Читать книгу The Digital Agricultural Revolution - Группа авторов - Страница 27

ANNs resembles the human brain based on the principle that:

 Information is processed by basic units known as neurons.

 Signals are transmitted from one neuron to the next via connecting links.

 Each connecting link has a weight associated with it, which amplifies the signal transmitted in a conventional neural network.

 To determine its output signal, each neuron’s net input passes through the activation function.

One of the popular architectures of ANN is a Multiple-layer perceptron (MLP) which consists of input, hidden, and output layers. Multiple-layer perceptrons have been successfully trained in a supervised manner utilizing a widely used method known as the Error Back Propagation Algorithm to solve a variety of complex and diverse tasks. The input layer consists of nodes that receive information from external sources and passes this information to one or more hidden layers of computation nodes and an output layer of computation nodes. During the training phase, the output is calculated for every given input and compared with the desired output. Based on the error, the network is updated. During the testing phase, the network will calculate the output for any new input data. Each conclusion has a probability assigned to it. For the most part, ANN is thought to be a good answer to difficult situations. They solve intricate relationships between crop production and interconnected characteristics that linear systems can’t solve. Artificial Neural Networks are computer programs that simulate the functioning of the human brain. Artificial Neural Network is a task-based strategy that instructs the system to work based on an internal task rather than a computationally programmed task.