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On Analysis and Evaluation of Comparative Performance for Selected Behavioral Neural Learning Models versus One Bio-Inspired Non-Neural Clever Model (Neural Networks Approach)

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DOI: 10.4236/oalib.1102933    641 Downloads   911 Views Citations

ABSTRACT

This piece of research addresses an interesting comparative analytical study, which considers two concepts of diverse algorithmic computational intelligent paradigms related tightly with Neural and Non-Neural Systems’ modeling. The first computational paradigm was concerned with practically obtained psycho-learning behavioral results after three animals’ neural modeling. These are namely: Pavlov’s, and Thorndike’s experimental work. In addition, the third model is concerned with optimal solution of reconstruction problem reached by a mouse’s movement inside Figure 8 maze. Conversely, second algorithmic intelligent paradigm was originated from observed activities’ results after Non-Neural bio-inspired clever modeling namely Ant Colony System (ACS). These results were obtained after attaining optimal solution while solving Traveling Sales-man Problem (TSP). Interestingly, the effect of increasing number of agents (either neurons or ants) on learning performance was shown to be similar for both introduced systems. Finally, performances of both intelligent learning paradigms have been shown to be in agreement with learning convergence process searching for least mean square error LMS algorithm. While its application was for training some Artificial Neural Network (ANN) models. Accordingly, adopted ANN modeling is a relevant and realistic tool to investigate observations and analyze performance for both selected computational intelligence (biological behavioral learning) systems.

Cite this paper

Mustafa, H. , Tourkia, F. and Ramadan, R. (2016) On Analysis and Evaluation of Comparative Performance for Selected Behavioral Neural Learning Models versus One Bio-Inspired Non-Neural Clever Model (Neural Networks Approach). Open Access Library Journal, 3, 1-20. doi: 10.4236/oalib.1102933.

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