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Title: LEAST MEAN SQUARE ALGORITHM
Page Link: LEAST MEAN SQUARE ALGORITHM -
Posted By: projectsofme
Created at: Wednesday 24th of November 2010 07:13:27 PM
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LEAST MEAN SQUARE ALGORITHM



Introduction
The Least Mean Square (LMS) algorithm, introduced by Widrow and Hoff in 1959 is an adaptive algorithm, which uses a gradient-based method of steepest decent . LMS algorithm uses the estimates of the gradient vector from the available data. LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vector ....etc

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Title: An Affine Combination of two LMS adaptive filters transient mean square analysis
Page Link: An Affine Combination of two LMS adaptive filters transient mean square analysis -
Posted By: electronics seminars
Created at: Saturday 16th of January 2010 03:14:43 AM
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An Affine Combination of two LMS adaptive filters transient mean square analysis

Abstract:
The design of many adaptive filters requires a tradeoff between convergence speed and steady-state mean-square error (MSE). A faster (slower) convergence speed yields a larger (smaller) steady-state mean-square deviation (MSD) and MSE. This property is usually independent of the type of adaptive algorithm, i.e., least mean-square (LMS), normalized least
Mean-square (NLMS), recursive least squares (RLS), or affine projection (AP).
This design tradeo ....etc

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Title: LEAST MEAN SQUARE ALGORITHM
Page Link: LEAST MEAN SQUARE ALGORITHM -
Posted By: project report helper
Created at: Monday 01st of November 2010 08:20:35 PM
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LEAST MEAN SQUARE ALGORITHM

Introduction


The Least Mean Square (LMS) algorithm, introduced by Widrow and Hoff in 1959 is an adaptive algorithm, which uses a gradient-based method of steepest decent . LMS algorithm uses the estimates of the gradient vector from the available data. LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vector which eventually leads to the minimum mean square error. Com ....etc

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Title: Clustering a data set using Fuzzy K mean algorithm
Page Link: Clustering a data set using Fuzzy K mean algorithm -
Posted By:
Created at: Monday 25th of March 2013 03:22:09 PM
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Hi,

Can anybody help me with the matlab code of Clustering the dataset using fuzzy k mean algorithm?

Regards,
Gowri
Hi,

Can anybody help me with the matlab code of Clustering the dataset using fuzzy C mean algorithm?

Regards,
Gowri ....etc

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Title: adaptive fuzzy k mean clustering algorithm for image segmentation
Page Link: adaptive fuzzy k mean clustering algorithm for image segmentation -
Posted By:
Created at: Thursday 17th of January 2013 12:39:07 PM
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can you provide clear explanation for the formulas used in adaptive fuzzy k mean clustering algorithm for image segmentation... n how the calculations are done??? ....etc

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Title: What is a Project mean
Page Link: What is a Project mean -
Posted By: project topics
Created at: Thursday 28th of April 2011 08:34:34 PM
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What is a Project?

a project is development of new product or the launch of a new service or a marketing campaign . A project is not something that is a part of our normal business operations. It is typically created once, it is temporary, and it is specific. As one expert notes, It has a beginning and an end. A project consumes resources (whether people, cash, materials, or time), and it has funding limits.

Processes


Traditionally, project management includes a number of elements: four to five process groups, and a co ....etc

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Title: Implementation of Median and Mean filters using Bit planes for noise removal in image
Page Link: Implementation of Median and Mean filters using Bit planes for noise removal in image -
Posted By: seminar class
Created at: Monday 07th of March 2011 02:21:23 PM
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presented by:
K.PRATHYUSHA
T.RAJ KUMAR
D.PAPARAO
G.SHIRISHA


Bit plane
A Bit plane consists of the bits corresponding to the same significant level in all the elements
For example, the most significant bit plane is formed by considering the most significant bit(MSB) of each elements
The elements of a gray scale image have a maximum value of 255 and hence can be represented in binary domain using 8 bits
Higher order Bit planes of an image carry a significant amount of visually relevant deta ....etc

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Title: Jet impingement heat transfer Part I Mean and root-mean-square heat transfer
Page Link: Jet impingement heat transfer Part I Mean and root-mean-square heat transfer -
Posted By: seminar class
Created at: Thursday 05th of May 2011 05:26:40 PM
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Jet impingement heat transfer – Part I: Mean and root-mean-square heat transfer and velocity distributions
Abstract
Impinging jets provide a means of achieving high heat transfer coefficients both locally and on an area averaged basis. The currentwork forms the first stage of a two part investigation of heat transfer distributions from a heated flat surface subject to an impinging airjet for Reynolds numbers from 10,000 to 30,000 and non-dimensional surface to jet exit spacing, H/D, from 0.5 to 8. In the present paper,the rel ....etc

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Title: Grayscale Image Retrieval using DCT on Row mean Column mean and Combination
Page Link: Grayscale Image Retrieval using DCT on Row mean Column mean and Combination -
Posted By: computer girl
Created at: Wednesday 06th of June 2012 07:27:02 PM
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Grayscale Image Retrieval using DCT on Row mean,
Column mean and Combination




Abstract

Today in the age of information explosion, how to search appropriate data from huge information
pool has become vital issue. Images have giant share in this information pool. Because of easy
availability of imaging devices, millions of images are being added to image pool every day. Image
retrieval deals with searching relevant images from large image database. The paper presents novel
image retrieval techniques bas ....etc

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Title: Fuzzy Logic and Fuzzy Sets
Page Link: Fuzzy Logic and Fuzzy Sets -
Posted By: seminar addict
Created at: Saturday 28th of January 2012 06:57:22 PM
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Fuzzy Logic and Fuzzy Sets


History


Lotfi Zadeh, at the University of California at Berkeley, first presented fuzzy logic in the mid-1960's.

Zadeh developed fuzzy logic as a way of processing data. Instead of requiring a data element to be either a member or non-member of a set, he introduced the idea of partial set membership.

In 1974 Mamdani and Assilian used fuzzy logic to regulate a steam engine.

In 1985 researchers at Bell laboratories ....etc

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