slide 1 the kalman filter - and other methods anders ringgaard kristensen

Slide 1

The Kalman filter- and other methodsAnders Ringgaard Kristensen

Slide 2

Outline

Filtering techniques applied to monitoring of daily gain in slaughter pigs:• Introduction• Basic monitoring• Shewart control charts• DLM and the Kalman filter

• Simple case• Seasonality

• Online monitoring• Used as input to decision support

Slide 3

”E-kontrol”, slaughter pigs

Quarterly calculated production resultsPresented as a tableA result for each of the most recent quarters and aggregatedSometimes comparison with expected (target) valuesOffered by two companies:

• Dansk Landbrugsrådgivning, Landscentret (as shown)

• AgroSoft A/S

One of the most important key figures: Average daily gain

Slide 4

Average daily gain, slaughter pigs

We have:• 4 quarterly results• 1 annual result• 1 target value

How do we interpret the results?Question 1: How is the figure calculated?

Slide 5

How is the figure calculated?

The basic principles are:• Total (live) weight of pigs delivered: xxxx• Total weight of piglets inserted: −xxxx• Valuation weight at end of the quarter: +xxxx• Valuation weight at beginning of the quarter: −xxxx• Total gain during the quarter yyyy

Daily gain = (Total gain)/(Days in feed)Registration sources?

• * Slaughter house – rather precise• ** Scale – very precise• *** ??? – anything from very precise to very uncertain

*********

Slide 6

First finding: Observation error

All measurements are encumbered with uncertainty (error), but it is most prevalent for the valuation weights.

We define a (very simple) model: = + eo , where:

• is the calculated daily gain (as it appears in the report)• is the true daily gain (which we wish to estimate)

• eo is the observation error which we assume is normally distributed N(0, o2)

The structure of the model (qualitative knowledge) is the equation

The parameters (quantitative knowledge) is the value of o (the standard deviation of the observation error). It depends on the observation method.

Slide 7

Observation error

= + eo , eo » N(0, o2)

What we measure is What we wish to know is The difference between

the two variables is undesired noise

We wish to filter the noise away, i.e. we wish to estimate from

Slide 8

Second finding: Randomness

The true daily gains vary at random.Even if we produce under exactly the same conditions in two

successive quarters the results will differ. We shall denote the phenomenon as the “sample error”.

We have, = + es, where• es is the sample error expressing random variation. We assume es » N(0,

s2)

• is the underlying permanent (and true) value

This supplementary qualitative knowledge should be reflected in the stucture of the model: = + e

o = + e

s + e

o

The parameters of the model are now: s og o

Slide 9

Sample error and measurement error

What we measure is What we wish to know is The difference between the

two variables is undesired noise:

• Sample noise• Observation noise

We wish to filter the noise away, i.e. we wish to estimate from

Slide 10

The model is necessary for any meaningful interpretation of calculated production results.

The standard deviation on the sample error, s , depends on the natural

individual variation between pigs in a herd and the herd size. The standard deviation of the observation error, o , depends on the

measurement method of valuation weights.For the interpretation of the calculated results, it is the total uncertainty, , that

matters (2 = s2 +

2) Competent guesses of the value of using different observation methods (1250

pigs):• Weighing of all pigs: 3 g• Stratified sample: 7 g• Random sample: 20 g• Visual assessment: 29 g

The model in practice: Preconditions

Slide 11

Different observation methods

= 3 g = 7 g = 20 g = 29 g

Slide 12

The model in practice: Interpretation

Calculated daily gain in a herd was 750 g, whereas the expected target value was 775 g.

Shall we be worried?It depends on the observation method! A lower control limit (LCL) is the target minus 2

times the standard deviation, i.e. 775 – 2Using each of the 4 observation methods, we

obtain the following LCLs:• Weighing of all pigs: 775 g – 2 x3 g = 769 • Stratified sample: 775 g – 2 x7 g = 761• Random sample: 775 g – 2 x 20 g = 735 • Visual assessment: 775 g – 2 x 29 g = 717

Slide 13

Third finding: Dynamics, time

Daily gain, slaughter pigs

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Daily gain in a herd over 4 years. Is this good or bad?

Slide 14

Modeling dynamics

We extend our model to include time.At time n we model the calculated result as follows:n = sn + eon = + esn + eon

Only change from before is that we know we have a new result each quarter.

We can calculate control limits for each quarter and plot everything in a diagram: A Shewart Control Chart …

1

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Slide 15

A simple Shewart control chart: Weighing all pigs

Daily gain, slaughter pigs

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Observed gain Expected

Upper control limit Lower control limit

Periode

Slide 16

Daily gain, slaughter pigs

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Simple Shewart control chart: Visual assessment

Periode

Slide 17

Interpretation: Conclusion

Something is wrong!Possible explanations:

• The pig farmer has serious problems with fluctuating daily gains.• Something is wrong with the model:

• Structure – our qualitative knowledge• Parameters – the quantitative knowledge (standard

deviations).

