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Artificial neurons attempt to emulate biological neurons in the human brain. They function by combining inputs and producing an output. Neural networks are used in crop protection systems and can adapt to varying conditions, becoming more efficient over time. Different types of artificial neurons have been developed, including semilinear neural networks and neuro-fuzzy models.
An artificial neuron is a mathematical function in computer system software programming that attempts to some extent to emulate the complex interaction of biological neurons, or impulse-conducting 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 input could be a value of 1 or -1. A combination of these inputs are weighted together. If a certain threshold is exceeded, the output of the artificial neuron is 1, and if the inputs are insufficient when combined, the output is a value of -1.
Together, a set of interconnected artificial neurons should function in a basic way like the human brain does. Such artificial neural network design is seen as a key stepping stone along the path to developing artificial life, synthetic computer systems that can reason somewhat like humans do. Intelligent computer systems today already employ neural networks that enable parallel processing of data input faster than traditional linear computer programming.
An example of a functioning system that depends on the artificial neuron is a crop protection system developed in 2006, which used a flying vehicle to scan crop conditions for seasonal diseases and pests. Neural network software was chosen to control crop scanning, as neural networks are essentially learning computers. As more data is entered based on local conditions, they become more efficient at detecting problems so they can be controlled quickly before they spread. A standard computerized system, on the other hand, would have treated the entire field of crops equally, regardless of the varying conditions in certain sections. Without constant reprogramming by the designers, it would have proved much more inefficient than a system based on artificial neural adaptations.
Neural network software also offers the advantage of being adaptable by engineers who are not familiar with the basic software design at the coding level. Software is capable of being adapted to a wide range of conditions and gains proficiency as it is exposed to those conditions and collects data about them. Initially a neural network will produce incorrect output as solutions to problems, but, as this output is produced, it is fed back into the system as input and a continuous process of refining and weighing the data leads it to an increasingly accurate understanding of the real conditions of the world, given enough time and feedback.
Adaptation in the way a neural network is designed has led to other types of artificial neurons beyond the basic binary neuronal structure created in 1943. Semilinear neural networks incorporate both linear and nonlinear functions that are triggered by conditions. If the analyzed problem shows non-linear conditions, or conditions that are not clearly foreseeable and not minor, the non-linear functions of the system are used giving more weight to the linear calculations. As the training of the neural system continues, the system gets better at checking the real-world conditions it is monitoring against what the system’s ideal conditions should be. This often involves incorporating neuro-fuzzy models into the neural network, which are capable of accounting for degrees of imprecision in producing meaningful output and control states.
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