Businesses around the world utilize different kinds of systems in order to help them direct their company and gain competitive advantage over their competition. These businesses use two different kind of systems to help them which are expert systems and neural networks. Both of these systems help solve problems but they work in entirely different ways.
The first characteristic that differs between them is the way that they process information. Expert systems use sequential processing by going through the data one line or rule at a time. It basically goes through the process logically using rule concepts to guide it to its answer. It is best used for questions that involve calculations such as balancing checkbooks and inventory management. Artificial neural networks process their data in a parallel environment. This means that it can do more than one thing at a time while trying to come up with the best solution. It can also process information such as images and pictures which expert systems can not process.
Another characteristic in which they differ is how they learn in order to have the knowledge to solve problems. Expert systems learn by being fed rules didactically. The system uses this knowledge base in order to know what path it should take when certain questions are answered the way that they are. They also learn from accounting, word processing, math inventory and digital communication application. Neural networks learn by example and interpretation. Some of the ways that they learn include sensor processing, speech recognition, pattern recognition and text recognition.
There also also some smaller characteristics that make them different as well. Expert systems use traditional processors while neural networks use artificial neural networks to process information. This includes a variety of technologies and hardware. The processing approach of an expert system is separate and can only process one rule at a time while neural networks’ processing approach is the same or simultaneously. Expert systems are externally programmed while neural networks are dynamically self programming meaning it teaches itself what it needs to know. An expert system can only learn algorithmic parameters which neural networks are continuously adaptable. Another difference is that expert systems so not have any fault tolerances without special processors but neural networks have significant fault tolerances that falls within the very nature of its interconnected neurons. Expert systems are also not based in neurobiology while neural networks are moderately based on that field. Neural networks mimic the entire biological environment and are similar to our nervous systems. Expert systems mimic human decision making only. Finally their ability to be fast differs as well. Expert systems require big processors in order to be fast. Neural networks require multiple custom built chips that compliment its learning ability to be fast.
The similarities between them are basically that they can help businesses solve problems and make decisions. Some of the systems are geared more toward certain problem solving skills but they both help the company gain competitive advantage. They both have their faults too. Businesses need to make sure that they choose the right systems for their situation and feed it the right information of program it correctly to get the desired results.