Reinforcement-Clustering Technique based on POPTVR FNN for Pattern Classification
A.Mohan1, V.Uday Kumar2, B.Sateesh3
1A.Mohan, Department of Computer Science and Engineering, Chaitanya Bharathi Institute of Technology, Hyderabad (Telangana), India.
2V.Uday Kumar, Department of Computer Science and Engineering, Chaitanya Bharathi Institute of Technology, Hyderabad (Telangana), India.
3B.Sateesh, Department of Computer Science and Engineering, Chaitanya Bharathi Institute of Technology, Hyderabad (Telangana), India.
Manuscript received on 8 August 2013 | Revised Manuscript received on 18 August 2013 | Manuscript Published on 30 August 2013 | PP: 114-118 | Volume-3 Issue-3, August 2013 | Retrieval Number: C1118083313/13©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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, three 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, They are the Reinforce Clustering Technique I (RCT-I), the Reinforce Clustering Technique II (RCT-II), and the Episodic Reinforce Clustering Technique (ERCT).we have implemented, the integrations of the RCT-I, the RCT-II, and 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 three reinforcement-based clustering techniques applied to the POPTVR network are able to exhibit the trial-and-error search characteristic that yields higher qualitative performance.
Keywords: Clustering, Fuzzy Neural Networks.
Scope of the Article: Classification