A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Bangare, P. M.
- An Overview of Artificial Neural Networks: Part 1
Authors
1 Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, Pune
2 Sinhgad College of Science, Pune, and Maharashtra, IN
3 Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, Pune, IN
4 Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, Pune, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 10, No 2 (2018), Pagination: 47-52Abstract
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
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- An Overview of Artificial Neural Networks:Part 2 McCulloch-Pitts Model
Authors
1 Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, Pune, IN
2 Department of Information Technology, SKN College of Engineering, Pune, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 10, No 3 (2018), Pagination: 53-58Abstract
This paper presents the basic concepts of Artificial Neural Networks (ANNs) which helps to beginner of ANN. The history of ANN, different applications of ANN, different learning rules and different types of architectures are discussed in the introductory part of this paper. The architecture of ANN has been described in detail. The McCulloch Pitts model is explained with mathematical equations. The McCulloch Pitts model is implemented for AND, OR, NOT and XOR gate. The algorithms for implementation of above gates have been given. To help users MATLAB code is also given in this paper.
Keywords
Artificial Neural Network, History of ANN, Learning Rules, Logic Gates.- Genetic Algorithm:A Search-Based Optimization Technique
Authors
1 Department of Electronics and Telecommunication Engineering, Sinhgad College of Engineering, Pune, IN
2 Department of Information Technology, SKN College of Engineering, Pune, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 10, No 5 (2018), Pagination: 109-114Abstract
Nature has been an unlimited source of motivation to all manhood. Current activities in Soft Computing is close the progress of technologies which have source and correspondence with biological phenomenon linked with human as evolutionary computation. Soft Computing is combination of several methods as Artificial Neural Network, Fuzzy Logic and Genetic Algorithm. This paper focuses on the search based optimization technique i.e. Genetic Algorithm. Optimization is the scheme of building a something best. The biological concepts of Genetic Algorithm are discussed. Steps required for implementing Genetic Algorithm i.e. Initialization, Encoding, Genetic Operators, Mutation and Termination are described. The traveling Salesman Problem is well-known problem of search based optimization. This problem is considered for discussion. The results are discussed for different number of cities to be travelled with minimum cost function.
Keywords
Genetic Algorithm, Evolutionary Computation, Optimization, Travelling Salesman Problem.References
- R. B. Dhumale, M. P. Ghatule, N. D. Thombare, P. M. Bangare, “An Overview of Artificial Neural Networks: Part 1”, Ciit International Journal of Artificial Intelligent Systems and Machine Learning, Feb 2018, Vol. 10, No. 3.
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- R. B. Dhumale, M. P. Ghatule, N. D. Thombare, P. M. Bangare, “An Overview of Artificial Neural Networks: Part 2”, Ciit International Journal of Artificial Intelligent Systems and Machine Learning, Feb 2018, Vol. 10, No. 3. (Accepted)
- R. B. Dhumale, M. P. Ghatule, N. D. Thombare, P. M. Bangare, “An Overview of Artificial Neural Networks: Part 3”, Ciit International Journal of Artificial Intelligent Systems and Machine Learning, Feb 2018, Vol. 10, No. 3. (Accepted)
- R. B. Dhumale, M. P. Ghatule, N. D. Thombare, P. M. Bangare, “An Overview of Artificial Neural Networks: Part 4”, Ciit International Journal of Artificial Intelligent Systems and Machine Learning, Feb 2018, Vol. 10, No. 3. (Accepted)
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