APPLICATION ARTIFICIAL NETWORK FROM DATABASE PREDICTED NORMALIZED TEMPERATURE AND HUMIDITY IN ROOM

Abstract

The Artificial neural network is a mathematical model that tries to simulate the structure and functionalities of biological neural networks. A neural network is a machine that is designed to model the way in which the brain performs a particular task or function of interest. Basic building block of every artificial neural network is neuron. Such a model has three simple sets of multiplication, summation and activation. The purpose of this network is to examine neural network and their emerging applications in the field of engineering focusing on control. The network is implemented by using electronic components and is simulated in software on a digital computer. In this work examined the application of neural network for predicted normalized temperature and humidity in room and the learning process. A neural network derives its computing through its massively parallel distributed structure and its ability to learn and generalize. Generalization refers to the neural network’s production of reasonable outputs for inputs not encountered during training or learning. The function of which is to modify the synaptic weights of the network in an orderly fashion to attain a desired design objective. The needs for neural networks, training of neural networks and important algorithms have been discussed. Artificial Neuron is sum function that sums all weighted inputs and bias. At the exit of artificial neuron the sum of previously weighted inputs and bias is passing trough activation function that is also called transfer function. It concluded by identifying limitations, recent advances and promising future research directions.