Reinforcement-Clustering Technique Based on POPTVR FNN for Pattern Classification
In general, a Fuzzy Neural Network (FNN) is characterized by its learning algorithm and its linguistic knowledge representation. However, it does not necessarily interact with its environment when the training data is assumed to be an accurate description of the environment under consideration. In interactive problems, it would be more appropriate for an agent to learn from its own experience through interactions with the environment, i.e. reinforcement learning. In this paper, three clustering algorithms are developed based on the reinforcement learning paradigm.