Radial basis neural network optimization using fruit fly

Anurag Rana (Autor)

Buch | Softcover

100 Seiten

2014 | 14001 A. 1. Auflage
GRIN Verlag
978-3-656-67872-4 (ISBN)

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Master's Thesis from the year 2014 in the subject Computer Sciences - Artificial Intelligence, grade: A, , course: Master Of Technology Computer Science and Engineering, language: English, abstract: This research presents the optimization of radial basis function (RBF) neural network by means of aFOA and establishment of network model, adopting it with the combination of the evaluation of the mean impact value (MIV) to select variables. The form of amended fruit fly optimization algorithm (aFOA) is easy to learn and has the characteristics of quick convergence and not readily dropping into local optimum. The validity of model is tested by two actual examples, furthermore, it is simpler to learn, more stable and practical.
Our aim is to find a variable function based on such a large number of experimental data in many scientific experiments such as Near Infrared Spectral data and Atlas data. But this kind of function is often highly uncertain, nonlinear dynamic model. When we perform on the data regression analysis, this requires choosing appropriate independent variables to establish the independent variables on the dependent variables regression model. Generally, experiments often get more variables, some variables affecting the results may be smaller or no influence at all, even some variable acquisition need to pay a large cost. If drawing unimportant variables into model, we can reduce the precision of the model, but cannot reach the ideal result. At the same time, a large number of variables may also exist in multicollinearity. Therefore, the independent variable screening before modeling is very necessary. Because the fruit fly optimization algorithm has concise form, is easy to learn, and have fault tolerant ability, besides algorithm realizes time shorter, and the iterative optimization is difficult to fall into the local extreme value. And radiate basis function (RBF) neural network's structure is simple, training concise and fasting speed of convergence by learning, can approximate any nonlinear function, having a "local perception field" reputation. For this reason, this paper puts forward a method of making use of the amended fruit flies optimization algorithm to optimize RBF neural network (aFOA-RBF algorithm) using for variable selection.

Anurag Rana
M. Tech. CSE, MCA, BCA
Scored 68 percentile in C-DAC exam organized by C-DAC, Mumbai in January 2009

Research and Development

1.Scope and Deployment Strategies of E-Governance in India: A Survey.
2.Computer: A communication Device
3.Resolving Set-Streaming Stream-Shop Scheduling in Distributed System by mean of an aFOA
4.Parallel Neural Nets Using Pattern- Partitioning for Process Distribution by Means of PVM
5.NETWORK NEUTRALITY: Developing Business Model and Evidence Based net neutrality Regulation
6.OPTIMIZATION OF RADIAL BASIS NEURAL NETWORK BY MEAN OF AMENDED FRUIT FLY OPTIMIZATION ALGORITHM
7.Rain: Graphical Rendering
8.Processing Cost Optimization for Tasks Allocation in Distributed System
9.Mobile Ad-Hoc Clustering Using Inclusive Particle Swarm Optimization Algorithm

Erscheint lt. Verlag	26.6.2014
Reihe/Serie	Akademische Schriftenreihe
Sprache	englisch
Maße	148 x 210 mm
Gewicht	150 g
Themenwelt	Mathematik / Informatik ► Informatik ► Netzwerke
Schlagworte	radial
ISBN-10	3-656-67872-3 / 3656678723
ISBN-13	978-3-656-67872-4 / 9783656678724
Zustand	Neuware