On Analysis and Evaluation of Comparative Performance for Selected Behavioral Neural Learning Models versus One Bio-Inspired Non-Neural Clever Model (Neural Networks Approach)
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. M. H. , Tourkia, F. B. and Ramadan, R. M. (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, e2933. doi: http://dx.doi.org/10.4236/oalib.1102933.
Hassan,
H.M.
(2011) On
Mathematical Modeling of Cooperative E-Learning Performance during Face to Face
Tutoring Sessions (Ant Colony System Approach). IEEE Conference on Education Engineering-Learning
Environments and Ecosystems in Engineering Education,
Amman,
4-6 April 2011,
338-346.
Hassan, H.
and Watany, M.
(2000) On
Mathematical Analysis of Pavlovian Conditioning Learning Process Using
Artificial Neural Network Model. 10th
Mediterranean Electro Technical Conference, Cyprus, 29-31 May 2000.
Hassan, H.M.
and Watany, M.
(2003) On
Comparative Evaluation and Analogy for Pavlovian and Throndikian
Psycho-Learning Experimental Processes Using Bioinformatics Modeling. AUEJ, 6, 424-432.
Hassan,
H.M. and Al-Hamadi, A.
(2009) On Comparative Analogy between Ant Colony
Systems and Neural Networks Considering Behavioral Learning Performance. 4th Indian International Conference on
Artificial Intelligence (IICAI),
Tumkur,
16-18 December 2009.
Mustafa, H.M.H., et al. (2013) Comparative
Analogy of Neural Network Modeling versus Ant Colony System (Algorithmic and
Mathematical Approach). Proceeding
of International Conference on Digital Information Processing, E-Business
and Cloud Computing (DIPECC),
Dubai, 23-25 October 2013. http://sdiwc.net/conferences/2013/dipecc2013/
Hassan, H.M.
(2005)
On Principles of Biological Information Processing Concerned with Learning
Convergence Mechanism in Neural and Non-Neural Bio-Systems. International Conference on Computational
Intelligence for Modelling, Control
and Automation, Vienna,
28-30 November 2005, 647-653. http://dx.doi.org/10.1109/cimca.2005.1631542
Hassan, H.M. (2005) On Learning Performance
Evaluation for Some Psycho-Learning Experimental Work versus an Optimal Swarm
Intelligent System. International Symposium
on Signal Processing and Information Technology, Athens,
18-20 December 2005.
Ghonaimy, M.A., Al-Bassiouni, A.M. and Hassan,
H.M. (1994)
Leaning
of Neural Networks Using Noisy Data. 2nd International Conference on Artificial
Intelligence Applications, Cairo, 22-24 January 1994, 387-399.
Jilk, D.J., Cer, D.M. and O’Rilly, R.C. (2003)
Effectiveness of Neural Network Learning Rules Generated by a Biophysical Model
of Synaptic Plasticity. Technical Report, Department of Psychology, University
of Colorado, Boulder.
Fukaya,
M., et al. (1988) Two Level Neural
Networks: Learning by Interaction with Environment. 1st International Conference on
Neural Networks, San Diego, 24-27 July 1988.
Hassan, H.M. (2005) On
Mathematical Analysis, and Evaluation of Phonics Method for Teaching of Reading
Using Artificial Neural Network Models. International Conference on Management of Data and Symposium on Principles
Database and Systems, Baltimore, 17-19 January
2005,
254-262.
Hassan, M.H. (2008) A
Comparative Analogy of Quantified Learning Creativity in Humans versus
Behavioral Learning Performance in Animals: Cats, Dogs, Ants, and Rats. A
Conceptual Overview. Workshop and Summer School on Evolutionary
Computing,
Derry,
18-22 August 2008.
Kennedy, J.,
Kennedy, J.F., Eberhart,
R.C.
and
Shi, Y. (2001) Swarm
Intelligence (The Morgan Kaufmann Series in Evolutionary Computation). Morgan
Kaufmann, Burlington, 81-86.
Hassan,
M.H. (2005) On
Quantitative Mathematical Evaluation of Long Term Potentiation and Depression
Phenomena, Using Neural Network Modeling. International Conference on Simulation and Modeling, Bangkok, 17-19
January 2005, 237-241.
Hassan, H.M.
(2005) On
Learning Performance Evaluation for Some Psycho-Learning Experimental Work
versus an Optimal Swarm Intelligent System. International
Symposium on Signal Processing and Information Technology,
Athens,
18-20 December 2005.
Hassan, H.M.
(2008) On
Comparison between Swarm Intelligence Optimization and Behavioral Learning
Concepts Using Artificial Neural Networks (An Overview). 14th International Conference on Information Systems Analysis and
Synthesis (ISAS), Orlando, 29 June-2 July
2008.
