Before starting to develop a solution to a given problem, questions must be answered. What is the point of using a neural network for solving that specific problem? Why should a neural network be used? What are the benefits of using a network? The generic properties of different neural network types, and connectionist models in general, must be known to answer this question. What properties are going to be useful for solving the problem?
Problem identification also includes analysis of the existing problem knowledge. The problem knowledge may contain data and rules. If data are available, the independent variables (the input variables) should be clearly distinguished from the dependent variables (the output variables) in order to choose a proper neural network architecture and a learning method. These variables can be discrete, continuous, linguistic, boolean, etc. In some cases, only input variables are present. In this case, unsupervised learning algorithms and appropriate neural networks can be used to cluster, to conceptualize, and to recognize patterns in the domain data.
If rules are available in the problem knowledge, they can be implemented in an appropriate neural network structure. Especially suitable for connectionist implementation are fuzzy rules. If both rules and data are available, then hybrid systems, which incorporate both neural networks and symbolic AI techniques, can be used, or a neural network can be trained by using both data and rules, the latter being treated as hints or input-output associations.
