neural based fault tolerance system using fpaa

7/28/2019 Neural Based Fault Tolerance System Using FPAA

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Neural Based Fault Tolerance

System Using FPAA


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Basic Concepts of FTS

Process resilience

Reliable client-server communication

Reliable group communication Distributed commit

Recovery


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Introduction

What is fault tolerance?

What are the ways in which systems can

fail?

What is the most basic way of achieving

fault tolerance?


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Dependability

Basics:A componentprovides services toclients. To provide services, the componentmay require the services from other componentsa component may depend on some other

component. Specifically:A component Cdepends on C*if

the correctness ofCs behavior depends on thecorrectness ofC*s behavior.

Some properties of dependability: Availability: Readiness for usage


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Reliability: Continuity of servicedelivery Safety: Very low probability of

catastrophes

Maintainability: How easy can afailed system be repaired

Note: For distributed systems,components can be either processes orchannels


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Neural Networks

Biological approach to AI.

Developed in 1943.

Comprised of one or more layers of neurons Several types, well focus on feed-forward

networks.

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Back Propagation Algorithm

Back-Propagation Algorithm:

function BACK-PROP-LEARNING(examples, network) returns a neural

network

inputs: examples, a set of examples, each with input vector x and output vector

y

network, a multilayer network with L layers, weights Wj,i , activation

function g

repeat

for each e in examples do

for each node j in the input layer do aj xj[e]for l = 2 to M do

inij Wj,i aj

aig(ini)

for each node i in the output layer do

Dj g(inj) i Wji Di

for l = M 1 to 1 dofor each node j in layer l do

Dj g(inj) i Wj,i Di

for each node i in layer l + 1 do

Wj,iWj,i + a x aj x Diuntil some stopping criterion is satisfied

return NEURAL-NET-HYPOTHESIS(network)

[Russell,] Fig. 20.25 Pg. 746


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Back-Propagation Illustration

ARTIFICIAL NEURAL NETWORKS Colin Fahey's Guide (Book CD)


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Training

Datasets from gaming sessions of humanvs. human are best

Must decide whether training will occurin-game, during development, or both

Learning during play provides foradaptations against individual players


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Training Patterns of Neural Network :


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Fuzzy Logic Controller

FUZZY SETS FOR LOAD AND SPEED

To choose membership functions, first of all oneneeds to consider the universe of distance for allthe linguistic variables, applied to the rules

formulation. To specify the universe of discourse, one must

firstly determine the applicable range for acharacteristic variable in the context of the

system designed. The range you select shouldbe carefully considered.


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Example :

For example, if you specify a range, which is too small,

regularly occurring data will be off the scale, that may impacton an overall system performance. Conversely, if the universefor the input is too large, a temptation will often be to havewide membership functions on the right or left to capture theextreme input values.

Because of this situation it is usually desirable and oftennecessary to scale or normalize the universe of discourse of aninput/output variable. Normalization means applying thestandard range of [1, +1] for the universe of discourse bothfor inputs and outputs. The universe of discourse for

percentage load, percentage speed and percentage controlvoltage is chosen as {0% to 100%). Error, change in error andcontrol voltage are taken as fuzzy variables and are assignedthe membership functions as shown in the following figure.


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NL NM NS AZ PS PM PL

-30 -20 -10 0 10 20 30Error


-3 -2 -1 0 1 2 3

Change in Error

Control voltage in %


-80 -55 -30 0 30 55 80

Fuzzy calculation:

In the membership

function plot

NL Negative LargeNM Negative Medium

NS Negative Small

AZ Absolutely Zero

PS Positive small

PM Positive Medium

PL Positive Large

Available in Knowledge base.


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cee


NL NL NM NS AZ PS PM PL

NM NL NL NL NM NS AZ PS

NS NL NL NM NS PS PS PM

AZ NL NL NS AZ PS PM PL

PS NM NS AZ PS PM PL PL

PM NS AZ PS PM PL PL PL

PL AZ PS PM PL PL PL PL

Rule-Base for the proposed FLC

The Fuzzy rule base relating the variables error, change in error and control

voltage is shown in Table below.


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FPAA and its Features

FPAA is Field Programmable Analog

Array.

Its architecture is shown in the next slide.

It is a dynamically reconfigurable device.

It is quickly accessible.


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Special features of FPAA


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In our project

Neural network is used as an Estimator.

FPAA is used as a reconfigurable device.

FLC is used as the controller.


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Block diagram of our project

neural based fault tolerance system using fpaa

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