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Fusion: Practice and Applications
Volume 6 , Issue 2, PP: 43-49 , 2021 | Cite this article as | XML |PDF

Title

A Hybrid Approach for Neural Network in Pattern Storage

Authors Names :   Kumud Sachdeva   1 *     Shruti Aggarwal   2  

1  Affiliation :  Department of Computer Science and Engineering, Chandigarh University, Gharuan, Punjab, 140413, India

    Email :  kumud.cse@cumail.in


2  Affiliation :  Department of Computer Science and Engineering, Chandigarh University, Gharuan, Punjab, 140413, India

    Email :  drshruti.cse@gmail.com



Doi   :   https://doi.org/10.54216/FPA.060201


Abstract :

Your mind does not manufacture your mind. Your mind forms neural networks. Neural networks had been effectively carried out to numerous sample garage and type troubles in phrases in their mastering ability, excessive discrimination electricity, and exceptional generalization ability. The achievement of many mastering schemes isn't always assured, however, seeing that algorithms like backpropagation have many drawbacks like stepping into the nearby minima, for that reason imparting suboptimal solutions. In the case of classifying big sets and complicated patterns, the traditional neural networks are afflicted by numerous problems inclusive of the dedication of the shape and length of the network, the computational complexity, and so on. This paper introduces neural computing techniques especially radial foundation features network. Various upgrades and trends made in an artificial neural network for rushing up training, keeping off nighborhood minima, growing the generalization capacity and different capabilities are reviewed.

Keywords :

Neural Network; Artificial Neural Network; Artificial Intelligence; Hopfield Network; Radial Basis Function.

References :

[1]     S. Aggarwal and P. Singh, “Cuckoo, Bat and Krill Herd based k-means++ clustering algorithms,” Cluster Comput., Mar. 2018, doi: 10.1007/s10586-018-2262-4.

 

[2]     T. K.-I. transactions on computers and  undefined 1972, “Correlation matrix memories,” ieeexplore.ieee.org, no. 4, 1972, Accessed: Sep. 14, 2021. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/5008975/.

 

[3]     P. Singh and S. Aggarwal, “Software Fault Prediction Using Hybrid Swarm Intelligent Cuckoo and Bat based k-means++ Clustering Technique,” Int. J. Adv. Intell. Paradig., vol. 20, no. 1/2, p. 1, 2021, doi: 10.1504/IJAIP.2021.10016288.

 

[4]     S. Aggarwal and P. Singh, “Cuckoo and krill herd-based k-means++ hybrid algorithms for clustering,” Expert Syst., vol. 36, no. 4, p. e12353, Aug. 2019, doi: 10.1111/EXSY.12353.

 

[5]     J. H.-P. of the national academy of and  undefined 1982, “Neural networks and physical systems with emergent collective computational abilities,” Natl. Acad Sci., vol. 79, pp. 2554–2558, 1982, Accessed: Sep. 14, 2021. [Online]. Available: https://www.pnas.org/content/79/8/2554.short.

 

[6]     W. McCulloch, W. P.-T. bulletin of mathematical biophysics, and  undefined 1943, “A logical calculus of the ideas immanent in nervous activity,” Springer, Accessed: Sep. 14, 2021. [Online]. Available: https://link.springer.com/article/10.1007%252FBF02478259.

 

[7]     J. J. Hopfield, “Neurons with graded response have collective computational properties like those of two-state neurons,” Proc. Natl. Acad. Sci., vol. 81, no. 10, pp. 3088–3092, May 1984, doi: 10.1073/PNAS.81.10.3088.

 

[8]     K. Sachdeva and A. Girdhar, “A Technique for Glass Defect Detection,” Int. J. Innov. Res. Dev., vol. 2, no. 13, pp. 92–96, 2013.

 

[9]     M. Cohen, S. G.-I. transactions on systems,  undefined man, and  undefined 1983, “Absolute stability of global pattern formation and parallel memory storage by competitive neural networks,” ieeexplore.ieee.org, Accessed: Sep. 15, 2021. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/6313075/.

 

[10]    “Neuronlike adaptive elements that can solve difficult learning control problems,” ieeexplore.ieee.org, Accessed: Sep. 15, 2021. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/6313077/.

