What is an artificial neuron?

Artificial neuron is a mathematical function in software programming for computer systems that to some extent attempt to imitate comprehensive interaction of biological neurons or pulse causing cells in the human brain and nervous system. The first version of the artificial neuron was created in 1943 by Warren McCulloch and Walter Pitts as a form of binary neuron, where the entry could either be 1 or -1. Together, the combination of these inputs is weighed. If a certain threshold is overcome, the output of artificial neuron is 1, and if the inputs are inadequate, the output is -1. Such design of the artificial neural network is considered to be a key springboard along the way to develop artificial life, synthetic computer systems that can think in a certain capacity as human beings. Intelligent computer systems today Already employs neural networks that allow parallel data input processing faster than traditional linear computer programming.

For an example of a work system that depends on artificial neuron, the crop protection system developed in 2006, which used a flying vehicle to scan crops for the presence of seasonal diseases and pests. Ne neural network software has been selected to check crops because neural networks are basically computer learning. Since they bring more data under local conditions, they become more efficient in detecting problems to be able to control rapidly before spreading. The standard computer system, on the other hand, would treat the whole field of crops equally, regardless of different conditions in some sections. Without constant reprogramming, designers would prove much multi -fellow than a system based on artificial neural adaptations.

Software Neural Network also offers the advantage that it is adaptable engineers who are not familiar with the basic software design at the coding level.The software is able to be adapted to a wide range of conditions and gains expertise because it is exposed to these conditions and collects data about it. The neuron network will initially produce incorrect output as a problem solving, but when creating this output, it is fed back to the system as an input and constant process of refining and data weighing leads to an increasingly accurate understanding of the conditions of the real world, given sufficient time and feedback.

Adaptation in the designed neuron network has led to other types of artificial neuron in addition to the basic structure of binary neurons created in 1943. The semi -linear neural networks include linear and non -linear functions that are activated by conditions. If the analysis with analysis shows conditions that are not linear or are not clearly predictable and not smaller, then non -linear functions of the system are used by having more weight than linear calculations. As the nervous system training continues, the system becomes better when checking the SCE conditionsThe tangent world, monitors, compared to the ideal conditions of the system. This often includes the integration of neuro-fuzzy models into a neuron network that is able to take into account the degree of inaccuracy in creating meaningful output and control states.