An accurate computation method based on artificial neural networks with different learning algorithms for resonant frequency of annular ring microstrip antennas

The article presents the application of artificial intelligence methods in the form of artificial neural networks (SSN) for modeling the geometrical state of a product shaped in the EDM process. The SSN with different architecture and different learning algorithms were implemented. The models' quality and their effectiveness in predicting some geometrical features of tool steel products were examined.

Download Full-text

Artificial Neural Networks and Human Brain: Survey of Improvement Possibilities of Learning

Environment Technology Resources Proceedings of the International Scientific and Practical Conference ◽

10.17770/etr2015vol3.165 ◽

2015 ◽

Vol 3 ◽

pp. 228 ◽

Cited By ~ 6

Author(s):

Aleksejs Zorins ◽

Peteris Grabusts

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Human Brain ◽

Network Topology ◽

Hybrid Methods ◽

Learning Algorithms ◽

Real Life ◽

Real Problem ◽

Brain Functions ◽

Artificial Neural

<p class="R-AbstractKeywords">There are numerous applications of Artificial Neural Networks (ANN) at the present time and there are different learning algorithms, topologies, hybrid methods etc. It is strongly believed that ANN is built using human brain’s functioning principles but still ANN is very primitive and tricky way for real problem solving. In the recent years modern neurophysiology advanced to a big extent in understanding human brain functions and structure, however, there is a lack of this knowledge application to real ANN learning algorithms. Each learning algorithm and each network topology should be carefully developed to solve more or less complex problem in real life. One may say that almost each serious application requires its own network topology, algorithm and data pre-processing. This article presents a survey of several ways to improve ANN learning possibilities according to human brain structure and functioning, especially one example of this concept – neuroplasticity – automatic adaptation of ANN topology to problem domain.</p>

Download Full-text

Calculation of Resonant Frequency for a Microstrip Antenna with Vertical Slots Using Applying Adaptive Network-Based Fuzzy Inference System

10.20944/preprints201710.0008.v1 ◽

2017 ◽

Author(s):

Mahmood Abbasi Layegh ◽

Changiz Ghobadi ◽

Javad Nourinia

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Resonant Frequency ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

High Approximation ◽

Adaptive Network ◽

Resonant Frequencies ◽

Inference System ◽

Artificial Neural

This paper attempts at applying adaptive network-based fuzzy inference system (ANFIS) for analysis of the resonant frequency of a microstrip rectangular patch antenna with two equal size slots which are placed on the patch vertically. The resonant frequency is calculated as the position of slots is shifted to the right and left sides on the patch. As a result , the antenna resonates at more than one frequency . Commonly, machine algorithms based on artificial neural networks are employed to recognize the whole resonant frequencies. However ,they fail to estimate the resonant frequencies correctly as in some cases variations are not very sensible and the resonant frequencies overlap each other . It can be concluded that artificial neural networks could be replaced in such designs by the adaptive network-based fuzzy Inference system due to its high approximation capability and much faster convergence rate.

Download Full-text

Supervised Learning with Artificial Neural Networks

Biologically Inspired Artificial Intelligence for Computer Games ◽

10.4018/978-1-59140-646-4.ch003 ◽

2008 ◽

pp. 24-40

Author(s):

Darryl Charles ◽

Colin Fyfe ◽

Daniel Livingstone ◽

Stephen McGlinchey

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Problem Solving ◽

Supervised Learning ◽

Learning Algorithms ◽

Theory And Practice ◽

Delta Rule ◽

Neural Learning ◽

Artificial Neural

In this chapter we will look at supervised learning in more detail, beginning with one of the simplest (and earliest) supervised neural learning algorithms – the Delta Rule. The objectives of this chapter are to provide a solid grounding in the theory and practice of problem solving with artificial neural networks – and an appreciation of some of the challenges and practicalities involved in their use.

Download Full-text