 

[11]    M. Aizerman, … E. B.-A. and R., and  undefined 1964, “Theoretical foundations of potential function method in pattern recognition,” mathnet.ru, Accessed: Sep. 15, 2021. [Online]. Available: http://www.mathnet.ru/eng/at11677.

 

[12]    B. Schölkopf, P. Simard, A. Smola, V. V.-A. in neural, and  undefined 1998, “Prior knowledge in support vector kernels,” Citeseer, Accessed: Sep. 15, 2021. [Online]. Available: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.9.5442&rep=rep1&type=pdf.

 

[13]    “Hopfield 1.1., 1982. "Neural networks and physical... - Google Scholar.” https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Hopfield+1.1.%2C+1982.+%22Neural+networks+and+physical+systems+with+emergent+collective+computational+abilities%22%2C+Proc.+Nat.+Acad.+Sci%2C+USA%2C+Vol.79%2C+pp.2554-2558%2C+in+Neurocomputing%3A+Foundations+ofResearch%2C+Anderson+and+Rosenfeld+%28Eds.%29%2C+MIT+Press%2C+1988%2C+pp.457-464.&btnG= (accessed Sep. 14, 2021).

 

[15]    G. Zhang et al., “Reinforced concrete deep beam shear strength capacity modelling using an integrative bio-inspired algorithm with an artificial intelligence model,” Eng. with Comput. 2020, pp. 1–14, Aug. 2020, doi: 10.1007/S00366-020-01137-1.

 [16]     M. Kumar, P. Mukherjee, K. Verma, S. Verma and D. B. Rawat, "Improved Deep Convolutional Neural Network based Malicious Node Detection and Energy-Efficient Data Transmission in Wireless Sensor Networks," in IEEE Transactions on Network Science and Engineering, doi: 10.1109/TNSE.2021.3098011.

[17]     P. Rani, Kavita, S. Verma and G. N. Nguyen, "Mitigation of Black Hole and Gray Hole Attack Using Swarm Inspired Algorithm with Artificial Neural Network," in IEEE Access, vol. 8, pp. 121755-121764, 2020, doi: 10.1109/ACCESS.2020.3004692.

[18]       Loveleen Gaur, Gurmeet Singh, Arun Solanki, Noor Zaman Jhanjhi, Ujwal Bhatia, Shavneet Sharma, Sahil Verma, Kavita, Nataša Petrović, Muhammad Fazal Ijaz, and Wonjoon Kim, Disposition of Youth in Predicting Sustainable Development Goals Using the Neuro-fuzzy and Random Forest Algorithms, Article number: 11:24 (2021)

[19]    Monica Sood, et.al.“Optimal Path Planning using Swarm Intelligence based Hybrid Techniques” Journal of computational and theoretical nanoscience (JCTN), ASPBS publisher. Vol. 16 No. 9, 2019, pp. 3717–3727, DOI:10.1166/jctn.2019.8240.

[20]    Kaur Manjit; et al. “Flying Ad-Hoc Network (FANET): Challenges and Routing Protocols” Journal of Computational and Theoretical Nanoscience, Volume 17, Number 6, June 2020, pp. 2575-2581(7), https://doi.org/10.1166/jctn.2020.8932

[21]    Ghosh, Gopal; et al. ‘Internet of Things based video surveillance systems for security applications’ Journal of Computational and Theoretical Nanoscience, Volume 17, Number 6, June 2020, pp. 2582-2588(7) https://doi.org/10.1166/jctn.2020.8933

[22]    Gopal Ghosh, et al. ‘A Systematic Review on Image Encryption Techniques’ Turkish Journal of Computer and Mathematics Education, Vol.12 No.10 (2021), 3055-3059 M. Balazinska et al., “Data management in the worldwide sensor web,” IEEE Pervasive Comput., vol. 6, no. 2, pp. 30–40, 2007, doi: 10.1109/MPRV.2007.27.

[23]     A. Hussain et al., "A Resource Efficient hybrid Proxy Mobile IPv6 extension for Next Generation IoT Networks," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2021.3058982.

 

 

 

 


Cite this Article as :
Kumud Sachdeva , Shruti Aggarwal, A Hybrid Approach for Neural Network in Pattern Storage, Fusion: Practice and Applications, Vol. 6 , No. 2 , (2021) : 43-49 (Doi   :  https://doi.org/10.54216/FPA.060201)