Comparison of rough multi layer perceptron and rough radial basis function networks using fuzzy attributes

Download

index.pdf

Date

2004

Author

Vural, Hülya

Metadata

Show full item record

Item Usage Stats

223
views

56
downloads

The hybridization of soft computing methods of Radial Basis Function (RBF) neural networks, Multi Layer Perceptron (MLP) neural networks with back-propagation learning, fuzzy sets and rough sets are studied in the scope of this thesis. Conventional MLP, conventional RBF, fuzzy MLP, fuzzy RBF, rough fuzzy MLP, and rough fuzzy RBF networks are compared. In the fuzzy neural networks implemented in this thesis, the input data and the desired outputs are given fuzzy membership values as the fuzzy properties أlowؤ, أmediumؤ and أhighؤ. In the rough fuzzy MLP, initial weights and near optimal number of hidden nodes are estimated using rough dependency rules. A rough fuzzy RBF structure similar to the rough fuzzy MLP is proposed. The rough fuzzy RBF was inspected whether dependencies like the ones in rough fuzzy MLP can be concluded.

Subject Keywords

Electronic computers.

URI

http://etd.lib.metu.edu.tr/upload/12605293/index.pdf
https://hdl.handle.net/11511/14466

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

New montgomery modular multıplier architecture Çiftçibaşı, Mehmet Emre; Yücel, Melek D; Department of Electrical and Electronics Engineering (2005) This thesis is the real time implementation of the new, unified field, dualا radix Montgomery modular multiplier architecture presented by Savaş et al, for performance comparison with standard Montgomery multiplication algorithms. The unified field architecture operates in both GF(p) and GF(2n). The dual radix capability enables processing of two bits of the multiplier in every clock cycle in GF(2n) mode, while one bit of the multiplier is processed in GF(p) mode. The new architecture is implemented in a Xi...
Deriving a dynamic programming algorithm for batch scheduling in the refinement calculus Aktuğ, İrem; Oğuztüzün, Mehmet Halit S.; Department of Computer Engineering (2003) Refinement Calculus is a formalization of stepwise program construction.In this approach a program is derived from its specification by applying refinement rules.The Refinement Calculator,developed at TUCS,Finland,provides tool support for the Refinement Calculus.This thesis presents a case study aiming to evaluate the applicability of the theory and the performance of the tool.The Refinement Calculator is used for deriving a dynamic progaramming algorithm for a single-machine batch scheduling problem.A qua...
Implementation of an 8-bit microcontroller with system c Kesen, Lokman; Aşkar, Murat; Department of Electrical and Electronics Engineering (2004) In this thesis, an 8-bit microcontroller, 8051 core, is implemented using SystemC programming language. SystemC is a new generation co-design language which is capable of both programming software and describing hardware parts of a complete system. The benefit of this design environment appears while developing a System-on-Chip (SoC), that is a system consisting both custom hardware parts and embedded software parts. SystemC is not a completely new language, but based on C++ with some additional class libra...
Monte Carlo analysis of ridged waveguides with transformation media Ozgun, Ozlem; Kuzuoğlu, Mustafa (Wiley, 2013-07-01) A computational model is presented for Monte Carlo simulation of waveguides with ridges, by combining the principles of transformation electromagnetics and the finite methods (such as finite element or finite difference methods). The principle idea is to place a transformation medium around the ridge structure, so that a single and easy-to-generate mesh can be used for each realization of the Monte Carlo simulation. Hence, this approach leads to less computational resources. The technique is validated by me...
A mathematical contribution of statistical learning and continuous optimization using infinite and semi-infinite programming to computational statistics Özöğür-Akyüz, Süreyya; Weber, Gerhard Wilhelm; Department of Scientific Computing (2009) A subfield of artificial intelligence, machine learning (ML), is concerned with the development of algorithms that allow computers to “learn”. ML is the process of training a system with large number of examples, extracting rules and finding patterns in order to make predictions on new data points (examples). The most common machine learning schemes are supervised, semi-supervised, unsupervised and reinforcement learning. These schemes apply to natural language processing, search engines, medical diagnosis,...

Citation Formats

H. Vural, “Comparison of rough multi layer perceptron and rough radial basis function networks using fuzzy attributes,” M.S. - Master of Science, Middle East Technical University, 2004.