Differences between Neural Networks and Expert Systems

Both neural networks and expert systems have been a huge help to most businesses. The two have similarities and differences. You can make decisions from them both. Differences are how they work, what they are, and how you get decisions from them.

Neural networks are a non-linear data statistical model. This is some light to how artificial neural networks are created. Just like any other network it is a system of functions that combine to make a network. First, the information is inputted for the network to analyze. Next, similarities are found from the information given. Once the similarities are made a decision is to be made from that information.

An expert system works by the user putting the information into the user interface. This this causes the system to go and try to seek out the appropriate data. The inference engine and the knowledge acquisition tool work together to find the information needed for the decision. Once the information is found it is analyzed and the put into knowledge base. That information is then turned around and put back onto the user interface so that the user can view the results of the systems find.

Neural networks do differ from expert systems in a number of ways. Neural networks use decision making through previous patterns and inputs and outputs. As far as expert systems go they use knowledge as an expert of a field would do to come up with their decision making. Also neural networks are non-linear. An example of an expert system would be IBM domains compared to the police tracking crimes in an area and looking to see where more police presence should be based on crime patterns, which is an example of neural networks.