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Artificial neural networks are widely used in various fields. In meteorology, they are most commonly used for short-term forecasting. The main application domain of ANNs is short-term forecasting. It is used to predict the weather parameters within a few days, such as wind speed, temperature and atmospheric pressure. This ability is particularly useful for agricultural meteorology because it allows farmers to choose the best time to plant their crops (Kolioura et al. 2002).
The neural network has been applied to a wide variety of scientific problems since the late 1960s. From the beginning, many researchers have been attracted by the neural network modeling approach, which can discover non-linear relationships that are difficult to estimate using conventional regression methods.
There are many algorithms for the training of neural networks: the simple Hebbian algorithm, the Levenberg-Marquardt algorithm, the conjugate gradient method and its variants, the conjugate gradient method (with a suitable modification) and the gradient descent method. Although there are many methods for training networks, the simple method of the Levenberg-Marquardt algorithm is the most commonly used one.
An ANN is a learning system which develops its internal structure as it learns from data. The learning process is accomplished by a process called back-propagation, which is a technique often used in neural network training to adjust the weights of the connections between neurons. The weights represent the amount of influence that the synapses have on the neurons. In the back-propagation technique, the learning rate is low and the weights are allowed to change slowly during the training process. The goal of the training process is to find the optimal weights so that the error function (i.e., the difference between the output and the target values) is minimized.
ANNs consist of many layers. These layers can be analogized to individual processing units of a computer, such as a neuron in the brain. The first layer is called the input layer and it receives data from other layers. The second layer is called the hidden layer and it is used to perform a data transformation and/or reduce the dimensionality of the data. The third layer is called the output layer and it generates the result that is the output of the network.
Artificial Neural Networks have been used for many decades for pattern recognition, data mining, data classification, regression analysis, financial forecasting and many other areas. The first artificial neural network designed for pattern recognition was created in the 1950s by Frank Rosenblatt.
The initial artificial neural network was developed in the 1950s and it was used for pattern recognition. Later the neural networks were used for controlling robots and other computer programs.
Artificial neural networks consist of three layers, the input layer, an intermediate or hidden layer, and the output layer. The input layer consists of neurons that receive data to be processed by the neural network. The data is received by the neurons and processed by the network. The output layer consists of neurons that are the result of the processing of the data. The neurons transfer the result of the activity back into the input layer and signal when a desired result has been achieved by the system. 827ec27edc