Abstract

Artificial Neurons Network (ANN) is used in the decision and control of dynamic systems which can be with a lack of superfluous information.it forces the use of fuzzy logic.

For this reason, several methods and monitoring techniques have been implemented. This article presents a technique based on artificial neural networks implanted at the level of a multisensor surveillance system. It is a statistical learning method that displays optimal training and generalization performance in several domains, including the recognition domain of forms.

In this case ANN based on raspberry PI card for decision node and arduino for the input and hidden nodes, in order to develop a complete platform environmental monitoring system. and hence enhance multi-Sensor wireless signals aggregation via multi-bit decision fusion.

The back-propagation algorithm generates a weight for all nodes in the networks, with aim of minimizing absolute error committed in fusion data and economics of electrical energy using artificial intelligence techniques. This algorithm is more efficient than the human being since it can reason and learn from its errors so as not to repeat them.

Its main applications include a variety of data monitoring parameters (such as : temperature, humidity, gas sensor, … etc), that can be found in factory automation, for instance : home automation, remote monitoring and home device control, or it may be used in environment to make an exact decision in short time.

This work is licensed under a Creative Commons Attribution 4.0 License.