What is intelligent system engineering?

Intelligent System Engineering (ISE) is a flat term used to designate various approaches to artificial intelligence (AI), including neural networks, evolutionary algorithms, prediction and control based on models, diagnostic systems based on cases, conventional control theory and symbolic AI. The term Intelligent system engineering is most often used in connection with AI applied to specific industrial challenges, such as optimizing a process sequence in a sugar factory. Intelligent system engineering tends to refer to the creation of a short -term, close task, trading AI, rather than long -term, flexible, generally intelligent AI.

There are university departments focused on intelligent system engineering in many countries. The terminology and general philosophy of ISE come from a mixture of mechanical and electrical engineering and computer science. ISE programs often exist within the engineering department.

Intelligent Systons are usually intendedY as robotics in industrial processes settings, although diagnostic systems can only be associated with passive sensors. Intelligent systems should be adaptive to solve problems with minimal human entry input. The field received considerable investments from private sectors and army.

Intelligent systems generally monitor the sequence of events in the diagnosis and solving the potential problem. First, the system identifies and defines the problem. It then identifies the evaluation criteria that relate to the situation used to create an alternative set to the problem.

There is an iterative search for solutions and evaluating potential solutions until selection and recommendations are selected. Then a human go-aead is sometimes required, the solution is implemented. Intelligent systems take some of the stress of people, automatically solve the simplest of many thousands of problems that appear inSetting the industrial process.

Intelligent system engineering seeks to create sensor networks that not only receive numerical values, but also act as virtual observers, integrate sensory data and create generalization. Given that our technological infrastructure becomes continuously more complex, many workers welcome artificial assistance in diagnosis and problem solving.