Artificial Neural Networks (ANN) or, more simply, neural networks, new computing systems and computing methods for machine learning, knowledge representation, and, finally, applying knowledge to the vast majority of output responses from complex systems. The main idea behind these networks is to some extent inspired by the way the biological nervous system functions to process data and information in order to learn and create knowledge.
The key element of this idea is to create new structures for the information processing system. The system consists of a large number of super-integrated processing elements called neurons that work together to solve a problem and transmit information through synapses (electromagnetic communications). In these networks, if a cell is damaged, other cells can compensate for it and also contribute to its reconstruction. These networks are able to learn. For example, by applying irritation to tactile neurons, the cells remember to not go to the hot object, and with this algorithm, the system learns to correct its error. Learning in these systems is adaptive, that is, by using the examples, the weight of the synapse changes in such a way that, in the case of new inputs, the system generates the correct response.
This book deals with the comprehensive study of the foundations of the neural network and its applications in various sciences, which tried to accurately and easily write the contents of the applied topics in this field.