Slide 18

More findings: n = + esn + eon

The true underlying daily gain in the herd, , may change over time:

• Trend• Seasonal variation

The sample error esn may be auto correlated • Temporary influences

The observation error eon is obviously auto correlated:

• Valuation weight at the end of Quarter n is the same as the valuation weight at the start of Quarter n+1

Slide 19

”Dynamisk e-kontrol”

Developed and described by Madsen & Ruby (2000).Principles:

• Avoid labor intensive valuation weighing.• Calculate new daily gain every time pigs have been sent to

slaughter (typically weekly)• Use a simple Dynamic Linear Model to monitor daily gain

• n = n + esn + eon = n + vn , where vn » N(0, v2)

• n = n-1 + wn, where wn » N(0, w2)

• The calculated results are filtered by the Kalman filter in order to remove random noise (sample error + observation error)

Slide 20

”Dynamisk E-kontrol”, results

Raw data to the left – filtered data to the rightFigures from:

• Madsen & Ruby (2000). An application for early detection of growth rate changes in the slaughter pig production unit. Computers and Electronics in Agriculture 25, 261-270.

Still: Results only available after slaughter

Slide 21

The Dynamic Linear Model (DLM)

ExampleObservation equation

n = n + vn , vn » N(0, v

2)

System equation n = n-1 + wn, wn » N(0, w

2)

General, first orderObservation equation

Yt = t + vt , vn » N(0, v2)

System equation t = t-1 + wn, wn » N(0,w

2)

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Slide 22

Extending the model Fnn is the true level described as a vector product.A general level, 0n, and 4 seasonal effects 1n, 2n, 3n and 4n are included in the model.From the model we are able to predict the expected daily gain for next quarter. As long as the forecast errors are small, production is in control (no large change in true underlying level)!

Slide 23

Observed and predicted

Daily gain

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Blue: ObservedPink: Predicted

Slide 24

Analysis of prediction errors

Daily gain

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Slide 25

The last model

Dynamic Linear ModelStructure of the model (qualitative knowledge):

• Seasonal variation allowed (no assumption about the size). • The general level as well as the seasonal pattern may change over time.

Are those assumptions correct?Parameters of the model:

• The observation and sample variance and the system variance.

The model learns as observations are done, and adapts to the observations over time.

Seasonal varation may be modeled more sophistically as demonstrated by Thomas Nejsum Madsen in FarmWatch™

Slide 26

Moral

If we wish to analyze the daily gain of a herd you need to:• Know exactly how the observations are done (and know

the precision).• Know how it may naturally develop over time.

Without professional knowledge you may conclude anything.Without a model you may interpret the results inadequately.Through the structure of the model we apply our professional

knowledge to the problem.

Slide 27

On-line monitoring of slaughter pigs: PigVision

Innovation project led by Danish Pig Production:• Danish Institute of Agricultural Sciences• Videometer (external assistance)• Skov A/S• LIFE, IPH, Production and Health

Continuous monitoring of daily gain while still in herd:

• Dynamic Linear Models• Chance of interference in the fattening period• Adaptation of delivery policy

Slide 28

PigVision: Principles

A camera is placed above the pen. In case of movements a series of pictures are recorded and sent to a computer.The computer automatically identifies the pig (by use of a model) and calculates the area (seen from above). If the computer doesn’t belief that a pig has been identified, the picture is ignored. The area is converted to live weight (using a model).Through many pictures, the average weight and the standard deviation are estimated.

Figure by Teresia Heiskanen

Slide 29

What is online weight assessment used for?

Continuous monitoring of gain.Collection of evidence about growth capacity

(learning)Adaptation of delivery policies depending on:

• Whether the pigs grow fast or slowly • Whether the uniformity is small or big• Whether a new batch of piglets is ready• Prices

Direct advice about pigs to deliver

Slide 30

The decision support model

Technique:• A hierarchical Markov Decision Process (dynamic programming) with a

Dynamic Linear Model (DLM) embedded.Every week, the average weight and the standard deviation is observedAfter each observation the parameters of the DLM are opdated using Kalman

filtering:• Permanent growth capacity of pigs, L• Temporary deviation, e(t)• Within-pen standard deviation, (t)

Decisions based on (state space):• Number of pigs left• Estimated values of the 3 parameters

Decision: Deliver all pigs with live weight bigger than a threshold

Uncertainty of knowledge is directly built into the model through the DLM

Slide 31

On-line weight assessment

Pen with n pigs is monitored.No identification of pigs.At any time t we have:

The precision 1/2 is assumed known

Slide 32

Objectives

Given the on-line weight estimates to assign an optimal delivery policy for the pigs in the pen.

Sequential (weekly) decision problem with decisions at two levels:• Slaughtering of individual pigs (the price is highest in a

rather narrow interval)• Terminating the batch (slaughter all remaining pigs and

insert a new batch of weaners)

Slide 33

Dynamic linear models

Slide 34

A dynamic linear weight model, I

Known average herd specific growth curve:

True weights at time t distributed as:

Slide 35

The scaling factor L

In principle unknown and not directly observable

Initial belief:The belief is updated each time we

observe a set of live weights from the pen.

Let L » N(1, L2) be the true average

weightThen

Slide 36

Observation & system equation 1

Full observation equation for mean:

Auto-correlated sample error (system eq.):

Slide 37

Observation & system equation 2

Far more information available from the observed live weights

Sample variance not normally distributed.Use the 0.16 sample quantile:

The symbol (t) is the standard deviation of the observed values. System equation:

Slide 38

Full equation set

Slide 39

Learning, permanent growth capacity

L = 1,00

0,85

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L= 0,85

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L = 1,07

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L = 1,12

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Sand værdi Lært værdi

Slide 40

Learning: Homogeneity (standard deviation)

Spredning = 3

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Sand værdi Lært værdi

Spredning = 11

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Sand værdi Lært værdi