(20 marks)
5. Distinguish neural network expert systems from traditional expert systems. What are the main problems with the back-propagation training algorithm?
Neural Network expert systems:
Can learn and improve the inferencing and reasoning without human intervention.
A neural network can learn, but is a black box to the user. Combining a neural network with an expert system to solve a problem can give the best of both methods. Allowing some explaination of the reasoning, while enabling the system to learn and adapt. One way of doing this is in a rule based system, is to have a neural network generate and modify rules from the knowledge base and any new data.
Traditional expert systems:
Cannot learn from experience (in a standard rule based system, though case based reasoning expert system could expand it’s knowledge base). In a traditional expert system the inferencing and reasoning cannot be modified by the expert system itself. In a rule based system it is the entire knowledge base that is static.
A traditional expert system cannot learn, but it can fully explain the reasoning that lead a solution.
Problems with the back-propagation training algorithm:
Lots of calculations are required, thus training is slow and expensive.
Also to train a neural network with this method, we must know the expected output of the training input data so that we can calculate the error. Which isn’t always possible or feasible.