Episodic Reinforcement- Clustering technique (ERCT) based POPTVR and Modified POPTVRFNN for pattern classification
Journal Title: International Journal of Research in Computer and Communication Technology - Year 2013, Vol 2, Issue 8
Abstract
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 work, clustering algorithms are developed based on the reinforcement learning paradigm. This allows a more accurate description of the clusters as the clustering process is influenced by the reinforcement signal, The episodic Reinforce Clustering Technique we have implemented, the integrations of the ERCT within the pseudo-outer product truth value restriction (POPTVR), which is a Fuzzy neural network integrated with the truth restriction value (TVR) inference scheme in its five layered feed forward neural network. The episodic reinforcement-based clustering techniques applied to the POPTVR network are able to exhibit the trial-and-error search characteristic that yields higher qualitative performance.
Authors and Affiliations
Mohan Ajmeera, K. Sagar
Keywords
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