists of neurons and the connections between them. Each neuron receives a set of input signals, weights and sums these signals through weights, processes them with a nonlinear activation function, and finally generates an output signal.

2. Artificial Neural Network Algorithm in PHP
In PHP, there are many open source libraries that can be used to implement artificial neural network algorithms, such as Encog, PHPSOM and Neural Network PHP. The following uses the Encog library as an example to explain.

1. Environment configuration
   First, we need to install the Encog library in the PHP environment. It can be installed through Composer, add the following code to the composer.json file:

{
  "require": {
    "encog/encog": "3.4.0"
  }
}

Then run the following command to install:

composer install

2. Create Neural Network Model
   Next, we create a simple neural network model through the following code:

use EncogEngineNetworkActivationActivationSigmoid;
use EncogEngineNetworkFeedforwardFeedforwardNetwork;
use EncogEngineUtilNetworkUtil;
use EncogMLDataBasicMLData;
use EncogMLDataMLData;
use EncogMLDataMLDataSet;
use EncogMLDataSpecificCSVCSVFormat;
use EncogMLDataSpecificCSVCSVMLDataSet;

$network = new FeedforwardNetwork();
$network->addLayer(NetworkUtil::createLayer(new ActivationSigmoid(), 2));
$network->addLayer(NetworkUtil::createLayer(new ActivationSigmoid(), 4));
$network->addLayer(NetworkUtil::createLayer(new ActivationSigmoid(), 1));
$network->getStructure()->finalizeStructure();
$network->reset();

The above code creates a model with 2 input layer neurons, 4 hidden layer neurons and 1 output Neural network model of layer neurons.

3. Training and testing
   Next, we need to prepare training data and test data, and train and test through the Encog library.

$format = new CSVFormat(',', '"');
$data = new CSVMLDataSet(__DIR__ . "/data.csv", 2, 1, false, $format);
$train = new ResilientPropagation($network, $data);
$train->train();

$input = new BasicMLData([0.1, 0.2]);
$output = $network->compute($input);
echo "Output:" . $output->getData(0) . "
";

The above code reads the training data set named data.csv and uses the ResilientPropagation algorithm to train the neural network. Then, we get the output from the given input.

Summary:
This article introduces the artificial neural network algorithm in PHP in detail and provides code examples of the Encog library. Through learning and practice, readers can use artificial neural network algorithms to solve machine learning and data mining problems in PHP. At the same time, readers can also try other open source libraries to implement artificial neural network algorithms to meet different needs.

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