Artificial Intelligent Systems and Machine Learning

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

An Overview of Artificial Neural Networks: Part 1


Affiliations
1 Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, Pune
2 Sinhgad College of Science, Pune, and Maharashtra, India
3 Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, Pune, India
4 Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, Pune, India
     

   Subscribe/Renew Journal


This paper presents the basic concepts of Artificial Neural Networks (ANNs) which helps to beginners of ANN. The need of soft computing, terms and the different trends related with Soft Computing (SC) is discussed in the introductory part. The architecture of ANN has been described with the help of structure of Biological Neural Network. The different thresholding techniques used for converting actual mathematical values into decision is also given. The concept of weight adjustment has been elaborated with the help of line fitting problem. The terms gradient and steepest descent have been explained with help of examples. Gradient descent algorithm is explained which helps to beginner of ANN with the help of MATLAB Code.  The results are discussed for line fitting. The parameters like Minimum Cost Function (CF), Iteration and Learning Rate are discussed. The final values of weights are in terms of slope and intercept are presented for line fitting.


Keywords

Artificial Neural Network, Gradient Descent, Line Fitting, Soft Computing, Steepest Descent.
User
Subscription Login to verify subscription
Notifications
Font Size

  • . R. B. Dhumale, S. D. Lokhande, “Neural Network Fault Diagnosis of Voltage Source Inverter under Variable Load Conditions at Different Frequencies”, Measurement, vol. 91, pp- 565–575, Sept. 2016.

  • . Jian Zhang, Chongyuan Tao, Pan Wang, “A Review of Soft Computing Based on Deep Learning”, IEEE International Conference on Industrial Informatics – Computing Technology, Intelligent Technology, Industrial Information Integration-2016 (ICIICII-2016), pp 136-144.

  • . R. B. Dhumale, S. D. Lokhande, “Comparative Study of Fault Diagnostic Methods in Voltage Source Inverter Fed Three Phase Induction Motor Drive”, IOP Conf. Series: Materials Science and Engineering, vol. 197, pp. 1-14, June, 2017

  • . Lofti Zadhe, “Fuzzy logic, neural networks, and soft computing”, Communications of the ACM, Mar. 1994, pp 77.

  • . Tharwat O. S. Hanafy, H. Zaini , Kamel A. Shoush, Ayman A. Aly, “Recent Trends in Soft Computing Techniques for Solving Real Time Engineering Problems”, International Journal of Control, Automation And Systems, vol.3, no.1, January 2014, pp 27-33.

  • . B.K. BOSE, “ARTIFICIAL NEURAL NETWORK APPLICATIONS IN POWER ELECTRONICS”, IEEE INDUSTRIAL ELECTRONICS SOCIETY, 2001. IECON '01, 06 AUGUST 2002.

  • . R. B. Dhumale, S. D. Lokhande, “Diagnosis of multiple open switch faults in three phase voltage source inverter”, Journal of Intelligent and Fuzzy Systems, vol. 30, iss. 4, pp- 2055-2065.

  • . S. MITRA ; S.K. PAL ; P. MITRA, “DATA MINING IN SOFT COMPUTING FRAMEWORK: A SURVEY”, IEEE TRANSACTION ON NEURAL NETWORK, VOL. 134, ISS. 1, JAN 2002, PP 3-14.

  • . R. B. Dhumale, S. D. Lokhande, “Modeling and Diagnosis of Open switch fault in three Phase VSI”, 2nd IEEE International Conference On Recent Trends in Electronics Information & Communication Technology (RTEICT), May 19-20, 2017, India

  • . C Küstü, KC Emregül, O Atakol, “Schiff bases of increasing complexity as mild steel corrosion inhibitors in 2 M HCl”, Corrosion science, Volume 49, Issue 7, July 2007, Pages 2800-2814

  • . Xiao Dang Zhong, “A Master-Slave Neural Network for Precise Diagnosis of Large Rotating Machinery”, Second International Conference on Innovative Computing, Information and Control, 2007. ICICIC '07.

  • . Ashraf Darwish, Aboul Ella Hassanien “Wearable and Implantable Wireless Sensor Network Solutions for Healthcare Monitoring”, Sensors, Vol. 11 (16), 2016..

  • . K. P. Valavanis, S. G. Tzafestas, “ Knowledge based (Expert) System in Engineering Applications: A Survey”, Journal of Intelligent and Robotic Systems, June 1994, Vol.10, Iss. 2, pp 113–145.

  • . G. P. Zhang,“Neural Network for Classification”, IEEE Transactions on Systems, Man, and Cybernetics, Part C Applications and Reviews, Vol. 30, Iss. 4, Nov 2000

  • . Amrita Sarkar, G.Sahoo, U.C.Sahoo, “ Application of Fuzzy Logic in Transport Planning” International Journal on Soft Computing (IJSC) Vol.3, No.2, May 2012.

  • . Jhon McCall ,“Genetic Algorithn for Modeling and Optimization”, Journal of Computational and Applied Mathematics, Volume 184, Issue 1, 1 December 2005, Pages 205-222


Abstract Views: 254

PDF Views: 1




  • An Overview of Artificial Neural Networks: Part 1

Abstract Views: 254  |  PDF Views: 1

Authors

R. B. Dhumale
Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, Pune
M. P. Ghatule
Sinhgad College of Science, Pune, and Maharashtra, India
N. D. Thombare
Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, Pune, India
P. M. Bangare
Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, Pune, India

Abstract


This paper presents the basic concepts of Artificial Neural Networks (ANNs) which helps to beginners of ANN. The need of soft computing, terms and the different trends related with Soft Computing (SC) is discussed in the introductory part. The architecture of ANN has been described with the help of structure of Biological Neural Network. The different thresholding techniques used for converting actual mathematical values into decision is also given. The concept of weight adjustment has been elaborated with the help of line fitting problem. The terms gradient and steepest descent have been explained with help of examples. Gradient descent algorithm is explained which helps to beginner of ANN with the help of MATLAB Code.  The results are discussed for line fitting. The parameters like Minimum Cost Function (CF), Iteration and Learning Rate are discussed. The final values of weights are in terms of slope and intercept are presented for line fitting.


Keywords


Artificial Neural Network, Gradient Descent, Line Fitting, Soft Computing, Steepest Descent.

References