What is computational intelligence?
Computational Intelligence (CI) is an computer science industry in which projects develop from below, the order based on the initial lack of structure. It is similar to many processes observed in the natural world. Computer intelligence includes concepts such as evolutionary calculation, where problems are solved using models of the evolutionary process and when applied to machine learning, it allows robots to learn from experience. Fuzzy logic, a system that resembles human decision -making, can be used to solve problems where there is vague or uncertainty. Neuron networks are systems based on the function of the human brain and can be used to detect formulas and trends in complex data. Computer intelligence often takes inspiration of nature, for example in the area of evolutionary calculation, where systems that develop to solve complex problems are formed. This can be used on artificial or synthetic intelligence, leading to robots that learn from experience and develop over time.
Fuzzy logic -based systems can be used in computing intelligence to simulate human ways of thinking. They could be combined with biologically inspired neural networks in the field of cognitive robotics and create robots with the ability to think in a way that resembles the processes of human thinking. Like thinking, such robots can also learn, remember, perceive and decide face to face uncertain as people do. This could allow robots to better understand human requirements, allowing them to detect the meaning behind the words used. This could be necessary for a machine that performs homework.neuron nets are usually considered part of computational intelligence. Like the human brain, they consist of numerous interconnected individual parts, similar to nerves. These work together to solve problems, learning as soon as they go because the connection between elements is adjustable, such as the connection between the nerveyou.
As soon as neural networks learn how to analyze data, they can effectively become experts in their fields and can be used to predict results in different scenarios. The disadvantage of this type of computational intelligence is that it requires a lot of computing performance and can work in an unpredictable way. Neuron networks should not be confused with professional systems that use predetermined sets of decision -making rules and do not modify them to match the data.